Comparative safety and efficacy of vasopressors for mortality in septic shock: A network meta-analysis.

PubMed

Nagendran, Myura; Maruthappu, Mahiben; Gordon, Anthony C; Gurusamy, Kurinchi S

2016-05-01

Septic shock is a life-threatening condition requiring vasopressor agents to support the circulatory system. Several agents exist with choice typically guided by the specific clinical scenario. We used a network meta-analysis approach to rate the comparative efficacy and safety of vasopressors for mortality and arrhythmia incidence in septic shock patients. We performed a comprehensive electronic database search including Medline, Embase, Science Citation Index Expanded and the Cochrane database. Randomised trials investigating vasopressor agents in septic shock patients and specifically assessing 28-day mortality or arrhythmia incidence were included. A Bayesian network meta-analysis was performed using Markov chain Monte Carlo methods. Thirteen trials of low to moderate risk of bias in which 3146 patients were randomised were included. There was no pairwise evidence to suggest one agent was superior over another for mortality. In the network meta-analysis, vasopressin was significantly superior to dopamine (OR 0.68 (95% CI 0.5 to 0.94)) for mortality. For arrhythmia incidence, standard pairwise meta-analyses confirmed that dopamine led to a higher incidence of arrhythmias than norepinephrine (OR 2.69 (95% CI 2.08 to 3.47)). In the network meta-analysis, there was no evidence of superiority of one agent over another. In this network meta-analysis, vasopressin was superior to dopamine for 28-day mortality in septic shock. Existing pairwise information supports the use of norepinephrine over dopamine. Our findings suggest that dopamine should be avoided in patients with septic shock and that other vasopressor agents should continue to be based on existing guidelines and clinical judgement of the specific presentation of the patient.

Bayesian network meta-analysis for cluster randomized trials with binary outcomes.

PubMed

Uhlmann, Lorenz; Jensen, Katrin; Kieser, Meinhard

2017-06-01

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

PubMed

Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

Protocol of GLUcose COntrol Safety and Efficacy in type 2 DIabetes, a NETwork meta-analysis: GLUCOSE DINET protocol-Rational and design.

PubMed

Grenet, Guillaume; Lajoinie, Audrey; Ribault, Shams; Nguyen, Gia Bao; Linet, Thomas; Metge, Augustin; Cornu, Catherine; Cucherat, Michel; Moulin, Philippe; Gueyffier, François

2017-06-01

The aim of this study was to propose a ranking of the currently available antidiabetic drugs, regarding vascular clinical outcomes, in patients with type 2 diabetes, through a network meta-analysis approach. Randomized clinical trials, regardless of the blinding design, testing contemporary antidiabetic drugs, and considering clinically relevant outcomes in patients with type 2 diabetes mellitus will be included. The primary outcomes of this analysis will be overall mortality, cardiovascular mortality, and major cardiovascular events. Diabetic microangiopathy will be a secondary outcome. Adverse events, hypoglycemia, weight evolution, bariatric surgery, and discontinuation of the treatment will also be recorded. Each drug will be analyzed according to its therapeutic class: biguanide, alpha-glucosidase inhibitors, sulfonylureas, glitazones, glinides, insulin, DPP-4 inhibitors, GLP-1 analogs, and gliflozins. The treatment effect of each drug class will be compared using pairwise meta-analysis and a Bayesian random model network meta-analysis. Sensitivity analyses will be conducted according to the quality of the studies and the glycemic control. The report will follow the PRISMA checklist for network meta-analysis. Results of the search strategy and of the study selection will be presented in a PRISMA compliant flowchart. The treatment effects will be summarized with odds ratio (OR) estimates and their 95% credible intervals. A ranking of the drugs will be proposed. Our network meta-analysis should allow a clinically relevant ranking of the contemporary antidiabetic drugs. © 2016 Société Française de Pharmacologie et de Thérapeutique.

Two new methods to fit models for network meta-analysis with random inconsistency effects.

PubMed

Law, Martin; Jackson, Dan; Turner, Rebecca; Rhodes, Kirsty; Viechtbauer, Wolfgang

2016-07-28

Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.

Bayesian Meta-Analysis of Coefficient Alpha

ERIC Educational Resources Information Center

Brannick, Michael T.; Zhang, Nanhua

2013-01-01

The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…

A Bayesian approach to meta-analysis of plant pathology studies.

PubMed

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework. Bayesian meta-analysis can readily include information not easily incorporated in classical methods, and allow for a full evaluation of competing models. Given the power and flexibility of Bayesian methods, we expect them to become widely adopted for meta-analysis of plant pathology studies.

Network meta-analysis: a technique to gather evidence from direct and indirect comparisons

PubMed Central

2017-01-01

Systematic reviews and pairwise meta-analyses of randomized controlled trials, at the intersection of clinical medicine, epidemiology and statistics, are positioned at the top of evidence-based practice hierarchy. These are important tools to base drugs approval, clinical protocols and guidelines formulation and for decision-making. However, this traditional technique only partially yield information that clinicians, patients and policy-makers need to make informed decisions, since it usually compares only two interventions at the time. In the market, regardless the clinical condition under evaluation, usually many interventions are available and few of them have been studied in head-to-head studies. This scenario precludes conclusions to be drawn from comparisons of all interventions profile (e.g. efficacy and safety). The recent development and introduction of a new technique – usually referred as network meta-analysis, indirect meta-analysis, multiple or mixed treatment comparisons – has allowed the estimation of metrics for all possible comparisons in the same model, simultaneously gathering direct and indirect evidence. Over the last years this statistical tool has matured as technique with models available for all types of raw data, producing different pooled effect measures, using both Frequentist and Bayesian frameworks, with different software packages. However, the conduction, report and interpretation of network meta-analysis still poses multiple challenges that should be carefully considered, especially because this technique inherits all assumptions from pairwise meta-analysis but with increased complexity. Thus, we aim to provide a basic explanation of network meta-analysis conduction, highlighting its risks and benefits for evidence-based practice, including information on statistical methods evolution, assumptions and steps for performing the analysis. PMID:28503228

Bayesian Meta-Analysis of Cronbach's Coefficient Alpha to Evaluate Informative Hypotheses

ERIC Educational Resources Information Center

Okada, Kensuke

2015-01-01

This paper proposes a new method to evaluate informative hypotheses for meta-analysis of Cronbach's coefficient alpha using a Bayesian approach. The coefficient alpha is one of the most widely used reliability indices. In meta-analyses of reliability, researchers typically form specific informative hypotheses beforehand, such as "alpha of…

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis

PubMed Central

Turner, Rebecca M; Jackson, Dan; Wei, Yinghui; Thompson, Simon G; Higgins, Julian P T

2015-01-01

Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-study heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:25475839

Are beta-blockers effective for preventing post-coronary artery bypass grafting atrial fibrillation? Direct and network meta-analyses.

PubMed

Ji, T; Feng, C; Sun, L; Ye, X; Bai, Y; Chen, Q; Qin, Y; Zhu, J; Zhao, X

2016-05-01

Atrial fibrillation is the most common arrhythmia in clinical practice and is a major contributor to mortality. Recently, several studies have reported different results for treatments aimed at reducing the risk of postoperative AF. The aim of this study was to evaluate the efficacy of beta-blockers (BBs) in preventing post-coronary artery bypass grafting (CABG) AF and to compare the efficacies of different BB treatments using a network meta-analytical approach. The PubMed, EMBASE and Cochrane Library databases were searched (Jan 1995 to May 2014) to identify randomized controlled trials. Two independent investigators separately extracted the data using a seven-point scoring system to assess randomization, allocation concealment, blinding, withdrawals and dropouts. A direct meta-analysis of these randomized controlled trials was conducted. Then, six trials comparing different BB treatments for the prevention of postoperative AF were added to perform a Bayesian network meta-analysis with mixed treatment comparisons. Treatment with BBs was associated with a significant reduction in the postoperative incidence of AF compared with placebo/control [22.37 % compared with 34.45 %, relative risk (RR) = 0.53, 95 % confidence interval (CI): 0.37-0.75, p < 0.00001]. The network meta-analysis revealed no significant differences among eight types of BB treatments but did provide a ranking. BB treatments could significantly reduce the occurrence of post-CABG AF. Insufficient evidence was available to show that one BB treatment was more effective than the others were. According to our network meta-analysis, bisoprolol and landiolol+bisoprolol are better alternatives compared with the other treatments.

Comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults: a network meta-analysis.

PubMed

Thorlund, Kristian; Druyts, Eric; Wu, Ping; Balijepalli, Chakrapani; Keohane, Denis; Mills, Edward

2015-05-01

To establish the comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults using the network meta-analysis approach. Systematic review and network meta-analysis. Individuals aged 60 and older. Data on partial response (defined as at least 50% reduction in depression score from baseline) and safety (dizziness, vertigo, syncope, falls, loss of consciousness) were extracted. A Bayesian network meta-analysis was performed on the efficacy and safety outcomes, and relative risks (RRs) with 95% credible intervals (CrIs) were produced. Fifteen randomized controlled trials were eligible for inclusion in the analysis. Citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, fluoxetine, and sertraline were represented. Reporting on partial response and dizziness was sufficient to conduct a network meta-analysis. Reporting on other outcomes was sparse. For partial response, sertraline (RR=1.28), paroxetine (RR=1.48), and duloxetine (RR=1.62) were significantly better than placebo. The remaining interventions yielded RRs lower than 1.20. For dizziness, duloxetine (RR=3.18) and venlafaxine (RR=2.94) were statistically significantly worse than placebo. Compared with placebo, sertraline had the lowest RR for dizziness (1.14) and fluoxetine the second lowest (1.31). Citalopram, escitalopram, and paroxetine all had RRs between 1.4 and 1.7. There was clear evidence of the effectiveness of sertraline, paroxetine, and duloxetine. There also appears to be a hierarchy of safety associated with the different antidepressants, although there appears to be a dearth of reporting of safety outcomes. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

Bayesian network meta-analysis of root coverage procedures: ranking efficacy and identification of best treatment.

PubMed

Buti, Jacopo; Baccini, Michela; Nieri, Michele; La Marca, Michele; Pini-Prato, Giovan P

2013-04-01

The aim of this work was to conduct a Bayesian network meta-analysis (NM) of randomized controlled trials (RCTs) to establish a ranking in efficacy and the best technique for coronally advanced flap (CAF)-based root coverage procedures. A literature search on PubMed, Cochrane libraries, EMBASE, and hand-searched journals until June 2012 was conducted to identify RCTs on treatments of Miller Class I and II gingival recessions with at least 6 months of follow-up. The treatment outcomes were recession reduction (RecRed), clinical attachment gain (CALgain), keratinized tissue gain (KTgain), and complete root coverage (CRC). Twenty-nine studies met the inclusion criteria, 20 of which were classified as at high risk of bias. The CAF+connective tissue graft (CTG) combination ranked highest in effectiveness for RecRed (Probability of being the best = 40%) and CALgain (Pr = 33%); CAF+enamel matrix derivative (EMD) was slightly better for CRC; CAF+Collagen Matrix (CM) appeared effective for KTgain (Pr = 69%). Network inconsistency was low for all outcomes excluding CALgain. CAF+CTG might be considered the gold standard in root coverage procedures. The low amount of inconsistency gives support to the reliability of the present findings. © 2012 John Wiley & Sons A/S.

Bayesian network meta-analysis comparing five contemporary treatment strategies for newly diagnosed acute promyelocytic leukaemia

PubMed Central

Ren, Yong; Duan, Chongyang; Chen, Shangwu; Xu, Anlong

2016-01-01

Acute promyelocytic leukemia (APL) is a curable subtype of acute myeloid leukemia. The optimum regimen for newly diagnosed APL remains inconclusive. In this Bayesian network meta-analysis, we compared the effectiveness of five regimens-arsenic trioxide (ATO) + all-trans retinoic acid (ATRA), realgar-indigo naturalis formula (RIF) which contains arsenic tetrasulfide + ATRA, ATRA + anthracycline-based chemotherapy (CT), ATO alone and ATRA alone, based on fourteen randomized controlled trials (RCTs), which included 1407 newly diagnosed APL patients. According to the results, the ranking efficacy of the treatment, including early death and complete remission in the induction stage, was the following: 1. ATO/RIF + ATRA; 2. ATRA + CT; 3. ATO, and 4. ATRA. For long-term benefit, ATO/RIF + ATRA significantly improved overall survival (OS) (hazard ratio = 0.35, 95%CI 0.15–0.82, p = 0.02) and event-free survival (EFS) (hazard ratio = 0.32, 95%CI 0.16–0.61, p = 0.001) over ATRA + CT regimen for the low-to-intermediate-risk patients. Thus, ATO + ATRA and RIF + ATRA might be considered the optimum treatments for the newly diagnosed APL and should be recommended as the standard care for frontline therapy. PMID:27322078

Bayesian network meta-analysis comparing five contemporary treatment strategies for newly diagnosed acute promyelocytic leukaemia.

PubMed

Wu, Fenfang; Wu, Di; Ren, Yong; Duan, Chongyang; Chen, Shangwu; Xu, Anlong

2016-07-26

Acute promyelocytic leukemia (APL) is a curable subtype of acute myeloid leukemia. The optimum regimen for newly diagnosed APL remains inconclusive. In this Bayesian network meta-analysis, we compared the effectiveness of five regimens-arsenic trioxide (ATO) + all-trans retinoic acid (ATRA), realgar-indigo naturalis formula (RIF) which contains arsenic tetrasulfide + ATRA, ATRA + anthracycline-based chemotherapy (CT), ATO alone and ATRA alone, based on fourteen randomized controlled trials (RCTs), which included 1407 newly diagnosed APL patients. According to the results, the ranking efficacy of the treatment, including early death and complete remission in the induction stage, was the following: 1. ATO/RIF + ATRA; 2. ATRA + CT; 3. ATO, and 4. ATRA. For long-term benefit, ATO/RIF + ATRA significantly improved overall survival (OS) (hazard ratio = 0.35, 95%CI 0.15-0.82, p = 0.02) and event-free survival (EFS) (hazard ratio = 0.32, 95%CI 0.16-0.61, p = 0.001) over ATRA + CT regimen for the low-to-intermediate-risk patients. Thus, ATO + ATRA and RIF + ATRA might be considered the optimum treatments for the newly diagnosed APL and should be recommended as the standard care for frontline therapy.

A selection model for accounting for publication bias in a full network meta-analysis.

PubMed

Mavridis, Dimitris; Welton, Nicky J; Sutton, Alex; Salanti, Georgia

2014-12-30

Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency. Copyright © 2014 John Wiley & Sons, Ltd.

Network meta-analysis of lorcaserin and oral hypoglycaemics for patients with type 2 diabetes mellitus and obesity.

PubMed

Neff, L M; Broder, M S; Beenhouwer, D; Chang, E; Papoyan, E; Wang, Z W

2017-12-01

In addition to weight loss, randomized controlled trials have shown improvement in glycaemic control in patients taking lorcaserin. The aim of this study aim was to compare adding lorcaserin or other glucose lowering medications to metformin on weight and glycaemic control. A systematic review and network meta-analysis of randomized controlled trials were conducted. Included studies (published 1990-2014) were of lorcaserin or glucose lowering medications in type 2 diabetic patients compared to placebo or different active treatments. Studies had to report ≥1 key outcome (change in weight or HbA1c, % HbA1c <7, hypoglycaemia). Direct meta-analysis was performed using DerSimonian and Laird random effects models, and network meta-analysis with Bayesian Markov-chain Monte Carlo random effects models; 6552 articles were screened and 41 included. Lorcaserin reduced weight significantly more than thiazolidinediones, glinides, sulphonylureas and dipeptidyl peptidase-4 inhibitors, some of which may have led to weight gain. There were no significant differences in weight change between lorcaserin and alpha-glucoside inhibitors, glucagon-like peptide-1 agonists and sodium/glucose cotransporter 2 inhibitors. Network meta-analysis showed lorcaserin was non-inferior to all other agents on HbA1c reduction and % achieving HbA1c of <7%. The risk of hypoglycaemia was not significantly different among studied agents except that sulphonylureas were associated with higher risk of hypoglycaemia than lorcaserin. Although additional studies are needed, this analysis suggests in a population of patients with a body mas index of ≥27 who do not achieve glycaemic control on a single agent, lorcaserin may be added as an alternative to an add-on glucose lowering medication. © 2017 World Obesity Federation.

A Bayesian network meta-analysis for binary outcome: how to do it.

PubMed

Greco, Teresa; Landoni, Giovanni; Biondi-Zoccai, Giuseppe; D'Ascenzo, Fabrizio; Zangrillo, Alberto

2016-10-01

This study presents an overview of conceptual and practical issues of a network meta-analysis (NMA), particularly focusing on its application to randomised controlled trials with a binary outcome of interest. We start from general considerations on NMA to specifically appraise how to collect study data, structure the analytical network and specify the requirements for different models and parameter interpretations, with the ultimate goal of providing physicians and clinician-investigators a practical tool to understand pros and cons of NMA. Specifically, we outline the key steps, from the literature search to sensitivity analysis, necessary to perform a valid NMA of binomial data, exploiting Markov Chain Monte Carlo approaches. We also apply this analytical approach to a case study on the beneficial effects of volatile agents compared to total intravenous anaesthetics for surgery to further clarify the statistical details of the models, diagnostics and computations. Finally, datasets and models for the freeware WinBUGS package are presented for the anaesthetic agent example. © The Author(s) 2013.

Software and package applicating for network meta-analysis: A usage-based comparative study.

PubMed

Xu, Chang; Niu, Yuming; Wu, Junyi; Gu, Huiyun; Zhang, Chao

2017-12-21

To compare and analyze the characteristics and functions of software applications for network meta-analysis (NMA). PubMed, EMbase, The Cochrane Library, the official websites of Bayesian inference Using Gibbs Sampling (BUGS), Stata and R, and Google were searched to collect the software and packages for performing NMA; software and packages published up to March 2016 were included. After collecting the software, packages, and their user guides, we used the software and packages to calculate a typical example. All characteristics, functions, and computed results were compared and analyzed. Ten types of software were included, including programming and non-programming software. They were developed mainly based on Bayesian or frequentist theory. Most types of software have the characteristics of easy operation, easy mastery, exact calculation, or excellent graphing. However, there was no single software that performed accurate calculations with superior graphing; this could only be achieved through the combination of two or more types of software. This study suggests that the user should choose the appropriate software according to personal programming basis, operational habits, and financial ability. Then, the choice of the combination of BUGS and R (or Stata) software to perform the NMA is considered. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Skin antiseptics in venous puncture site disinfection for preventing blood culture contamination: A Bayesian network meta-analysis of randomized controlled trials.

PubMed

Liu, Wenjie; Duan, Yuchen; Cui, Wenyao; Li, Li; Wang, Xia; Dai, Heling; You, Chao; Chen, Maojun

2016-07-01

To compare the efficacy of several antiseptics in decreasing the blood culture contamination rate. Network meta-analysis. Electronic searches of PubMed and Embase were conducted up to November 2015. Only randomized controlled trials or quasi-randomized controlled trials were eligible. We applied no language restriction. A comprehensive review of articles in the reference lists was also accomplished for possible relevant studies. Relevant studies evaluating efficacy of different antiseptics in venous puncture site for decreasing the blood culture contamination rate were included. The data were extracted from the included randomized controlled trials by two authors independently. The risk of bias was evaluated using Detsky scale by two authors independently. We used WinBUGS1.43 software and statistic model described by Chaimani to perform this network meta-analysis. Then graphs of statistical results of WinBUGS1.43 software were generated using 'networkplot', 'ifplot', 'netfunnel' and 'sucra' procedure by STATA13.0. Odds ratio and 95% confidence intervals were assessed for dichotomous data. A probability of p less than 0.05 was considered to be statistically significant. Compared with ordinary meta-analyses, this network meta-analysis offered hierarchies for the efficacy of different antiseptics in decreasing the blood culture contamination rate. Seven randomized controlled trials involving 34,408 blood samples were eligible for the meta-analysis. No significant difference was found in blood culture contamination rate among different antiseptics. No significant difference was found between non-alcoholic antiseptics and alcoholic antiseptics, alcoholic chlorhexidine and povidone iodine, chlorhexidine and iodine compounds, povidone iodine and iodine tincture in this aspect, respectively. Different antiseptics may not affect the blood culture contamination rate. Different intervals between the skin disinfection and the venous puncture, the different settings (emergency room, medical wards, and intensive care units) and the performance of the phlebotomy may affect the blood culture contamination rate. Copyright © 2016 Elsevier Ltd. All rights reserved.

Propagation of population pharmacokinetic information using a Bayesian approach: comparison with meta-analysis.

PubMed

Dokoumetzidis, Aristides; Aarons, Leon

2005-08-01

We investigated the propagation of population pharmacokinetic information across clinical studies by applying Bayesian techniques. The aim was to summarize the population pharmacokinetic estimates of a study in appropriate statistical distributions in order to use them as Bayesian priors in consequent population pharmacokinetic analyses. Various data sets of simulated and real clinical data were fitted with WinBUGS, with and without informative priors. The posterior estimates of fittings with non-informative priors were used to build parametric informative priors and the whole procedure was carried on in a consecutive manner. The posterior distributions of the fittings with informative priors where compared to those of the meta-analysis fittings of the respective combinations of data sets. Good agreement was found, for the simulated and experimental datasets when the populations were exchangeable, with the posterior distribution from the fittings with the prior to be nearly identical to the ones estimated with meta-analysis. However, when populations were not exchangeble an alternative parametric form for the prior, the natural conjugate prior, had to be used in order to have consistent results. In conclusion, the results of a population pharmacokinetic analysis may be summarized in Bayesian prior distributions that can be used consecutively with other analyses. The procedure is an alternative to meta-analysis and gives comparable results. It has the advantage that it is faster than the meta-analysis, due to the large datasets used with the latter and can be performed when the data included in the prior are not actually available.

Comparison of pharmacological and non-pharmacological interventions to prevent delirium in critically ill patients: a protocol for a systematic review incorporating network meta-analyses.

PubMed

Burry, L D; Hutton, B; Guenette, M; Williamson, D; Mehta, S; Egerod, I; Kanji, S; Adhikari, N K; Moher, D; Martin, C M; Rose, L

2016-09-08

Delirium is characterized by acute changes in mental status including inattention, disorganized thinking, and altered level of consciousness, and is highly prevalent in critically ill adults. Delirium has adverse consequences for both patients and the healthcare system; however, at this time, no effective treatment exists. The identification of effective prevention strategies is therefore a clinical and research imperative. An important limitation of previous reviews of delirium prevention is that interventions were considered in isolation and only direct evidence was used. Our systematic review will synthesize all existing data using network meta-analysis, a powerful statistical approach that enables synthesis of both direct and indirect evidence. We will search Ovid MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science from 1980 to March 2016. We will search the PROSPERO registry for protocols and the Cochrane Library for published systematic reviews. We will examine reference lists of pertinent reviews and search grey literature and the International Clinical Trials Registry Platform for unpublished studies and ongoing trials. We will include randomized and quasi-randomized trials of critically ill adults evaluating any pharmacological, non-pharmacological, or multi-component intervention for delirium prevention, administered in or prior to (i.e., peri-operatively) transfer to the ICU. Two authors will independently screen search results and extract data from eligible studies. Risk of bias assessments will be completed on all included studies. To inform our network meta-analysis, we will first conduct conventional pair-wise meta-analyses for primary and secondary outcomes using random-effects models. We will generate our network meta-analysis using a Bayesian framework, assuming a common heterogeneity parameter across all comparisons, and accounting for correlations in multi-arm studies. We will perform analyses using WinBUGS software. This systematic review will address the existing knowledge gap regarding best practices for delirium prevention in critically ill adults by synthesizing evidence from trials of pharmacological, non-pharmacological, and multi-component interventions administered in or prior to transfer to the ICU. Use of network meta-analysis will clarify which delirium prevention strategies are most effective in improving clinical outcomes while causing least harm. The network meta-analysis is a novel approach and will provide knowledge users and decision makers with comparisons of multiple interventions of delirium prevention strategies. PROSPERO CRD42016036313.

Pathfinding to an optimal strategy of revascularization in primary coronary intervention in patients with multivessel disease: a network meta-analysis of randomized trials.

PubMed

Komócsi, András; Kehl, Dániel; d'Ascenso, Fabrizio; DiNicolantonio, James; Vorobcsuk, András

2017-03-01

In ST-segment elevation myocardial infarction (STEMI), current guidelines discourage treatment of the non-culprit lesions at the time of the primary intervention. Latest trials have challenged this strategy suggesting benefit of early complete revascularization. We performed a Bayesian multiple treatment network meta-analysis of randomized clinical trials (RCTs) in STEMI on culprit-only intervention (CO) versus different timing multivessel revascularization, including immediate (IM), same hospitalization (SH) or later staged (ST). Outcome parameters were pooled with a random-effects model. For multiple-treatment meta-analysis, a Bayesian Markov chain Monte Carlo method was used. Eight RCTs involving 2077 patients were identified. ST and IM revascularization was associated with a decrease in major adverse cardiac events (MACEs) compared to culprit-only approach (risk ratio [RR]: 0.43 credible interval [CrI]: 0.22-0.77 and RR: 0.36 CrI: 0.24-0.54, respectively). IM was superior to SH (RR: 0.49 CrI: 0.29-0.80). With regards to myocardial infarction IM was superior to SH (RR: 0.18 CrI: 0.02-0.99). The posterior probability of being the best choice of treatment regarding the frequency of MACEs was 71.2% for IM, 28.5% for ST, 0.3% for SH and 0.05% for culprit-only approach. Results from RCTs indicate that immediate or staged revascularization of non-culprit lesions reduces major adverse events in patients after primary percutaneous coronary intervention. Differences in MACEs suggest superiority of the immediate or staged intervention; however, further randomized trials are needed to determine the optimal timing of revascularization of the non-culprit lesions.

Comparative efficacy and safety of tofacitinib, with or without methotrexate, in patients with active rheumatoid arthritis: a Bayesian network meta-analysis of randomized controlled trials.

PubMed

Lee, Young Ho; Bae, Sang-Cheol; Song, Gwan Gyu

2015-12-01

This study aimed to assess the relative efficacy and safety of tofacitinib 5 and 10 mg twice daily, or in combination with methotrexate (MTX), in patients with active RA. Randomized controlled trials (RCTs) examining the efficacy and safety of tofacitinib in patients with active RA were included in this network meta-analysis. We performed a Bayesian network meta-analysis to combine the direct and indirect evidence from the RCTs. Ten RCTs including 4867 patients met the inclusion criteria. There were 21 pairwise comparisons including 11 direct comparisons of seven interventions. The ACR20 response rate was significantly higher in the tofacitinib 10 mg + MTX group than in the placebo and MTX groups (OR 7.56, 95 % credible interval (CrI) 3.07-21.16; OR 3.67, 95 % CrI 2.60-5.71, respectively). Ranking probabilities based on the surface under the cumulative ranking curve (SUCRA) indicated that tofacitinib 10 mg + MTX had the highest probability of being the best treatment for achieving the ACR20 response rate (SUCRA = 0.9254), followed by tofacitinib 5 mg + MTX (SUCRA = 0.7156), adalimumab 40 mg + MTX (SUCRA = 0.6097), tofacitinib 10 mg (SUCRA = 0.5984), tofacitinib 5 mg (SUCRA = 0.4749), MTX (SUCRA = 0.1674), and placebo (SUCRA = 0.0086). In contrast, the safety based on the number of withdrawals due to adverse events did not differ significantly among the seven interventions. Tofacitinib, at dosages 5 and 10 mg twice daily, in combination with MTX, was the most efficacious intervention for active RA and was not associated with a significant risk for withdrawals due to adverse events.

Node-Splitting Generalized Linear Mixed Models for Evaluation of Inconsistency in Network Meta-Analysis.

PubMed

Yu-Kang, Tu

2016-12-01

Network meta-analysis for multiple treatment comparisons has been a major development in evidence synthesis methodology. The validity of a network meta-analysis, however, can be threatened by inconsistency in evidence within the network. One particular issue of inconsistency is how to directly evaluate the inconsistency between direct and indirect evidence with regard to the effects difference between two treatments. A Bayesian node-splitting model was first proposed and a similar frequentist side-splitting model has been put forward recently. Yet, assigning the inconsistency parameter to one or the other of the two treatments or splitting the parameter symmetrically between the two treatments can yield different results when multi-arm trials are involved in the evaluation. We aimed to show that a side-splitting model can be viewed as a special case of design-by-treatment interaction model, and different parameterizations correspond to different design-by-treatment interactions. We demonstrated how to evaluate the side-splitting model using the arm-based generalized linear mixed model, and an example data set was used to compare results from the arm-based models with those from the contrast-based models. The three parameterizations of side-splitting make slightly different assumptions: the symmetrical method assumes that both treatments in a treatment contrast contribute to inconsistency between direct and indirect evidence, whereas the other two parameterizations assume that only one of the two treatments contributes to this inconsistency. With this understanding in mind, meta-analysts can then make a choice about how to implement the side-splitting method for their analysis. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Comparative Effectiveness of Pharmacological Interventions for Severe Alcoholic Hepatitis: A Systematic Review and Network Meta-analysis.

PubMed

Singh, Siddharth; Murad, Mohammad Hassan; Chandar, Apoorva K; Bongiorno, Connie M; Singal, Ashwani K; Atkinson, Stephen R; Thursz, Mark R; Loomba, Rohit; Shah, Vijay H

2015-10-01

Severe alcoholic hepatitis (AH) has high mortality. We assessed the comparative effectiveness of pharmacological interventions for severe AH, through a network meta-analysis combining direct and indirect treatment comparisons. We conducted a systematic literature review, through February 2015, for randomized controlled trials of adults with severe AH (discriminant function ≥32 and/or hepatic encephalopathy) that compared the efficacy of active pharmacologic interventions (corticosteroids, pentoxifylline, and N-acetylcysteine [NAC], alone or in combination) with each other or placebo, in reducing short-term mortality (primary outcome) and medium-term mortality, acute kidney injury, and/or infections (secondary outcomes). We performed direct and Bayesian network meta-analysis for all treatments, and used Grading of Recommendations Assessment, Development and Evaluation criteria to appraise quality of evidence. We included 22 randomized controlled trials (2621 patients) comparing 5 different interventions. In a direct meta-analysis, only corticosteroids decreased risk of short-term mortality. In a network meta-analysis, moderate quality evidence supported the use of corticosteroids alone (relative risk [RR], 0.54; 95% credible interval [CrI], 0.39-0.73) or in combination with pentoxifylline (RR, 0.53; 95% CrI, 0.36-0.78) or NAC (RR, 0.15; 95% CI, 0.05-0.39), to reduce short-term mortality; low quality evidence showed that pentoxifylline also decreased short-term mortality (RR, 0.70; 95% CrI, 0.50-0.97). The addition of NAC, but not pentoxifylline, to corticosteroids may be superior to corticosteroids alone for reducing short-term mortality. No treatment was effective in reducing medium-term mortality. Imprecise estimates and the small number of direct trials lowered the confidence in several comparisons. In patients with severe AH, pentoxifylline and corticosteroids (alone and in combination with pentoxifylline or NAC) can reduce short-term mortality. No treatment decreases risk of medium-term mortality. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Systematic review with network meta-analysis: the efficacy of anti-tumour necrosis factor-alpha agents for the treatment of ulcerative colitis.

PubMed

Stidham, R W; Lee, T C H; Higgins, P D R; Deshpande, A R; Sussman, D A; Singal, A G; Elmunzer, B J; Saini, S D; Vijan, S; Waljee, A K

2014-04-01

Antibodies against tumour necrosis factor-alpha (anti-TNF) are effective therapies in the treatment of ulcerative colitis (UC), but their comparative efficacy is unknown. To perform a network meta-analysis comparing the efficacy of anti-TNF agents in UC. After screening 506 studies, reviewers extracted information on seven studies. Traditional meta-analysis (TMA) was used to compare each anti-TNF agent to placebo. Bayesian network meta-analysis (NMA) was performed to compare the effects of anti-TNF agents to placebo. In addition, sample sizes for comparative efficacy trials were calculated. Compared to placebo, TMA revealed that anti-TNF agents result in a higher likelihood of induction of remission and response (RR: 2.45, 95% CI: 1.72-3.47 and RR: 1.65, 95% CI: 1.37-1.99 respectively) as well as maintenance of remission and response (RR: 2.00, 95% CI: 1.52-2.62 and RR: 1.76, 95% CI: 1.46-2.14 respectively). Individually, infliximab, adalimumab and goliumumab resulted in a higher likelihood of induction and maintenance for both remission and response. NMA found nonsignificant trends in comparisons of the individual agents. The required sample sizes for direct head-to-head trials between infliximab and adalimumab for induction and maintenance are 174 and 204 subjects respectively. This study demonstrates that, compared to placebo, infliximab, adalimumab and golimumab are all effective for the induction and maintenance of remission in ulcerative colitis. However, network meta-analysis demonstrates that no single agent is clinically superior to the others and therefore, other factors such as cost, safety, route of administration and patient preference should dictate our choice of anti-TNF agents. A randomised comparative efficacy trial between infliximab and adalimumab in UC is of practical size and should be performed. © 2014 John Wiley & Sons Ltd.

Epidemiology Characteristics, Methodological Assessment and Reporting of Statistical Analysis of Network Meta-Analyses in the Field of Cancer

PubMed Central

Ge, Long; Tian, Jin-hui; Li, Xiu-xia; Song, Fujian; Li, Lun; Zhang, Jun; Li, Ge; Pei, Gai-qin; Qiu, Xia; Yang, Ke-hu

2016-01-01

Because of the methodological complexity of network meta-analyses (NMAs), NMAs may be more vulnerable to methodological risks than conventional pair-wise meta-analysis. Our study aims to investigate epidemiology characteristics, conduction of literature search, methodological quality and reporting of statistical analysis process in the field of cancer based on PRISMA extension statement and modified AMSTAR checklist. We identified and included 102 NMAs in the field of cancer. 61 NMAs were conducted using a Bayesian framework. Of them, more than half of NMAs did not report assessment of convergence (60.66%). Inconsistency was assessed in 27.87% of NMAs. Assessment of heterogeneity in traditional meta-analyses was more common (42.62%) than in NMAs (6.56%). Most of NMAs did not report assessment of similarity (86.89%) and did not used GRADE tool to assess quality of evidence (95.08%). 43 NMAs were adjusted indirect comparisons, the methods used were described in 53.49% NMAs. Only 4.65% NMAs described the details of handling of multi group trials and 6.98% described the methods of similarity assessment. The median total AMSTAR-score was 8.00 (IQR: 6.00–8.25). Methodological quality and reporting of statistical analysis did not substantially differ by selected general characteristics. Overall, the quality of NMAs in the field of cancer was generally acceptable. PMID:27848997

A Bayesian Meta-Analysis on Prevalence of Hepatitis B Virus Infection among Chinese Volunteer Blood Donors

PubMed Central

Liu, Guang-ying; Zheng, Yang; Deng, Yan; Gao, Yan-yan; Wang, Lie

2013-01-01

Background Although transfusion-transmitted infection of hepatitis B virus (HBV) threatens the blood safety of China, the nationwide circumstance of HBV infection among blood donors is still unclear. Objectives To comprehensively estimate the prevalence of HBsAg positive and HBV occult infection (OBI) among Chinese volunteer blood donors through bayesian meta-analysis. Methods We performed an electronic search in Pub-Med, Web of Knowledge, Medline, Wanfang Data and CNKI, complemented by a hand search of relevant reference lists. Two authors independently extracted data from the eligible studies. Then two bayesian random-effect meta-analyses were performed, followed by bayesian meta-regressions. Results 5957412 and 571227 donors were identified in HBsAg group and OBI group, respectively. The pooled prevalence of HBsAg group and OBI group among donors is 1.085% (95% credible interval [CI] 0.859%∼1.398%) and 0.094% (95% CI 0.0578%∼0.1655%). For HBsAg group, subgroup analysis shows the more developed area has a lower prevalence than the less developed area; meta-regression indicates there is a significant decreasing trend in HBsAg positive prevalence with sampling year (beta = −0.1202, 95% −0.2081∼−0.0312). Conclusion Blood safety against HBV infection in China is suffering serious threats and the government should take effective measures to improve this situation. PMID:24236110

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

PubMed Central

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

Prokinetics for the treatment of functional dyspepsia: Bayesian network meta-analysis.

PubMed

Yang, Young Joo; Bang, Chang Seok; Baik, Gwang Ho; Park, Tae Young; Shin, Suk Pyo; Suk, Ki Tae; Kim, Dong Joon

2017-06-26

Controversies persist regarding the effect of prokinetics for the treatment of functional dyspepsia (FD). This study aimed to assess the comparative efficacy of prokinetic agents for the treatment of FD. Randomized controlled trials (RCTs) of prokinetics for the treatment of FD were identified from core databases. Symptom response rates were extracted and analyzed using odds ratios (ORs). A Bayesian network meta-analysis was performed using the Markov chain Monte Carlo method in WinBUGS and NetMetaXL. In total, 25 RCTs, which included 4473 patients with FD who were treated with 6 different prokinetics or placebo, were identified and analyzed. Metoclopramide showed the best surface under the cumulative ranking curve (SUCRA) probability (92.5%), followed by trimebutine (74.5%) and mosapride (63.3%). However, the therapeutic efficacy of metoclopramide was not significantly different from that of trimebutine (OR:1.32, 95% credible interval: 0.27-6.06), mosapride (OR: 1.99, 95% credible interval: 0.87-4.72), or domperidone (OR: 2.04, 95% credible interval: 0.92-4.60). Metoclopramide showed better efficacy than itopride (OR: 2.79, 95% credible interval: 1.29-6.21) and acotiamide (OR: 3.07, 95% credible interval: 1.43-6.75). Domperidone (SUCRA probability 62.9%) showed better efficacy than itopride (OR: 1.37, 95% credible interval: 1.07-1.77) and acotiamide (OR: 1.51, 95% credible interval: 1.04-2.18). Metoclopramide, trimebutine, mosapride, and domperidone showed better efficacy for the treatment of FD than itopride or acotiamide. Considering the adverse events related to metoclopramide or domperidone, the short-term use of these agents or the alternative use of trimebutine or mosapride could be recommended for the symptomatic relief of FD.

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

Therapies for bruxism: a systematic review and network meta-analysis (protocol).

PubMed

Mesko, Mauro Elias; Hutton, Brian; Skupien, Jovito Adiel; Sarkis-Onofre, Rafael; Moher, David; Pereira-Cenci, Tatiana

2017-01-13

Bruxism is a sleep disorder characterized by grinding and clenching of the teeth that may be related to irreversible tooth injuries. It is a prevalent condition occurring in up to 31% of adults. However, there is no definitive answer as to which of the many currently available treatments (including drug therapy, intramuscular injections, physiotherapy, biofeedback, kinesiotherapy, use of intraoral devices, or psychological therapy) is the best for the clinical management of the different manifestations of bruxism. The aim of this systematic review and network meta-analysis is to answer the following question: what is the best treatment for adult bruxists? Comprehensive searches of the Cochrane Library, MEDLINE (via PubMed), Scopus, and LILACS will be completed using the following keywords: bruxism and therapies and related entry terms. Studies will be included, according to the eligibility criteria (Controlled Clinical Trials and Randomized Clinical Trials, considering specific outcome measures for bruxism). The reference lists of included studies will be hand searched. Relevant data will be extracted from included studies using a specially designed data extraction sheet. Risk of bias of the included studies will be assessed, and the overall strength of the evidence will be summarized (i.e., GRADE). A random effects model will be used for all pairwise meta-analyses (with a 95% confidence interval). A Bayesian network meta-analysis will explore the relative benefits between the various treatments. The review will be reported using the Preferred Reporting Items for Systematic Reviews incorporating Network Meta-Analyses (PRISMA-NMA) statement. This systematic review aims at identifying and evaluating therapies to treat bruxism. This systematic review may lead to several recommendations, for both patients and researchers, as which is the best therapy for a specific patient case and how future studies need to be designed, considering what is available now and what is the reality of the patient. PROSPERO CRD42015023308.

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Current treatment of ocular toxoplasmosis in immunocompetent patients: A network meta-analysis.

PubMed

Zhang, Yanxia; Lin, Xiao; Lu, Fangli

2018-04-25

Ocular toxoplasmosis (OT) is the most frequent form of infectious posterior uveitis caused by the protozoan parasite Toxoplasma gondii. To evaluate the available evidence in peer-reviewed publications about the most effective therapy for OT in immunocompetent patients, herein a systematic literature search was conducted using Embase, PubMed, Google Scholar, and the Cochrane Central Register of Controlled Trials (CENTRAL) database from January 1987 to October 2017, with search terms "OT", "retinochoroiditis", "treatment", and "immunocompetent"; search filters "controlled clinical trial", "randomized clinical trial", and "clinical trial". The included studies were performed to evaluate the various treatment modalities of OT. Different treatment regimens were compared with regard to the improvement of visual acuity, the resolution of vitreous inflammation, recurrence, and side-effects. We independently extracted data and assessed eligibility and risk of bias using the preferred reporting items for systematic reviews and meta-analysis, and resolved any disagreement through discussion. A Bayesian network meta-analysis model was used to evaluate the interesting outcomes of all the interventions. Total 10 trials of treatments for OT were found to meet the inclusion criteria. Six trials of treatments including clindamycin, azithromycin, and trimethoprim-sulfamethoxazole (TMP-SMX) were compared with conventional therapy (the combination of pyrimethamine, sulfadiazine, and prednisone) for evaluation of the effect on visual acuity, vitreous inflammation, recurrence of OT, and side-effects. Two trials were compared TMP-SMX with placebo. One trial was compared azithromycin with TMP-SMX. And another trial was compared among treatments with clindamycin, P-S, TMP-SMX, and placebo. Based on our network meta-analysis, therapy with TMP-SMX seems to be an alternative treatment of OT in immunocompetent patients. Copyright © 2018 Elsevier B.V. All rights reserved.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Bayesian meta-analysis of Cronbach's coefficient alpha to evaluate informative hypotheses.

PubMed

Okada, Kensuke

2015-12-01

This paper proposes a new method to evaluate informative hypotheses for meta-analysis of Cronbach's coefficient alpha using a Bayesian approach. The coefficient alpha is one of the most widely used reliability indices. In meta-analyses of reliability, researchers typically form specific informative hypotheses beforehand, such as 'alpha of this test is greater than 0.8' or 'alpha of one form of a test is greater than the others.' The proposed method enables direct evaluation of these informative hypotheses. To this end, a Bayes factor is calculated to evaluate the informative hypothesis against its complement. It allows researchers to summarize the evidence provided by previous studies in favor of their informative hypothesis. The proposed approach can be seen as a natural extension of the Bayesian meta-analysis of coefficient alpha recently proposed in this journal (Brannick and Zhang, 2013). The proposed method is illustrated through two meta-analyses of real data that evaluate different kinds of informative hypotheses on superpopulation: one is that alpha of a particular test is above the criterion value, and the other is that alphas among different test versions have ordered relationships. Informative hypotheses are supported from the data in both cases, suggesting that the proposed approach is promising for application. Copyright © 2015 John Wiley & Sons, Ltd.

Preventive Strategies for Contrast-Induced Acute Kidney Injury in Patients Undergoing Percutaneous Coronary Procedures: Evidence From a Hierarchical Bayesian Network Meta-Analysis of 124 Trials and 28 240 Patients.

PubMed

Giacoppo, Daniele; Gargiulo, Giuseppe; Buccheri, Sergio; Aruta, Patrizia; Byrne, Robert A; Cassese, Salvatore; Dangas, George; Kastrati, Adnan; Mehran, Roxana; Tamburino, Corrado; Capodanno, Davide

2017-05-01

The effectiveness of currently available effective preventive strategies for contrast-induced acute kidney injury (CIAKI) is a matter of debate. We performed a Bayesian random-effects network meta-analysis of 124 trials (28 240 patients) comparing a total of 10 strategies: saline, statin, N-acetylcysteine (NAC), sodium bicarbonate (NaHCO 3 ), NAC+NaHCO 3 , ascorbic acid, xanthine, dopaminergic agent, peripheral ischemic preconditioning, and natriuretic peptide. Compared with saline, the risk of CIAKI was reduced by using statin (odds ratio [OR], 0.42; 95% credible interval [CrI], 0.26-0.67), xanthine (OR, 0.32; 95% CrI, 0.17-0.57), ischemic preconditioning (OR, 0.48; 95% CrI, 0.26-0.87), NAC+NaHCO 3 (OR, 0.50; 95% CrI, 0.33-0.76), NAC (OR, 0.68; 95% CrI, 0.55-0.84), and NaHCO 3 (OR, 0.66; 95% CrI, 0.47-0.90). The benefit of statin therapy was consistent across multiple sensitivity analyses, whereas the efficacy of all the other strategies was questioned by restricting the analysis to high-quality trials. Overall, high heterogeneity was observed for comparisons involving xanthine and ischemic preconditioning, although the impact of NAC and xanthine was probably influenced by publication bias/small-study effect. Hydration alone was the least effective preventive strategy for CIAKI. Meta-regressions did not reveal significant associations with baseline creatinine and contrast volume. In patients with diabetes mellitus, no strategy was found to reduce the incidence of CIAKI. In patients undergoing percutaneous coronary procedures, statin administration is associated with a marked and consistent reduction in the risk of CIAKI compared with saline. Although xanthine, NAC, NaHCO 3 , NAC+NaHCO 3 , ischemic preconditioning, and natriuretic peptide may have nephroprotective effects, these results were not consistent across multiple sensitivity analyses. © 2017 American Heart Association, Inc.

Neoadjuvant treatments for locally advanced, resectable esophageal cancer: A network meta-analysis.

PubMed

Chan, Kelvin K W; Saluja, Ronak; Delos Santos, Keemo; Lien, Kelly; Shah, Keya; Cramarossa, Gemma; Zhu, Xiaofu; Wong, Rebecca K S

2018-02-14

The relative survival benefits and postoperative mortality among the different types of neoadjuvant treatments (such as chemotherapy only, radiotherapy only or chemoradiotherapy) for esophageal cancer patients are not well established. To evaluate the relative efficacy and safety of neoadjuvant therapies in resectable esophageal cancer, a Bayesian network meta-analysis was performed. MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials were searched for publications up to May 2016. ASCO and ASTRO annual meeting abstracts were also searched up to the 2015 conferences. Randomized controlled trials that compared at least two of the following treatments for resectable esophageal cancer were included: surgery alone, surgery preceded by neoadjuvant chemotherapy, neoadjuvant radiotherapy or neoadjuvant chemoradiotherapy. The primary outcome assessed from the trials was overall survival. Thirty-one randomized controlled trials involving 5496 patients were included in the quantitative analysis. The network meta-analysis showed that neoadjuvant chemoradiotherapy improved overall survival when compared to all other treatments including surgery alone (HR 0.75, 95% CR 0.67-0.85), neoadjuvant chemotherapy (HR 0.83. 95% CR 0.70-0.96) and neoadjuvant radiotherapy (HR 0.82, 95% CR 0.67-0.99). However, the risk of postoperative mortality increased when comparing neoadjuvant chemoradiotherapy to either surgery alone (RR 1.46, 95% CR 1.00-2.14) or to neoadjuvant chemotherapy (RR 1.58, 95% CR 1.00-2.49). In conclusion, neoadjuvant chemoradiotherapy improves overall survival but may also increase the risk of postoperative mortality in patients locally advanced resectable esophageal carcinoma. © 2018 UICC.

Albendazole and Corticosteroids for the Treatment of Solitary Cysticercus Granuloma: A Network Meta-analysis.

PubMed

Zhao, Bing-Cheng; Jiang, Hong-Ye; Ma, Wei-Ying; Jin, Da-Di; Li, Hao-Miao; Lu, Hai; Nakajima, Hideaki; Huang, Tong-Yi; Sun, Kai-Yu; Chen, Shu-Ling; Chen, Ke-Bing

2016-02-01

Solitary cysticercus granuloma (SCG) is the commonest form of neurocysticercosis in the Indian subcontinent and in travelers. Several different treatment options exist for SCG. We conducted a Bayesian network meta-analysis of randomized clinical trials (RCTs) to identify the best treatment option to prevent seizure recurrence and promote lesion resolution for patients with SCG. PubMed, EMBASE and the Cochrane Library databases (up to June 1, 2015) were searched for RCTs that compared any anthelmintics or corticosteroids, alone or in combination, with placebo or head to head and reported on seizure recurrence and lesion resolution in patients with SCG. A total of 14 RCTs (1277 patients) were included in the quantitative analysis focusing on four different treatment options. A Bayesian network model computing odds ratios (OR) with 95% credible intervals (CrI) and probability of being best (Pbest) was used to compare all interventions simultaneously. Albendazole and corticosteroids combination therapy was the only regimen that significantly decreased the risk of seizure recurrence compared with conservative treatment (OR 0.32, 95% CrI 0.10-0.93, Pbest 73.3%). Albendazole and corticosteroids alone or in combination were all efficacious in hastening granuloma resolution, but the combined therapy remained the best option based on probability analysis (OR 3.05, 95% CrI 1.24-7.95, Pbest 53.9%). The superiority of the combination therapy changed little in RCTs with different follow-up durations and in sensitivity analyses. The limitations of this study include high risk of bias and short follow-up duration in most studies. Dual therapy of albendazole and corticosteroids was the most efficacious regimen that could prevent seizure recurrence and promote lesion resolution in a follow-up period of around one year. It should be recommended for the management of SCG until more high-quality evidence is available.

Albendazole and Corticosteroids for the Treatment of Solitary Cysticercus Granuloma: A Network Meta-analysis

PubMed Central

Nakajima, Hideaki; Huang, Tong-Yi; Sun, Kai-Yu; Chen, Shu-Ling; Chen, Ke-Bing

2016-01-01

Background Solitary cysticercus granuloma (SCG) is the commonest form of neurocysticercosis in the Indian subcontinent and in travelers. Several different treatment options exist for SCG. We conducted a Bayesian network meta-analysis of randomized clinical trials (RCTs) to identify the best treatment option to prevent seizure recurrence and promote lesion resolution for patients with SCG. Methods and Principal Findings PubMed, EMBASE and the Cochrane Library databases (up to June 1, 2015) were searched for RCTs that compared any anthelmintics or corticosteroids, alone or in combination, with placebo or head to head and reported on seizure recurrence and lesion resolution in patients with SCG. A total of 14 RCTs (1277 patients) were included in the quantitative analysis focusing on four different treatment options. A Bayesian network model computing odds ratios (OR) with 95% credible intervals (CrI) and probability of being best (Pbest) was used to compare all interventions simultaneously. Albendazole and corticosteroids combination therapy was the only regimen that significantly decreased the risk of seizure recurrence compared with conservative treatment (OR 0.32, 95% CrI 0.10–0.93, Pbest 73.3%). Albendazole and corticosteroids alone or in combination were all efficacious in hastening granuloma resolution, but the combined therapy remained the best option based on probability analysis (OR 3.05, 95% CrI 1.24–7.95, Pbest 53.9%). The superiority of the combination therapy changed little in RCTs with different follow-up durations and in sensitivity analyses. The limitations of this study include high risk of bias and short follow-up duration in most studies. Conclusions Dual therapy of albendazole and corticosteroids was the most efficacious regimen that could prevent seizure recurrence and promote lesion resolution in a follow-up period of around one year. It should be recommended for the management of SCG until more high-quality evidence is available. PMID:26849048

Adding results to a meta-analysis: Theory and example

NASA Astrophysics Data System (ADS)

Willson, Victor L.

Meta-analysis has been used as a research method to describe bodies of research data. It promotes hypothesis formation and the development of science education laws. A function overlooked, however, is the role it plays in updating research. Methods to integrate new research with meta-analysis results need explication. A procedure is presented using Bayesian analysis. Research in science education attitude correlation with achievement has been published after a recent meta-analysis of the topic. The results show how new findings complement the previous meta-analysis and extend its conclusions. Additional methodological questions adddressed are how studies are to be weighted, which variables are to be examined, and how often meta-analysis are to be updated.

Meta-Analysis of a Continuous Outcome Combining Individual Patient Data and Aggregate Data: A Method Based on Simulated Individual Patient Data

ERIC Educational Resources Information Center

Yamaguchi, Yusuke; Sakamoto, Wataru; Goto, Masashi; Staessen, Jan A.; Wang, Jiguang; Gueyffier, Francois; Riley, Richard D.

2014-01-01

When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta-analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta-analysis model to the mixture of simulated IPD and…

Aminoglycoside Therapy Manager: An Advanced Computer Program for Decision Support for Drug Dosing and Therapeutic Monitoring

PubMed Central

Lenert, Leslie; Lurie, Jon; Coleman, Robert; Klosterman, Heidrun; Blaschke, Terrence

1990-01-01

In this paper, we will describe an advanced drug dosing program, Aminoglycoside Therapy Manager that reasons using Bayesian pharmacokinetic modeling and symbolic modeling of patient status and drug response. Our design is similar to the design of the Digitalis Therapy Advisor program, but extends previous work by incorporating a Bayesian pharmacokinetic model, a “meta-level” analysis of drug concentrations to identify sampling errors and changes in pharmacokinetics, and including the results of the “meta-level” analysis in reasoning for dosing and therapeutic monitoring recommendations. The program is user friendly and runs on low cost general-purpose hardware. Validation studies show that the program is as accurate in predicting future drug concentrations as an expert using commercial Bayesian forecasting software.

An Exploratory Study Examining the Feasibility of Using Bayesian Networks to Predict Circuit Analysis Understanding

ERIC Educational Resources Information Center

Chung, Gregory K. W. K.; Dionne, Gary B.; Kaiser, William J.

2006-01-01

Our research question was whether we could develop a feasible technique, using Bayesian networks, to diagnose gaps in student knowledge. Thirty-four college-age participants completed tasks designed to measure conceptual knowledge, procedural knowledge, and problem-solving skills related to circuit analysis. A Bayesian network was used to model…

A comparison of discontinuation rates of tofacitinib and biologic disease-modifying anti-rheumatic drugs in rheumatoid arthritis: a systematic review and Bayesian network meta-analysis.

PubMed

Park, Sun-Kyeong; Lee, Min-Young; Jang, Eun-Jin; Kim, Hye-Lin; Ha, Dong-Mun; Lee, Eui-Kyung

2017-01-01

The purpose of this study was to compare the discontinuation rates of tofacitinib and biologics (tumour necrosis factor inhibitors (TNFi), abatacept, rituximab, and tocilizumab) in rheumatoid arthritis (RA) patients considering inadequate responses (IRs) to previous treatment(s). Randomised controlled trials of tofacitinib and biologics - reporting at least one total discontinuation, discontinuation due to lack of efficacy (LOE), and discontinuation due to adverse events (AEs) - were identified through systematic review. The analyses were conducted for patients with IRs to conventional synthetic disease-modifying anti-rheumatic drugs (cDMARDs) and for patients with biologics-IR, separately. Bayesian network meta-analysis was used to estimate rate ratio (RR) of a biologic relative to tofacitinib with 95% credible interval (CrI), and probability of RR being <1 (P[RR<1]). The analyses of 34 studies showed no significant differences in discontinuation rates between tofacitinib and biologics in the cDMARDs-IR group. In the biologics-IR group, however, TNFi (RR 0.17, 95% CrI 0.01-3.61, P[RR<1] 92.0%) and rituximab (RR 0.20, 95% CrI 0.01-2.91, P[RR<1] 92.3%) showed significantly lower total discontinuation rates than tofacitinib did. Despite the difference, discontinuation cases owing to LOE and AEs revealed that tofacitinib was comparable to the biologics. The comparability of discontinuation rate between tofacitinib and biologics was different based on previous treatments and discontinuation reasons: LOE, AEs, and total (due to other reasons). Therefore, those factors need to be considered to decide the optimal treatment strategy.

Univariate and bivariate likelihood-based meta-analysis methods performed comparably when marginal sensitivity and specificity were the targets of inference.

PubMed

Dahabreh, Issa J; Trikalinos, Thomas A; Lau, Joseph; Schmid, Christopher H

2017-03-01

To compare statistical methods for meta-analysis of sensitivity and specificity of medical tests (e.g., diagnostic or screening tests). We constructed a database of PubMed-indexed meta-analyses of test performance from which 2 × 2 tables for each included study could be extracted. We reanalyzed the data using univariate and bivariate random effects models fit with inverse variance and maximum likelihood methods. Analyses were performed using both normal and binomial likelihoods to describe within-study variability. The bivariate model using the binomial likelihood was also fit using a fully Bayesian approach. We use two worked examples-thoracic computerized tomography to detect aortic injury and rapid prescreening of Papanicolaou smears to detect cytological abnormalities-to highlight that different meta-analysis approaches can produce different results. We also present results from reanalysis of 308 meta-analyses of sensitivity and specificity. Models using the normal approximation produced sensitivity and specificity estimates closer to 50% and smaller standard errors compared to models using the binomial likelihood; absolute differences of 5% or greater were observed in 12% and 5% of meta-analyses for sensitivity and specificity, respectively. Results from univariate and bivariate random effects models were similar, regardless of estimation method. Maximum likelihood and Bayesian methods produced almost identical summary estimates under the bivariate model; however, Bayesian analyses indicated greater uncertainty around those estimates. Bivariate models produced imprecise estimates of the between-study correlation of sensitivity and specificity. Differences between methods were larger with increasing proportion of studies that were small or required a continuity correction. The binomial likelihood should be used to model within-study variability. Univariate and bivariate models give similar estimates of the marginal distributions for sensitivity and specificity. Bayesian methods fully quantify uncertainty and their ability to incorporate external evidence may be useful for imprecisely estimated parameters. Copyright © 2017 Elsevier Inc. All rights reserved.

A Bayesian Nonparametric Meta-Analysis Model

ERIC Educational Resources Information Center

Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.

2015-01-01

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…

Allowing for Correlations between Correlations in Random-Effects Meta-Analysis of Correlation Matrices

ERIC Educational Resources Information Center

Prevost, A. Toby; Mason, Dan; Griffin, Simon; Kinmonth, Ann-Louise; Sutton, Stephen; Spiegelhalter, David

2007-01-01

Practical meta-analysis of correlation matrices generally ignores covariances (and hence correlations) between correlation estimates. The authors consider various methods for allowing for covariances, including generalized least squares, maximum marginal likelihood, and Bayesian approaches, illustrated using a 6-dimensional response in a series of…

Comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder in children and adolescents: study protocol for a systematic review and network meta-analysis.

PubMed

Zhang, Yuqing; Zhou, Xinyu; Yang, Lining; Hetrick, Sarah E; Weisz, John R; Cuijpers, Pim; Barth, Jürgen; Del Giovane, Cinzia; Yuan, Shuai; Cohen, David; Gillies, Donna; Jiang, Xiaofeng; Teng, Teng; Xie, Peng

2018-03-12

Post-traumatic stress disorder (PTSD) is common among children and adolescents who are exposed to trauma, and it is often associated with significant negative impacts on their psychosocial functioning and quality of life. Many types of psychotherapies have been found to be effective for PTSD in children and adolescents. However, due to the lack of direct comparisons between different psychotherapies, the hierarchy of treatment efficacy is still unclear. Therefore, we plan to conduct a systematic review and network meta-analysis to evaluate the efficacy and acceptability of various types of psychotherapies for PTSD in children and adolescents. A systematic search will be conducted among eight electronic databases, including PubMed, Cochrane, Embase, Web of Science, PsycINFO, Cumulative Index of Nursing and Allied Health, Published International Literature on Traumatic Stress (PILOTS) and ProQuest Dissertations, from inception to October 2017. Randomised controlled trials, regardless of language, publication year and publication type, comparing any psychotherapies for PTSD to any control condition or alternative treatment in children and adolescents (18 years old or less) diagnosed with full or subclinical PTSD will be included. Study duration and the number of treatment sessions will not be limited. The primary outcome will be PTSD symptom severity at post-treatment as measured by a rating scale reported by the child, parent or a clinician. The secondary outcomes will include: (1) efficacy at follow-up; (2) acceptability (all-cause discontinuation); (3) anxiety symptom severity; (4) depressive symptom severity and (5) quality of life and functional improvement. Bayesian network meta-analyses for all relative outcome measures will be performed. We will conduct subgroup and sensitivity network meta-analyses to determine whether the findings are affected by study characteristics. The quality of the evidence contributing to network estimates of the primary outcome will be evaluated by the Grading of Recommendations, Assessment, Development and Evaluations framework. No ethical issues are foreseen. The results will be published in a peer-reviewed journal, which will be disseminated electronically and in print. This network meta-analysis may be updated to inform and guide the clinical management of PTSD in children and adolescents. CRD42016051786. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

PubMed Central

Majumdar, Arunabha; Haldar, Tanushree; Bhattacharya, Sourabh; Witte, John S.

2018-01-01

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method. PMID:29432419

A Bayesian Meta-Analysis of the Effect of Alcohol Use on HCV-Treatment Outcomes with a Comparison of Resampling Methods to Assess Uncertainty in Parameter Estimates.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cauthen, Katherine Regina; Lambert, Gregory Joseph; Finley, Patrick D.

There is mounting evidence that alcohol use is significantly linked to lower HCV treatment response rates in interferon-based therapies, though some of the evidence is conflicting. Furthermore, although health care providers recommend reducing or abstaining from alcohol use prior to treatment, many patients do not succeed in doing so. The goal of this meta-analysis was to systematically review and summarize the Englishlanguage literature up through January 30, 2015 regarding the relationship between alcohol use and HCV treatment outcomes, among patients who were not required to abstain from alcohol use in order to receive treatment. Seven pertinent articles studying 1,751 HCV-infectedmore » patients were identified. Log-ORs of HCV treatment response for heavy alcohol use and light alcohol use were calculated and compared. We employed a hierarchical Bayesian meta-analytic model to accommodate the small sample size. The summary estimate for the log-OR of HCV treatment response was -0.775 with a 95% credible interval of (-1.397, -0.236). The results of the Bayesian meta-analysis are slightly more conservative compared to those obtained from a boot-strapped, random effects model. We found evidence of heterogeneity (Q = 14.489, p = 0.025), accounting for 60.28% of the variation among log-ORs. Meta-regression to capture the sources of this heterogeneity did not identify any of the covariates investigated as significant. This meta-analysis confirms that heavy alcohol use is associated with decreased HCV treatment response compared to lighter levels of alcohol use. Further research is required to characterize the mechanism by which alcohol use affects HCV treatment response.« less

Systematic review with meta-analysis: comparative efficacy of biologics for induction and maintenance of mucosal healing in Crohn's disease and ulcerative colitis controlled trials.

PubMed

Cholapranee, A; Hazlewood, G S; Kaplan, G G; Peyrin-Biroulet, L; Ananthakrishnan, A N

2017-05-01

Mucosal healing is an important therapeutic endpoint in the management of Crohn's disease (CD) and ulcerative colitis (UC). Limited data exist regarding the comparative efficacy of various therapies in achieving this outcome. To perform a systematic review and meta-analysis of biologics for induction and maintenance of mucosal healing in Crohn's disease and ulcerative colitis. We performed a systematic review and meta-analysis of randomised controlled trials (RCT) examining mucosal healing as an endpoint of immunosuppressives, anti-tumour necrosis factor α (anti-TNF) or anti-integrin monoclonal antibody therapy for moderate-to-severe CD or UC. Pooled effect sizes for induction and maintenance of mucosal healing were calculated and pairwise treatment comparisons evaluated using a Bayesian network meta-analysis. A total of 12 RCTs were included in the meta-analysis (CD - 2 induction, 4 maintenance; UC - 8 induction, 5 maintenance). Duration of follow-up was 6-12 weeks for induction and 32-54 weeks for maintenance trials. In CD, anti-TNFs were more effective than placebo for maintaining mucosal healing [28% vs. 1%, Odds ratio (OR) 19.71, 95% confidence interval (CI) 3.51-110.84]. In UC, anti-TNFs and anti-integrins were more effective than placebo for inducing (45% vs. 30%) and maintaining mucosal healing (33% vs. 18%). In network analysis, adalimumab therapy was inferior to infliximab [OR 0.45, 95% credible interval (CrI) 0.25-0.82] and combination infliximab-azathioprine (OR 0.32, 95% CrI 0.12-0.84) for inducing mucosal healing in UC. There was no statistically significant pairwise difference between vedolizumab and anti-TNF agents in UC. Anti-TNF and anti-integrin biological agents are effective in inducing mucosal healing in UC, with adalimumab being inferior to infliximab or combination therapy. Infliximab and adalimumab were similar in CD. © 2017 John Wiley & Sons Ltd.

Sample size and power considerations in network meta-analysis

PubMed Central

2012-01-01

Background Network meta-analysis is becoming increasingly popular for establishing comparative effectiveness among multiple interventions for the same disease. Network meta-analysis inherits all methodological challenges of standard pairwise meta-analysis, but with increased complexity due to the multitude of intervention comparisons. One issue that is now widely recognized in pairwise meta-analysis is the issue of sample size and statistical power. This issue, however, has so far only received little attention in network meta-analysis. To date, no approaches have been proposed for evaluating the adequacy of the sample size, and thus power, in a treatment network. Findings In this article, we develop easy-to-use flexible methods for estimating the ‘effective sample size’ in indirect comparison meta-analysis and network meta-analysis. The effective sample size for a particular treatment comparison can be interpreted as the number of patients in a pairwise meta-analysis that would provide the same degree and strength of evidence as that which is provided in the indirect comparison or network meta-analysis. We further develop methods for retrospectively estimating the statistical power for each comparison in a network meta-analysis. We illustrate the performance of the proposed methods for estimating effective sample size and statistical power using data from a network meta-analysis on interventions for smoking cessation including over 100 trials. Conclusion The proposed methods are easy to use and will be of high value to regulatory agencies and decision makers who must assess the strength of the evidence supporting comparative effectiveness estimates. PMID:22992327

Network meta-analysis: an introduction for clinicians.

PubMed

Rouse, Benjamin; Chaimani, Anna; Li, Tianjing

2017-02-01

Network meta-analysis is a technique for comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. Network meta-analysis may assist assessing the comparative effectiveness of different treatments regularly used in clinical practice and, therefore, has become attractive among clinicians. However, if proper caution is not taken in conducting and interpreting network meta-analysis, inferences might be biased. The aim of this paper is to illustrate the process of network meta-analysis with the aid of a working example on first-line medical treatment for primary open-angle glaucoma. We discuss the key assumption of network meta-analysis, as well as the unique considerations for developing appropriate research questions, conducting the literature search, abstracting data, performing qualitative and quantitative synthesis, presenting results, drawing conclusions, and reporting the findings in a network meta-analysis.

Effectiveness of progesterone, cerclage and pessary for preventing preterm birth in singleton pregnancies: a systematic review and network meta-analysis.

PubMed

Jarde, A; Lutsiv, O; Park, C K; Beyene, J; Dodd, J M; Barrett, J; Shah, P S; Cook, J L; Saito, S; Biringer, A B; Sabatino, L; Giglia, L; Han, Z; Staub, K; Mundle, W; Chamberlain, J; McDonald, S D

2017-07-01

Preterm birth (PTB) is the leading cause of infant death, but it is unclear which intervention is best to prevent it. To compare progesterone, cerclage and pessary, determine their relative effects and rank them. We searched Medline, EMBASE, CINAHL, Cochrane CENTRAL and Web of Science (to April 2016), without restrictions, and screened references of previous reviews. We included randomised trials of progesterone, cerclage or pessary for preventing PTB in women with singleton pregnancies at risk as defined by each study. We extracted data by duplicate using a piloted form and performed Bayesian random-effects network meta-analyses and pairwise meta-analyses. We rated evidence quality using GRADE, ranked interventions using SUCRA and calculated numbers needed to treat (NNT). We included 36 trials (9425 women; 25 low risk of bias trials). Progesterone ranked first or second for most outcomes, reducing PTB < 34 weeks [odds ratio (OR) 0.44; 95% credible interval (CrI) 0.22-0.79; NNT 9; low quality], <37 weeks (OR 0.58; 95% CrI 0.41-0.79; NNT 9; moderate quality), and neonatal death (OR 0.50; 95% CrI 0.28-0.85; NNT 35; high quality), compared with control, in women overall at risk. We found similar results in the subgroup with previous PTB, but only a reduction of PTB < 34 weeks in women with a short cervix. Pessary showed inconsistent benefit and cerclage did not reduce PTB < 37 or <34 weeks. Progesterone was the best intervention for preventing PTB in singleton pregnancies at risk, reducing PTB < 34 weeks, <37 weeks, neonatal demise and other sequelae. Progesterone was better than cerclage and pessary to prevent preterm birth, neonatal death and more in network meta-analysis. © 2017 Royal College of Obstetricians and Gynaecologists.

Should Perioperative Supplemental Oxygen Be Routinely Recommended for Surgical Patients? A Bayesian Meta-analysis

PubMed Central

Kao, Lillian S.; Millas, Stefanos G.; Pedroza, Claudia; Tyson, Jon E.; Lally, Kevin P.

2012-01-01

Objective The purpose of this study is to use updated data and Bayesian methods to evaluate the effectiveness of hyperoxia to reduce surgical site infections (SSIs) and/or mortality in both colorectal and all surgical patients. Because few trials assessed potential harms of hyperoxia, hazards were not included. Background Use of hyperoxia to reduce SSIs is controversial. Three recent meta-analyses have had conflicting conclusions. Methods A systematic literature search and review were performed. Traditional fixed-effect and random-effects meta-analyses and Bayesian meta-analysis were performed to evaluate SSIs and mortality. Results Traditional meta-analysis yielded a relative risk of an SSI with hyperoxia among all surgery patients of 0.84 (95% confidence interval, CI, 0.73–0.97) and 0.84 (95% CI 0.61–1.16) for the fixed-effect and random effects models respectively. The probabilities of any risk reduction in SSIs among all surgery patients were 77%, 81%, and 83% for skeptical, neutral, and enthusiastic priors. Subset analysis of colorectal surgery patients increased the probabilities to 86%, 89%, and 92%. The probabilities of at least a 10% reduction were 57%, 62%, and 68% for all surgical patients and 71%, 75%, and 80% among the colorectal surgery subset. Conclusions There is a moderately high probability of a benefit to hyperoxia in reducing SSIs in colorectal surgery patients; however, the magnitude of benefit is relatively small and might not exceed treatment hazards. Further studies should focus on generalizability to other patient populations or on treatment hazards and other outcomes. PMID:23160100

Evaluating the Quality of Evidence from a Network Meta-Analysis

PubMed Central

Salanti, Georgia; Del Giovane, Cinzia; Chaimani, Anna; Caldwell, Deborah M.; Higgins, Julian P. T.

2014-01-01

Systematic reviews that collate data about the relative effects of multiple interventions via network meta-analysis are highly informative for decision-making purposes. A network meta-analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a network meta-analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a network meta-analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the network meta-analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of network meta-analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a network meta-analysis. Judgements about evidence from a network meta-analysis can be different from those made about evidence from pairwise meta-analyses. PMID:24992266

Real medical benefit assessed by indirect comparison.

PubMed

Falissard, Bruno; Zylberman, Myriam; Cucherat, Michel; Izard, Valérie; Meyer, François

2009-01-01

Frequently, in data packages submitted for Marketing Approval to the CHMP, there is a lack of relevant head-to-head comparisons of medicinal products that could enable national authorities responsible for the approval of reimbursement to assess the Added Therapeutic Value (ASMR) of new clinical entities or line extensions of existing therapies.Indirect or mixed treatment comparisons (MTC) are methods stemming from the field of meta-analysis that have been designed to tackle this problem. Adjusted indirect comparisons, meta-regressions, mixed models, Bayesian network analyses pool results of randomised controlled trials (RCTs), enabling a quantitative synthesis.The REAL procedure, recently developed by the HAS (French National Authority for Health), is a mixture of an MTC and effect model based on expert opinions. It is intended to translate the efficacy observed in the trials into effectiveness expected in day-to-day clinical practice in France.

Extraordinary Claims Require Extraordinary Evidence: The Case of Non-Local Perception, a Classical and Bayesian Review of Evidences

PubMed Central

Tressoldi, Patrizio E.

2011-01-01

Starting from the famous phrase “extraordinary claims require extraordinary evidence,” we will present the evidence supporting the concept that human visual perception may have non-local properties, in other words, that it may operate beyond the space and time constraints of sensory organs, in order to discuss which criteria can be used to define evidence as extraordinary. This evidence has been obtained from seven databases which are related to six different protocols used to test the reality and the functioning of non-local perception, analyzed using both a frequentist and a new Bayesian meta-analysis statistical procedure. According to a frequentist meta-analysis, the null hypothesis can be rejected for all six protocols even if the effect sizes range from 0.007 to 0.28. According to Bayesian meta-analysis, the Bayes factors provides strong evidence to support the alternative hypothesis (H1) over the null hypothesis (H0), but only for three out of the six protocols. We will discuss whether quantitative psychology can contribute to defining the criteria for the acceptance of new scientific ideas in order to avoid the inconclusive controversies between supporters and opponents. PMID:21713069

Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences

PubMed Central

Bujkiewicz, Sylwia; Riley, Richard D

2016-01-01

Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is little guidance about the choice of prior distributions for the variances or, crucially, the between-study correlation, ρB; for the latter, researchers often use a Uniform(−1,1) distribution assuming it is vague. In this paper, an extensive simulation study and a real illustrative example is used to examine the impact of various (realistically) vague prior distributions for ρB and the between-study variances within a Bayesian bivariate random-effects meta-analysis of two correlated treatment effects. A range of diverse scenarios are considered, including complete and missing data, to examine the impact of the prior distributions on posterior results (for treatment effect and between-study correlation), amount of borrowing of strength, and joint predictive distributions of treatment effectiveness in new studies. Two key recommendations are identified to improve the robustness of multivariate meta-analysis results. First, the routine use of a Uniform(−1,1) prior distribution for ρB should be avoided, if possible, as it is not necessarily vague. Instead, researchers should identify a sensible prior distribution, for example, by restricting values to be positive or negative as indicated by prior knowledge. Second, it remains critical to use sensible (e.g. empirically based) prior distributions for the between-study variances, as an inappropriate choice can adversely impact the posterior distribution for ρB, which may then adversely affect inferences such as joint predictive probabilities. These recommendations are especially important with a small number of studies and missing data. PMID:26988929

Relative efficacy and tolerability of etoricoxib, celecoxib, and naproxen in the treatment of osteoarthritis : A Bayesian network meta-analysis of randomized controlled trials based on patient withdrawal.

PubMed

Song, Gwan Gyu; Seo, Young Ho; Kim, Jae-Hoon; Choi, Sung Jae; Ji, Jong Dae; Lee, Young Ho

2016-06-01

This study aimed to assess the relative efficacy and tolerability of etoricoxib, celecoxib, and naproxen at recommended dosages in patients with osteoarthritis (OA). Randomized controlled trials (RCTs) examining the efficacy and tolerability of etoricoxib 30-60 mg, celecoxib 200-400 mg, and naproxen 1000 mg, based on the number of patient withdrawals among those with OA, were included in this network meta-analysis. We performed a Bayesian random-effects network meta-analysis to combine direct and indirect evidence from the RCTs. Eight RCTs, including 5,942 patients, met the inclusion criteria. The proportion of patient withdrawals due to lack of efficacy was significantly lower in the etoricoxib 30-60 mg (OR 0.21, 95 % CrI 0.12-0.38), celecoxib 200-400 mg (OR 0.29, 95 % CrI 0.18-0.47), and naproxen 1000 mg (OR 0.31, 95 % CrI 0.18-0.51) groups than in the placebo group. The number of patient withdrawals due to lack of efficacy tended to be lower in the etoricoxib 30-60 mg group than in the naproxen 1000 mg and celecoxib 200-400 mg groups, although they did not reach statistical significance (OR 0.68, 95 % CrI 0.36-1.33 and OR 0.70, 95 % CrI 0.38-1.37, respectively). Ranking probabilities based on the surface under the cumulative ranking curve (SUCRA) indicated that etoricoxib 30-60 mg had the highest probability of being the best treatment based on the number of withdrawals due to lack of efficacy (SUCRA = 0.9168) followed by celecoxib 200-400 mg (SUCRA = 0.5659), naproxen 1000 mg (SUCRA = 0.5171), and placebo (SUCRA = 0.000189). With respect to tolerability, the number of withdrawals due to adverse events was not significantly different among etoricoxib, celecoxib, naproxen, and placebo, although it tended to be lower with etoricoxib and placebo. Etoricoxib 30-60 mg, celecoxib 200-400 mg, and naproxen 1000 mg were more efficacious than placebo. However, there was no significant difference in efficacy and tolerability between the medications.

Accounting for correlation in network meta-analysis with multi-arm trials.

PubMed

Franchini, A J; Dias, S; Ades, A E; Jansen, J P; Welton, N J

2012-06-01

Multi-arm trials (trials with more than two arms) are particularly valuable forms of evidence for network meta-analysis (NMA). Trial results are available either as arm-level summaries, where effect measures are reported for each arm, or as contrast-level summaries, where the differences in effect between arms compare with the control arm chosen for the trial. We show that likelihood-based inference in both contrast-level and arm-level formats is identical if there are only two-arm trials, but that if there are multi-arm trials, results from the contrast-level format will be incorrect unless correlations are accounted for in the likelihood. We review Bayesian and frequentist software for NMA with multi-arm trials that can account for this correlation and give an illustrative example of the difference in estimates that can be introduced if the correlations are not incorporated. We discuss methods of imputing correlations when they cannot be derived from the reported results and urge trialists to report the standard error for the control arm even if only contrast-level summaries are reported. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

A Bayesian Missing Data Framework for Generalized Multiple Outcome Mixed Treatment Comparisons

ERIC Educational Resources Information Center

Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P.

2016-01-01

Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two…

Network meta-analysis: an introduction for pharmacists.

PubMed

Xu, Yina; Amiche, Mohamed Amine; Tadrous, Mina

2018-05-21

Network meta-analysis is a new tool used to summarize and compare studies for multiple interventions, irrespective of whether these interventions have been directly evaluated against each other. Network meta-analysis is quickly becoming the standard in conducting therapeutic reviews and clinical guideline development. However, little guidance is available to help pharmacists review network meta-analysis studies in their practice. Major institutions such as the Cochrane Collaboration, Agency for Healthcare Research and Quality, Canadian Agency for Drugs and Technologies in Health, and National Institute for Health and Care Excellence Decision Support Unit have endorsed utilizing network meta-analysis to establish therapeutic evidence and inform decision making. Our objective is to introduce this novel technique to pharmacy practitioners, and highlight key assumptions behind network meta-analysis studies.

A tutorial on Bayesian bivariate meta-analysis of mixed binary-continuous outcomes with missing treatment effects.

PubMed

Gajic-Veljanoski, Olga; Cheung, Angela M; Bayoumi, Ahmed M; Tomlinson, George

2016-05-30

Bivariate random-effects meta-analysis (BVMA) is a method of data synthesis that accounts for treatment effects measured on two outcomes. BVMA gives more precise estimates of the population mean and predicted values than two univariate random-effects meta-analyses (UVMAs). BVMA also addresses bias from incomplete reporting of outcomes. A few tutorials have covered technical details of BVMA of categorical or continuous outcomes. Limited guidance is available on how to analyze datasets that include trials with mixed continuous-binary outcomes where treatment effects on one outcome or the other are not reported. Given the advantages of Bayesian BVMA for handling missing outcomes, we present a tutorial for Bayesian BVMA of incompletely reported treatment effects on mixed bivariate outcomes. This step-by-step approach can serve as a model for our intended audience, the methodologist familiar with Bayesian meta-analysis, looking for practical advice on fitting bivariate models. To facilitate application of the proposed methods, we include our WinBUGS code. As an example, we use aggregate-level data from published trials to demonstrate the estimation of the effects of vitamin K and bisphosphonates on two correlated bone outcomes, fracture, and bone mineral density. We present datasets where reporting of the pairs of treatment effects on both outcomes was 'partially' complete (i.e., pairs completely reported in some trials), and we outline steps for modeling the incompletely reported data. To assess what is gained from the additional work required by BVMA, we compare the resulting estimates to those from separate UVMAs. We discuss methodological findings and make four recommendations. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

PubMed Central

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias

2018-01-01

Abstract Objective To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) (“living” network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Outcomes and analysis Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. Results 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. Conclusions In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. PMID:29490922

Long term outcomes of new generation drug eluting stents versus coronary artery bypass grafting for multivessel and/or left main coronary artery disease. A Bayesian network meta-analysis of randomized controlled trials.

PubMed

Mina, George S; Watti, Hussam; Soliman, Demiana; Shewale, Anand; Atkins, Jessica; Reddy, Pratap; Dominic, Paari

2018-01-05

Most data guiding revascularization of multivessel disease (MVD) and/or left main disease (LMD) favor coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI). However, those data are based on trials comparing CABG to bare metal stents (BMS) or old generation drug eluting stents (OG-DES). Hence, it is essential to outcomes of CABG to those of new generation drug eluting stents (NG-DES). We searched PUBMED and Cochrane database for trials evaluating revascularization of MVD and/or LMD with CABG and/or PCI. A Bayesian network meta-analysis was performed to calculate odds ratios (OR) and 95% credible intervals (CrI). Primary outcome was major adverse cardiovascular events (MACE) at 3-5 years. Secondary outcomes were mortality, cerebrovascular accidents (CVA), myocardial infarction (MI) and repeat revascularization. We included 10 trials with a total of 9287 patients. CABG was associated with lower MACE when compared to BMS or OG-DES. However, MACE was not significantly different between CABG and NG-DES (OR 0.79, CrI 0.45-1.40). Moreover, there were no significant differences between CABG and NG-DES in mortality (OR 0.78, CrI 0.45-1.37), CVA (OR 0.93 CrI 0.35-2.2) or MI (OR 0.6, CrI 0.17-2.0). On the other hand, CABG was associated with lower repeat revascularization (OR 0.55, CrI 0.36-0.84). Our study suggests that NG-DES is an acceptable alternative to CABG in patients with MVD and/or LMD. However, repeat revascularization remains to be lower with CABG than with PCI. Copyright © 2018. Published by Elsevier Inc.

Comparative efficacy and safety of tocilizumab, rituximab, abatacept and tofacitinib in patients with active rheumatoid arthritis that inadequately responds to tumor necrosis factor inhibitors: a Bayesian network meta-analysis of randomized controlled trials.

PubMed

Lee, Young Ho; Bae, Sang-Cheol

2016-11-01

This study aimed to assess the relative efficacy and safety of biologics and tofacitinib in patients with rheumatoid arthritis (RA) showing an inadequate response to tumor necrosis factor (TNF) inhibitors. We performed a Bayesian network meta-analysis to combine the direct and indirect evidence from randomized controlled trials (RCTs) examining the efficacy and safety of tocilizumab, rituximab, abatacept and tofacitinib in patients with RA that inadequately responds to TNF inhibitors. Four RCTs including 1796 patients met the inclusion criteria. The tocilizumab 8 mg group showed a significantly higher American College of Rheumatology 20% (ACR20) response rate than the abatacept and tofacitinib groups. Ranking probability based on surface under the cumulative ranking curve (SUCRA) indicated that tocilizumab 8 mg had the highest probability of being the best treatment for achieving the ACR20 response rate (SUCRA = 0.9863), followed by rituximab (SUCRA = 0.6623), abatacept (SUCRA = 0.5428), tocilizumab 4 mg (SUCRA = 0.4956), tofacitinib 10 mg (SUCRA = 0.4715), tofacitinib 5 mg (SUCRA = 0.3415) and placebo (SUCRA = 0). In contrast, the safety based on the number of withdrawals due to adverse events did not differ significantly among the treatment options. Tocilizumab 8 mg was the second-line non-TNF biologic with the highest performance regarding an early good response based on ACR20 response rate and acceptable safety profile, followed by rituximab, abatacept and tofacitinib in patients with RA and an inadequate response to anti-TNF therapy, and none of these treatments were associated with a significant risk of withdrawal due to adverse events. © 2015 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd.

Comparative Effectiveness of Mesalamine, Sulfasalazine, Corticosteroids, and Budesonide for the Induction of Remission in Crohn's Disease: A Bayesian Network Meta-analysis.

PubMed

Coward, Stephanie; Kuenzig, M Ellen; Hazlewood, Glen; Clement, Fiona; McBrien, Kerry; Holmes, Rebecca; Panaccione, Remo; Ghosh, Subrata; Seow, Cynthia H; Rezaie, Ali; Kaplan, Gilaad G

2017-03-01

Induction treatment of mild-to-moderate Crohn's disease is controversial. To compare the induction of remission between different doses of mesalamine, sulfasalazine, corticosteroids, and budesonide for active Crohn's disease. We identified randomized controlled trials from existing Cochrane reviews and an updated literature search in Medline, EMBASE, and CENTRAL to November 2015. We included randomized controlled trials (n = 22) in adult patients with Crohn's disease that compared budesonide, sulfasalazine, mesalamine, or corticosteroids with placebo or each other, for the induction of remission (8-17 wks). Mesalamine (above and below 2.4 g/d) and budesonide (above and below 6 mg/d) were stratified into low and high doses. Our primary outcome was remission, defined as a Crohn's Disease Activity Index score <150. A Bayesian random-effects network meta-analysis was performed on the proportion in remission. Corticosteroids (odds ratio [OR] = 3.80; 95% credible interval [CrI]: 2.48-5.66), high-dose budesonide (OR = 2.96; 95% CrI: 2.06-4.30), and high-dose mesalamine (OR = 2.29; 95% CrI: 1.58-3.33) were superior to placebo. Corticosteroids were similar to high-dose budesonide (OR = 1.21; 95% CrI: 0.84-1.76), but more effective than high-dose mesalamine (OR = 1.83; 95% CrI: 1.16-2.88). Sulfasalazine was not significantly superior to any therapy including placebo. Randomized controlled trials that use a strict definition of induction of remission and disease severity at enrollment to assess effectiveness in treating mild-to-moderate Crohn's disease are limited. Corticosteroids and high-dose budesonide were effective treatments for inducing remission in mild-to-moderate Crohn's disease. High-dose mesalamine is an option among patients preferring to avoid steroids.

Systematic review with network meta-analysis: comparative efficacy and tolerability of different intravenous iron formulations for the treatment of iron deficiency anaemia in patients with inflammatory bowel disease.

PubMed

Aksan, A; Işık, H; Radeke, H H; Dignass, A; Stein, J

2017-05-01

Iron deficiency anaemia (IDA) is a common complication of inflammatory bowel disease (IBD) associated with reduced quality of life and increased hospitalisation rates. While the best way of treating IDA in IBD patients is not clearly established, current European guidelines recommend intravenous iron therapy in IBD patients with severe anaemia or intolerance to oral iron compounds. To compare the efficacy and tolerability of different intravenous iron formulations used to treat IDA in IBD patients in a systematic review and Bayesian network meta-analysis (NMA), PROSPERO registration number: 42016046565. In June 2016, we systematically searched for studies analysing efficacy and safety of intravenous iron for IDA therapy in IBD. Primary outcome was therapy response, defined as Hb normalisation or increase ≥2 g/dL. Five randomised, controlled trials (n = 1143 patients) were included in a network meta-analysis. Only ferric carboxymaltose was significantly more effective than oral iron [OR=1.9, 95% CrI: (1.1;3.2)]. Rank probabilities showed ferric carboxymaltose to be most effective, followed by iron sucrose, iron isomaltose and oral iron. Pooled data from the systematic review (n = 1746 patients) revealed adverse event rates of 12.0%, 15.3%, 12.0%, 17.0% for ferric carboxymaltose, iron sucrose, iron dextran and iron isomaltose respectively. One drug-related serious adverse event (SAE) each was reported for ferric carboxymaltose and iron isomaltoside, and one possibly drug-related SAE for iron sucrose. Ferric carboxymaltose was the most effective intravenous iron formulation, followed by iron sucrose. In addition, ferric carboxymaltose tended to be better tolerated. Thus, nanocolloidal IV iron products exhibit differing therapeutic and safety characteristics and are not interchangeable. © 2017 John Wiley & Sons Ltd.

Systematic review with network meta-analysis: comparative efficacy and safety of budesonide and mesalazine (mesalamine) for Crohn's disease.

PubMed

Moja, L; Danese, S; Fiorino, G; Del Giovane, C; Bonovas, S

2015-06-01

Budesonide and mesalazine (mesalamine) are commonly used in the medical management of patients with mild to moderate Crohn's disease. To assess their comparative efficacy and harm using the methodology of network meta-analysis. A comprehensive search of Medline, Embase, the Cochrane Library and ClinicalTrials.gov, through October 2014, was performed to identify randomised controlled trials (RCTs) that recruited adult patients with active or quiescent Crohn's disease, and compared budesonide or mesalazine with placebo, or against each other, or different dosing strategies of one drug. Twenty-five RCTs were combined using Bayesian network meta-analysis. Budesonide 9 mg/day, or at higher doses (15 or 18 mg/day), was shown superior to placebo for induction of remission [odds ratio (OR), 2.93; 95% credible interval (CrI), 1.52-5.39, and OR, 3.28; CrI, 1.46-7.55 respectively] and ranks at the top of the hierarchy of the competing treatments. For maintenance of remission, budesonide 6 mg/day demonstrated superiority over placebo (OR, 1.69; CrI, 1.05-2.75), being also at the best ranking position among all compared treatment strategies. No other comparisons (i.e. different doses of mesalazine vs. placebo or budesonide, for induction or maintenance of remission) reached significance. The occurrence of withdrawals due to adverse events was not shown different between budesonide, mesalazine and placebo, in both the induction and maintenance phases. Budesonide, at the doses of 9 mg/day, or higher, for induction of remission in active mild or moderate Crohn's disease, and at 6 mg/day for maintenance of remission, appears to be the best treatment choice. © 2015 John Wiley & Sons Ltd.

Meta-analysis of diagnostic test data: a bivariate Bayesian modeling approach.

PubMed

Verde, Pablo E

2010-12-30

In the last decades, the amount of published results on clinical diagnostic tests has expanded very rapidly. The counterpart to this development has been the formal evaluation and synthesis of diagnostic results. However, published results present substantial heterogeneity and they can be regarded as so far removed from the classical domain of meta-analysis, that they can provide a rather severe test of classical statistical methods. Recently, bivariate random effects meta-analytic methods, which model the pairs of sensitivities and specificities, have been presented from the classical point of view. In this work a bivariate Bayesian modeling approach is presented. This approach substantially extends the scope of classical bivariate methods by allowing the structural distribution of the random effects to depend on multiple sources of variability. Meta-analysis is summarized by the predictive posterior distributions for sensitivity and specificity. This new approach allows, also, to perform substantial model checking, model diagnostic and model selection. Statistical computations are implemented in the public domain statistical software (WinBUGS and R) and illustrated with real data examples. Copyright © 2010 John Wiley & Sons, Ltd.

Outdoor fine particles and nonfatal strokes: systematic review and meta-analysis.

PubMed

Shin, Hwashin H; Fann, Neal; Burnett, Richard T; Cohen, Aaron; Hubbell, Bryan J

2014-11-01

Epidemiologic studies find that long- and short-term exposure to fine particles (PM2.5) is associated with adverse cardiovascular outcomes, including ischemic and hemorrhagic strokes. However, few systematic reviews or meta-analyses have synthesized these results. We reviewed epidemiologic studies that estimated the risks of nonfatal strokes attributable to ambient PM2.5. To pool risks among studies we used a random-effects model and 2 Bayesian approaches. The first Bayesian approach assumes a normal prior that allows risks to be zero, positive or negative. The second assumes a gamma prior, where risks can only be positive. This second approach is proposed when the number of studies pooled is small, and there is toxicological or clinical literature to support a causal relation. We identified 20 studies suitable for quantitative meta-analysis. Evidence for publication bias is limited. The frequentist meta-analysis produced pooled risk ratios of 1.06 (95% confidence interval = 1.00-1.13) and 1.007 (1.003-1.010) for long- and short-term effects, respectively. The Bayesian meta-analysis found a posterior mean risk ratio of 1.08 (95% posterior interval = 0.96-1.26) and 1.008 (1.003-1.013) from a normal prior, and of 1.05 (1.02-1.10) and 1.008 (1.004-1.013) from a gamma prior, for long- and short-term effects, respectively, per 10 μg/m PM2.5. Sufficient evidence exists to develop a concentration-response relation for short- and long-term exposures to PM2.5 and stroke incidence. Long-term exposures to PM2.5 result in a higher risk ratio than short-term exposures, regardless of the pooling method. The evidence for short-term PM2.5-related ischemic stroke is especially strong.

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study.

PubMed

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias; Salanti, Georgia

2018-02-28

To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A Bayes Factor Meta-Analysis of Recent Extrasensory Perception Experiments: Comment on Storm, Tressoldi, and Di Risio (2010)

ERIC Educational Resources Information Center

Rouder, Jeffrey N.; Morey, Richard D.; Province, Jordan M.

2013-01-01

Psi phenomena, such as mental telepathy, precognition, and clairvoyance, have garnered much recent attention. We reassess the evidence for psi effects from Storm, Tressoldi, and Di Risio's (2010) meta-analysis. Our analysis differs from Storm et al.'s in that we rely on Bayes factors, a Bayesian approach for stating the evidence from data for…

Network meta-analyses performed by contracting companies and commissioned by industry.

PubMed

Schuit, Ewoud; Ioannidis, John Pa

2016-11-25

Industry commissions contracting companies to perform network meta-analysis for health technology assessment (HTA) and reimbursement submissions. Our objective was to estimate the number of network meta-analyses performed by consulting companies contracted by industry, to assess whether they were published, and to explore reasons for non-publication. We searched MEDLINE for network meta-analyses of randomized trials. Papers were included if they had authors affiliated with any contracting company. All identified contracting companies as well as additional ones from the list of the exhibitors at the International Society for Pharmacoeconomics and Outcomes Research, an annual meeting that representatives from many contracting companies attend and exhibit at, were surveyed regarding conduct and publication of network meta-analyses. In 162 of 822 (20%) network meta-analysis papers, authors were affiliated to 66 contracting companies. Another 36 contracting companies were identified by the exhibitors list. Three companies had no contact information and six merged with others, therefore 93 companies were contacted. Thirty seven out of ninety three (40%) companies responded, and 19 indicated that they had performed a total of 476 network meta-analyses, but only 102 (21%) papers were published. Thirteen companies that disclosed to have conducted 174 network meta-analyses (45 published) provided reasons for non-publication. Of the 129 still unpublished meta-analyses, for 40 there were plans for future publication, for 37 the sponsor did not allow publication, for 16 the contracting companies did not plan to publish the meta-analysis, for another 23 plans were unclear, and the remaining 13 were used as HTA submission. The protocol of the network meta-analysis was publically available from 11/162 (6.8%) network meta-analyses published by authors affiliated with contracting companies. There is a prolific sector of professional contracting companies that perform network meta-analyses. Industry commissions many network meta-analyses, but most are not registered before or published after analyses in the scientific literature. Mechanisms to improve publication rates of network meta-analysis commissioned by industry are warranted.

Confidence Intervals for the Between-Study Variance in Random Effects Meta-Analysis Using Generalised Cochran Heterogeneity Statistics

ERIC Educational Resources Information Center

Jackson, Dan

2013-01-01

Statistical inference is problematic in the common situation in meta-analysis where the random effects model is fitted to just a handful of studies. In particular, the asymptotic theory of maximum likelihood provides a poor approximation, and Bayesian methods are sensitive to the prior specification. Hence, less efficient, but easily computed and…

Is Local Infiltration Analgesia Superior to Peripheral Nerve Blockade for Pain Management After THA: A Network Meta-analysis.

PubMed

Jiménez-Almonte, José H; Wyles, Cody C; Wyles, Saranya P; Norambuena-Morales, German A; Báez, Pedro J; Murad, Mohammad H; Sierra, Rafael J

2016-02-01

Local infiltration analgesia and peripheral nerve blocks are common methods for pain management in patients after THA but direct head-to-head, randomized controlled trials (RCTs) have not been performed. A network meta-analysis allows indirect comparison of individual treatments relative to a common comparator; in this case placebo (or no intervention), epidural analgesia, and intrathecal morphine, yielding an estimate of comparative efficacy. We asked, when compared with a placebo, (1) does use of local infiltration analgesia reduce patient pain scores and opioid consumption, (2) does use of peripheral nerve blocks reduce patient pain scores and opioid consumption, and (3) is local infiltration analgesia favored over peripheral nerve blocks for postoperative pain management after THA? We searched six databases, from inception through June 30, 2014, to identify RCTs comparing local infiltration analgesia or peripheral nerve block use in patients after THA. A total of 35 RCTs at low risk of bias based on the recommended Cochrane Collaboration risk assessment tool were included in the network meta-analysis (2296 patients). Primary outcomes for this review were patient pain scores at rest and cumulative opioid consumption, both assessed at 24 hours after THA. Because of substantial heterogeneity (variation of outcomes between studies) across included trials, a random effect model for meta-analysis was used to estimate the weighted mean difference (WMD) and 95% CI. The gray literature was searched with the same inclusion criteria as published trials. Only one unpublished trial (published abstract) fulfilled our criteria and was included in this review. All other studies included in this systematic review were full published articles. Bayesian network meta-analysis included all RCTs that compared local infiltration analgesia or peripheral nerve blocks with placebo (or no intervention), epidural analgesia, and intrathecal morphine. Compared with placebo, local infiltration analgesia reduced patient pain scores (WMD, -0.61; 95% CI, -0.97 to -0.24; p = 0.001) and opioid consumption (WMD, -7.16 mg; 95% CI, -11.98 to -2.35; p = 0.004). Peripheral nerve blocks did not result in lower pain scores or reduced opioid consumption compared with placebo (WMD, -0.43; 95% CI, -0.99 to 0.12; p = 0.12 and WMD, -3.14 mg, 95% CI, -11.30 to 5.02; p = 0.45). However, network meta-analysis comparing local infiltration analgesia with peripheral nerve blocks through common comparators showed no differences between postoperative pain scores (WMD, -0.36; 95% CI, -1.06 to 0.31) and opioid consumption (WMD, -4.59 mg; 95% CI, -9.35 to 0.17), although rank-order analysis found local infiltration analgesia to be ranked first in more simulations than peripheral nerve blocks, suggesting that it may be more effective. Using the novel statistical network meta-analysis approach, we found no differences between local infiltration analgesia and peripheral nerve blocks in terms of analgesia or opioid consumption 24 hours after THA; there was a suggestion of a slight advantage to peripheral nerve blocks based on rank-order analysis, but the effect size in question is likely not large. Given the slight difference between interventions, clinicians may choose to focus on other factors such as cost and intervention-related complications when debating which analgesic treatment to use after THA. Level I, therapeutic study.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Systematic review, network meta-analysis and economic evaluation of biological therapy for the management of active psoriatic arthritis.

PubMed

Cawson, Matthew Richard; Mitchell, Stephen Andrew; Knight, Chris; Wildey, Henry; Spurden, Dean; Bird, Alex; Orme, Michelle Elaine

2014-01-20

An updated economic evaluation was conducted to compare the cost-effectiveness of the four tumour necrosis factor (TNF)-α inhibitors adalimumab, etanercept, golimumab and infliximab in active, progressive psoriatic arthritis (PsA) where response to standard treatment has been inadequate. A systematic review was conducted to identify relevant, recently published studies and the new trial data were synthesised, via a Bayesian network meta-analysis (NMA), to estimate the relative efficacy of the TNF-α inhibitors in terms of Psoriatic Arthritis Response Criteria (PsARC) response, Health Assessment Questionnaire (HAQ) scores and Psoriasis Area and Severity Index (PASI). A previously developed economic model was updated with the new meta-analysis results and current cost data. The model was adapted to delineate patients by PASI 50%, 75% and 90% response rates to differentiate between psoriasis outcomes. All four licensed TNF-α inhibitors were significantly more effective than placebo in achieving PsARC response in patients with active PsA. Adalimumab, etanercept and infliximab were significantly more effective than placebo in improving HAQ scores in patients who had achieved a PsARC response and in improving HAQ scores in PsARC non-responders. In an analysis using 1,000 model simulations, on average etanercept was the most cost-effective treatment and, at the National Institute for Health and Care Excellence willingness-to-pay threshold of between £20,000 to £30,000, etanercept is the preferred option. The economic analysis agrees with the conclusions from the previous models, in that biologics are shown to be cost-effective for treating patients with active PsA compared with the conventional management strategy. In particular, etanercept is cost-effective compared with the other biologic treatments.

Systematic review with meta-analysis: Comparative efficacy of biologics for induction and maintenance of mucosal healing in Crohn’s disease and ulcerative colitis controlled trials

PubMed Central

Cholapranee, Aurada; Hazlewood, Glen S; Kaplan, Gilaad G.; Peyrin-Biroulet, Laurent; Ananthakrishnan, Ashwin N

2017-01-01

Background Mucosal healing is an important therapeutic endpoint in the management of Crohn’s disease (CD) and ulcerative colitis (UC). Limited data exists regarding the comparative efficacy of various therapies in achieving this outcome. Methods We performed a systematic review and meta-analysis of randomized controlled trials (RCT) examining mucosal healing as an endpoint of immunosuppressives, anti-tumor necrosis factor α (anti-TNF) or anti-integrin monoclonal antibody therapy for moderate-to-severe CD or UC. Pooled effect sizes for induction and maintenance of mucosal healing were calculated and pair-wise treatment comparisons evaluated using a Bayesian network meta-analysis. Results A total of 12 RCTs were included in the meta-analysis (CD – 2 induction, 4 maintenance; UC – 8 induction, 5 maintenance). Duration of follow-up was 6–12 weeks for induction and 32–54 weeks for maintenance trials. In CD, anti-TNFs were more effective than placebo for maintaining mucosal healing (28% vs. 1%, Odds ratio (OR) 19.71, 95% confidence interval (CI) 3.51 – 110.84). In UC, anti-TNFs and anti-integrins were more effective than placebo for inducing (45% vs. 30%) and maintaining mucosal healing (33% vs. 18%). In network analysis, adalimumab therapy was inferior to infliximab (OR 0.45, 95% credible interval (CrI) 0.25 – 0.82) and combination infliximab-azathioprine (OR 0.32, 95% CrI 0.12 – 0.84) for inducing mucosal healing in UC. There was no statistically significant pairwise difference between vedolizumab and anti-TNF agents in UC. Conclusion Anti-TNF and anti-integrin biologic agents are effective in inducing mucosal healing in UC with adalimumab being inferior to infliximab or combination therapy. Infliximab and adalimumab were similar in CD. PMID:28326566

Testing the Storm et al. (2010) Meta-Analysis Using Bayesian and Frequentist Approaches: Reply to Rouder et al. (2013)

ERIC Educational Resources Information Center

Storm, Lance; Tressoldi, Patrizio E.; Utts, Jessica

2013-01-01

Rouder, Morey, and Province (2013) stated that (a) the evidence-based case for psi in Storm, Tressoldi, and Di Risio's (2010) meta-analysis is supported only by a number of studies that used manual randomization, and (b) when these studies are excluded so that only investigations using automatic randomization are evaluated (and some additional…

A Mediterranean diet improves HbA1c but not fasting blood glucose compared to alternative dietary strategies: a network meta-analysis.

PubMed

Carter, P; Achana, F; Troughton, J; Gray, L J; Khunti, K; Davies, M J

2014-06-01

Overweight or obese individuals with type 2 diabetes are encouraged to lose weight for optimal glucose management, yet many find this difficult. Determining whether alterations in dietary patterns irrespective of weight loss can aid glucose control has not been fully investigated. We conducted a systematic review and meta-analysis aiming to determine the effects of a Mediterranean diet compared to other dietary interventions on glycaemic control irrespective of weight loss. Electronic databases were searched for controlled trials that included a Mediterranean diet intervention. The interventions included all major components of the Mediterranean diet and were carried out in free-living individuals at high risk or diagnosed with type 2 diabetes. Network meta-analysis compared all interventions with one another at the same time as maintaining randomisation. Analyses were conducted within a Bayesian framework. Eight studies met the inclusion criteria, seven examined fasting blood glucose (n = 972), six examined fasting insulin (n = 1330) and three examined HbA1c (n = 487). None of the interventions were significantly better than the others in lowering glucose parameters. The Mediterranean diet reduced HbA1c significantly compared to usual care but not compared to the Palaeolithic diet. The effect of alterations in dietary practice irrespective of weight loss on glycaemic control cannot be concluded from the present review. The need for further research in this area is apparent because no firm conclusions about relative effectiveness of interventions could be drawn as a result of the paucity of the evidence. © 2013 The British Dietetic Association Ltd.

Proton pump inhibitors for preventing non-steroidal anti-inflammatory drug induced gastrointestinal toxicity: a systematic review.

PubMed

Yang, Man; He, Min; Zhao, Miao; Zou, Bing; Liu, Jun; Luo, Ling-Min; Li, Qiu-Lan; He, Jun-Hui; Lei, Ping-Guang

2017-06-01

Proton pump inhibitors (PPIs) are recommended for preventing gastrointestinal lesions induced by non-steroidal anti-inflammatory drugs (NSAIDs). We performed this study: (1) to evaluate the effectiveness and safety of PPIs, (2) to explore the association between effectiveness and potential influential factors, and (3) to investigate the comparative effect of different PPIs. MEDLINE, EMBASE, and the Cochrane Library were searched to identify randomized controlled trials comparing different classes of PPIs, or comparing PPIs with placebo, H 2 receptor antagonists or misoprostol in NSAIDs users. Both pairwise meta-analysis and Bayesian network meta-analysis were performed. Analyses were based on 12,532 participants from 31 trials. PPIs were significantly more effective than placebo in reducing ulcer complications (relative risk [RR] = 0.29; 95% confidence interval [CI], 0.20 to 0.42) and endoscopic peptic ulcers (RR = 0.27; 95% CI, 0.22 to 0.33), with no subgroup differences according to class of NSAIDs, ulcer risk, history of previous ulcer disease, Helicobacter pylori infection, or age. To prevent one ulcer complication, 10 high risk patients and 268 moderate risk patients need PPI therapy. Network meta-analysis indicated that the effectiveness of different PPIs in reducing ulcer complications and endoscopic peptic ulcers is generally similar. PPIs significantly reduced gastrointestinal adverse events and the related withdrawals compared to placebo; there is no difference in safety between different PPIs. PPIs are effective and safe in preventing peptic ulcers and complications in a wide spectrum of patients requiring NSAID therapy. There is no major difference in the comparative effectiveness and safety between different PPIs.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

The effects of various diets on glycemic outcomes during pregnancy: A systematic review and network meta-analysis.

PubMed

Ha, Vanessa; Bonner, Ashley J; Jadoo, Jaynendr K; Beyene, Joseph; Anand, Sonia S; de Souza, Russell J

2017-01-01

Evidence to support dietary modifications to improve glycemia during pregnancy is limited, and the benefits of diet beyond limiting gestational weight gain is unclear. Therefore, a systematic review and network meta-analysis of randomized trials was conducted to compare the effects of various common diets, stratified by the addition of gestational weight gain advice, on fasting glucose and insulin, hemoglobin A1c (HbA1c), and homeostatic model assessment for insulin resistance (HOMA-IR) in pregnant women. MEDLINE, EMBASE, Cochrane database, and reference lists of published studies were searched through April 2017. Randomized trials directly comparing two or more diets for ≥2-weeks were eligible. Bayesian network meta-analysis was performed for fasting glucose. Owing to a lack of similar dietary comparisons, a standard pairwise meta-analysis for the other glycemic outcomes was performed. The certainty of the pooled effect estimates was assessed using the GRADE tool. Twenty-one trials (1,865 participants) were included. In general, when given alongside gestational weight gain advice, fasting glucose improved in most diets compared to diets that gave gestational weight gain advice only. However, fasting glucose increased in high unsaturated or monounsaturated fatty acids diets. In the absence of gestational weight gain advice, fasting glucose improved in DASH-style diets compared to standard of care. Although most were non-significant, similar trends were observed for these same diets for the other glycemic outcomes. Dietary comparisons ranged from moderate to very low in quality of evidence. Alongside with gestational weight gain advice, most diets, with the exception of a high unsaturated or a high monounsaturated fatty acid diet, demonstrated a fasting glucose improvement compared with gestational weight gain advice only. When gestational weight gain advice was not given, the DASH-style diet appeared optimal on fasting glucose. However, a small number of trials were identified and most dietary comparisons were underpowered to detect differences in glycemic outcomes. Further studies that are high in quality and adequately powered are needed to confirm these findings. PROSPERO CRD42015026008.

The effects of various diets on glycemic outcomes during pregnancy: A systematic review and network meta-analysis

PubMed Central

Ha, Vanessa; Bonner, Ashley J.; Jadoo, Jaynendr K.; Beyene, Joseph; Anand, Sonia S.

2017-01-01

Aims Evidence to support dietary modifications to improve glycemia during pregnancy is limited, and the benefits of diet beyond limiting gestational weight gain is unclear. Therefore, a systematic review and network meta-analysis of randomized trials was conducted to compare the effects of various common diets, stratified by the addition of gestational weight gain advice, on fasting glucose and insulin, hemoglobin A1c (HbA1c), and homeostatic model assessment for insulin resistance (HOMA-IR) in pregnant women. Methods MEDLINE, EMBASE, Cochrane database, and reference lists of published studies were searched through April 2017. Randomized trials directly comparing two or more diets for ≥2-weeks were eligible. Bayesian network meta-analysis was performed for fasting glucose. Owing to a lack of similar dietary comparisons, a standard pairwise meta-analysis for the other glycemic outcomes was performed. The certainty of the pooled effect estimates was assessed using the GRADE tool. Results Twenty-one trials (1,865 participants) were included. In general, when given alongside gestational weight gain advice, fasting glucose improved in most diets compared to diets that gave gestational weight gain advice only. However, fasting glucose increased in high unsaturated or monounsaturated fatty acids diets. In the absence of gestational weight gain advice, fasting glucose improved in DASH-style diets compared to standard of care. Although most were non-significant, similar trends were observed for these same diets for the other glycemic outcomes. Dietary comparisons ranged from moderate to very low in quality of evidence. Conclusion/Interpretation Alongside with gestational weight gain advice, most diets, with the exception of a high unsaturated or a high monounsaturated fatty acid diet, demonstrated a fasting glucose improvement compared with gestational weight gain advice only. When gestational weight gain advice was not given, the DASH-style diet appeared optimal on fasting glucose. However, a small number of trials were identified and most dietary comparisons were underpowered to detect differences in glycemic outcomes. Further studies that are high in quality and adequately powered are needed to confirm these findings. Registration PROSPERO CRD42015026008 PMID:28771519

Diagnostics for generalized linear hierarchical models in network meta-analysis.

PubMed

Zhao, Hong; Hodges, James S; Carlin, Bradley P

2017-09-01

Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors.

PubMed

Guo, Jingyi; Riebler, Andrea; Rue, Håvard

2017-08-30

In a bivariate meta-analysis, the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Using Bayesian inference is particularly attractive as informative priors that add a small amount of information can stabilise the analysis without overwhelming the data. However, Bayesian analysis is often computationally demanding and the selection of the prior for the covariance matrix of the bivariate structure is crucial with little data. The integrated nested Laplace approximations method provides an efficient solution to the computational issues by avoiding any sampling, but the important question of priors remain. We explore the penalised complexity (PC) prior framework for specifying informative priors for the variance parameters and the correlation parameter. PC priors facilitate model interpretation and hyperparameter specification as expert knowledge can be incorporated intuitively. We conduct a simulation study to compare the properties and behaviour of differently defined PC priors to currently used priors in the field. The simulation study shows that the PC prior seems beneficial for the variance parameters. The use of PC priors for the correlation parameter results in more precise estimates when specified in a sensible neighbourhood around the truth. To investigate the usage of PC priors in practice, we reanalyse a meta-analysis using the telomerase marker for the diagnosis of bladder cancer and compare the results with those obtained by other commonly used modelling approaches. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Comparative safety and effectiveness of cognitive enhancers for Alzheimer's dementia: protocol for a systematic review and individual patient data network meta-analysis

PubMed Central

Veroniki, Areti Angeliki; Straus, Sharon E; Ashoor, Huda M; Hamid, Jemila S; Hemmelgarn, Brenda R; Holroyd-Leduc, Jayna; Majumdar, Sumit R; McAuley, Glenn; Tricco, Andrea C

2016-01-01

Introduction Alzheimer's dementia (AD) is the most common cause of dementia, and several organisations, such as the National Institute for Health and Care Excellence, suggest that management of patients with AD should be tailored to their needs. To date, little research has been conducted on the treatment effect in different subgroups of patients with AD. The aim of this study is to examine the comparative effectiveness and safety of cognitive enhancers for different patient characteristics. Methods and analysis We will update our previous literature search from January 2015 forward, using the same terms and electronic databases (eg, MEDLINE) from our previous review. We will additionally search grey literature and scan the reference lists of the included studies. Randomised clinical trials of any duration conducted at any time comparing cognitive enhancers alone or in any combination against other cognitive enhancers, or placebo in adults with AD will be eligible. The outcomes of interest are cognition according to the Mini-Mental State Examination, and overall serious adverse events. For each outcome and treatment comparison, we will perform a Bayesian hierarchical random-effects meta-analysis combining the individual patient data (IPD) from each eligible study. If the identified treatment comparisons form a connected network diagram, we will perform an IPD network meta-analysis (NMA) to estimate subgroup effects for patients with different characteristics, such as AD severity and sex. We will combine aggregated data from studies that we will not be able to obtain IPD, with the IPD provided by the original authors, in a single model. We will use the PRISMA-IPD and PRISMA-NMA statements to report our findings. Ethics and dissemination The findings of this study will be of interest to stakeholders, including decision makers, guideline developers, clinicians, methodologists and patients, and they will help to improve guidelines for the management of patients with AD. Trial registration number CRD42015023507. PMID:26769792

The effectiveness and safety of treatments used for acute diarrhea and acute gastroenteritis in children: protocol for a systematic review and network meta-analysis.

PubMed

Florez, Ivan D; Al-Khalifah, Reem; Sierra, Javier M; Granados, Claudia M; Yepes-Nuñez, Juan J; Cuello-Garcia, Carlos; Perez-Gaxiola, Giordano; Zea, Adriana M; Hernandez, Gilma N; Veroniki, Areti-Angeliki; Guyatt, Gordon H; Thabane, Lehana

2016-01-20

Acute diarrhea and acute gastroenteritis (AD/AGE) are common among children in low- and middle-income countries (LMIC) and high-income countries (HIC). Supportive therapy including maintaining feeding, prevention of dehydration, and use of oral rehydration solution (ORS), is the mainstay of treatment in all children. Several additional treatments aiming to reduce the episode duration have been compared to placebo, but the differences in effectiveness among them are unknown. We will conduct a systematic review of all randomized controlled trials evaluating the use of zinc, vitamin A, probiotics, prebiotics, synbiotics, racecadotril, smectite, and fermented and lactose-free milk/formula for AD/AGE treatment in children. The primary outcomes are diarrhea duration and mortality. Secondary outcomes are diarrhea lasting 3 or 7 days, stool frequency, treatment failure, hospitalizations, and adverse events. We will search MEDLINE, Ovid EMBASE, CINAHL, the Cochrane Central Register of Controlled Trials (CENTRAL), and LILACS through Ovid, as well as grey literature resources. Two reviewers will independently screen titles and abstracts, review full texts, extract information, and assess the risk of bias (ROB) and the confidence in the estimate (with the grading of recommendations, assessment, development, and evaluation [GRADE] approach). Results will be summarized narratively and statistically. Subgroup analysis according to HIC vs. LMIC, age, nutrition status, and ROB is planned. We will perform a Bayesian network meta-analysis to combine the pooled direct and indirect treatment effect estimates for each outcome, if adequate data is available. This is the first systematic review and network meta-analysis that aims to determine the relative effectiveness of pharmacological and nutritional treatments for reducing the duration of AD/AGE in children. The results will help to reduce the uncertainty of the effectiveness of the interventions, find knowledge gaps, and/or encourage further research for other therapeutic options. PROSPERO registration number: CRD42015023778.

Chemoradiotherapy enhanced the efficacy of radiotherapy in nasopharyngeal carcinoma patients: a network meta-analysis

PubMed Central

He, Jian; Wu, Ping; Tang, Yaoyun; Liu, Sulai; Xie, Chubo; Luo, Shi; Zeng, Junfeng; Xu, Jing; Zhao, Suping

2017-01-01

Object A Bayesian network meta-analysis (NMA) was conducted to estimate the overall survival (OS) and complete response (CR) performance in nasopharyngeal carcinoma (NPC) patients who have been given the treatment of radiotherapy, concurrent chemoradiotherapy (C), adjuvant chemotherapy (A), neoadjuvant chemotherapy (N), concurrent chemoradiotherapy with adjuvant chemotherapy (C+A), concurrent chemoradiotherapy with neoadjuvant chemotherapy (C+N) and neoadjuvant chemotherapy with adjuvant chemotherapy (N+A). Methods Literature search was conducted in electronic databases. Hazard ratios (HRs) accompanied their 95% confidence intervals (95%CIs) or 95% credible intervals (95%CrIs) were applied to measure the relative survival benefit between two comparators. Meanwhile odd ratios (ORs) with their 95% CIs or CrIs were given to present CR data from individual studies. RESULTS Totally 52 qualified studies with 10,081 patients were included in this NMA. In conventional meta-analysis (MA), patients with N+C exhibited an average increase of 9% in the 3-year OS in relation to those with C+A. As for the NMA results, five therapies were associated with a significantly reduced HR when compared with the control group when concerning 5-year OS. C, C+A and N+A also presented a decreased HR compared with A. There was continuity among 1-year, 3-year and 5-year OS status. Cluster analysis suggested that the three chemoradiotherapy appeared to be divided into the most compete group which is located in the upper right corner of the cluster plot. Conclusion In view of survival rate and complete response, the NMA results revealed that C, C+A and C+N showed excellent efficacy. As a result, these 3 therapies were supposed to be considered as the first-line treatment according to this NMA. PMID:28418901

Efficacy of Laparoscopic Nissen Fundoplication vs Transoral Incisionless Fundoplication or Proton Pump Inhibitors in Patients With Gastroesophageal Reflux Disease: A Systematic Review and Network Meta-analysis.

PubMed

Richter, Joel E; Kumar, Ambuj; Lipka, Seth; Miladinovic, Branko; Velanovich, Vic

2018-04-01

The effects of transoral incisionless fundoplication (TIF) and laparoscopic Nissen fundoplication (LNF) have been compared with those of proton pump inhibitors (PPIs) or a sham procedure in patients with gastroesophageal reflux disease (GERD), but there has been no direct comparison of TIF vs LNF. We performed a systematic review and network meta-analysis of randomized controlled trials to compare the relative efficacies of TIF vs LNF in patients with GERD. We searched publication databases and conference abstracts through May 10, 2017 for randomized controlled trials that compared the efficacy of TIF or LNF with that of a sham procedure or PPIs in patients with GERD. We performed a network meta-analysis using Bayesian methods under random-effects multiple treatment comparisons. We assessed ranking probability by surface under the cumulative ranking curve. Our search identified 7 trials comprising 1128 patients. Surface under the cumulative ranking curve ranking indicated TIF had highest probability of increasing patients' health-related quality of life (0.96), followed by LNF (0.66), a sham procedure (0.35), and PPIs (0.042). LNF had the highest probability of increasing percent time at pH <4 (0.99), followed by PPIs (0.64), TIF (0.32), and the sham procedure (0.05). LNF also had the highest probability of increasing LES pressure (0.78), followed by TIF (0.72) and PPIs (0.01). Patients who underwent the sham procedure had the highest probability for persistent esophagitis (0.74), followed by those receiving TIF (0.69), LNF (0.38), and PPIs (0.19). Meta-regression showed a shorter follow-up time as a significant confounder for the outcome of health-related quality of life in studies of TIF. In a systematic review and network meta-analysis of trials of patients with GERD, we found LNF to have the greatest ability to improve physiologic parameters of GERD, including increased LES pressure and decreased percent time pH <4. Although TIF produced the largest increase in health-related quality of life, this could be due to the shorter follow-up time of patients treated with TIF vs LNF or PPIs. TIF is a minimally invasive endoscopic procedure, yet based on evaluation of benefits vs risks, we do not recommend it as a long-term alternative to PPI or LNF treatment of GERD. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Metrics for evaluating performance and uncertainty of Bayesian network models

Treesearch

Bruce G. Marcot

2012-01-01

This paper presents a selected set of existing and new metrics for gauging Bayesian network model performance and uncertainty. Selected existing and new metrics are discussed for conducting model sensitivity analysis (variance reduction, entropy reduction, case file simulation); evaluating scenarios (influence analysis); depicting model complexity (numbers of model...

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Graphical tools for network meta-analysis in STATA.

PubMed

Chaimani, Anna; Higgins, Julian P T; Mavridis, Dimitris; Spyridonos, Panagiota; Salanti, Georgia

2013-01-01

Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.

Graphical Tools for Network Meta-Analysis in STATA

PubMed Central

Chaimani, Anna; Higgins, Julian P. T.; Mavridis, Dimitris; Spyridonos, Panagiota; Salanti, Georgia

2013-01-01

Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results. PMID:24098547

Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain.

PubMed

Saramago, Pedro; Woods, Beth; Weatherly, Helen; Manca, Andrea; Sculpher, Mark; Khan, Kamran; Vickers, Andrew J; MacPherson, Hugh

2016-10-06

Network meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes. This paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises. Individual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance. The network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

Comparative efficacy and safety of first-line antiretroviral therapy for the treatment of HIV infection: a systematic review and network meta-analysis.

PubMed

Kanters, Steve; Vitoria, Marco; Doherty, Meg; Socias, Maria Eugenia; Ford, Nathan; Forrest, Jamie I; Popoff, Evan; Bansback, Nick; Nsanzimana, Sabin; Thorlund, Kristian; Mills, Edward J

2016-11-01

New antiretroviral therapy (ART) regimens for HIV could improve clinical outcomes for patients. To inform global guidelines, we aimed to assess the comparative effectiveness of recommended ART regimens for HIV in ART-naive patients. For this systematic review and network meta-analysis, we searched for randomised clinical trials published up to July 5, 2015, comparing recommended antiretroviral regimens in treatment-naive adults and adolescents (aged 12 years or older) with HIV. We extracted data on trial and patient characteristics, and the following primary outcomes: viral suppression, mortality, AIDS defining illnesses, discontinuations, discontinuations due to adverse events, and serious adverse events. We synthesised data using network meta-analyses in a Bayesian framework and included older treatments, such as indinavir, to serve as connecting nodes. We defined network nodes in terms of specific antivirals rather than specific ART regimens. We categorised backbone regimens and adjusted for them through group-specific meta-regression. We used the GRADE framework to interpret the strength of inference. We identified 5865 citations through database searches and other sources, of which, 126 articles related to 71 unique trials were included in the network analysis, including 34 032 patients randomly assigned to 161 treatment groups. For viral suppression at 48 weeks, compared with efavirenz, the odds ratio (OR) for viral suppression was 1·87 (95% credible interval [CrI] 1·34-2·64) with dolutegravir and 1·40 (1·02-1·96) with raltegravir; with respect to viral suppression, low-dose efavirenz was similar to all other treatments. Both low-dose efavirenz and integrase strand transfer inhibitors tended to be protective of discontinuations due to adverse events relative to normal-dose efavirenz. The most protective effect relative to efavirenz in network meta-analyses was that of dolutegravir (OR 0·26, 95% CrI 0·14-0·47), followed by low-dose efavirenz (0·39, 0·16-0·92). Owing to insufficient data, we could make no conclusions about serious adverse events. Low event rates also limited the quality of evidence with regard to mortality and AIDS defining illnesses. The efficacy and safety of ART has substantially improved with the introduction of newer drug classes of antiretrovirals that are now available to patients and HIV care providers. Their improved tolerance could be part of a larger solution to improve retention, which is a challenge, particularly in low-income and middle-income country settings. The World Health Organization. Copyright © 2016 Elsevier Ltd. All rights reserved.

Network Meta-Analysis of Erlotinib, Gefitinib, Afatinib and Icotinib in Patients with Advanced Non-Small-Cell Lung Cancer Harboring EGFR Mutations

PubMed Central

Zhao, Yuanyuan; Yang, Yunpeng; Hu, Zhihuang; Xue, Cong; Zhang, Jing; Zhang, Jianwei; Ma, Yuxiang; Zhou, Ting; Yan, Yue; Hou, Xue; Qin, Tao; Dinglin, Xiaoxiao; Tian, Ying; Huang, Peiyu; Huang, Yan; Zhao, Hongyun; Zhang, Li

2014-01-01

Background Several EGFR-tyrosine kinase inhibitors (EGFR-TKIs) including erlotinib, gefitinib, afatinib and icotinib are currently available as treatment for patients with advanced non-small-cell lung cancer (NSCLC) who harbor EGFR mutations. However, no head to head trials between these TKIs in mutated populations have been reported, which provides room for indirect and integrated comparisons. Methods We searched electronic databases for eligible literatures. Pooled data on objective response rate (ORR), progression free survival (PFS), overall survival (OS) were calculated. Appropriate networks for different outcomes were established to incorporate all evidences. Multiple-treatments comparisons (MTCs) based on Bayesian network integrated the efficacy and specific toxicities of all included treatments. Results Twelve phase III RCTs that investigated EGFR-TKIs involving 1821 participants with EGFR mutation were included. For mutant patients, the weighted pooled ORR and 1-year PFS of EGFR-TKIs were significant superior to that of standard chemotherapy (ORR: 66.6% vs. 30.9%, OR 5.46, 95%CI 3.59 to 8.30, P<0.00001; 1-year PFS: 42.9% vs. 9.7%, OR 7.83, 95%CI 4.50 to 13.61; P<0.00001) through direct meta-analysis. In the network meta-analyses, no statistically significant differences in efficacy were found between these four TKIs with respect to all outcome measures. Trend analyses of rank probabilities revealed that the cumulative probabilities of being the most efficacious treatments were (ORR, 1-year PFS, 1-year OS, 2-year OS): erlotinib (51%, 38%, 14%, 19%), gefitinib (1%, 6%, 5%, 16%), afatinib (29%, 27%, 30%, 27%) and icotinib (19%, 29%, NA, NA), respectively. However, afatinib and erlotinib showed significant severer rash and diarrhea compared with gefitinib and icotinib. Conclusions The current study indicated that erlotinib, gefitinib, afatinib and icotinib shared equivalent efficacy but presented different efficacy-toxicity pattern for EGFR-mutated patients. Erlotinib and afatinib revealed potentially better efficacy but significant higher toxicities compared with gefitinib and icotinib. PMID:24533047

Network meta-analysis of erlotinib, gefitinib, afatinib and icotinib in patients with advanced non-small-cell lung cancer harboring EGFR mutations.

PubMed

Liang, Wenhua; Wu, Xuan; Fang, Wenfeng; Zhao, Yuanyuan; Yang, Yunpeng; Hu, Zhihuang; Xue, Cong; Zhang, Jing; Zhang, Jianwei; Ma, Yuxiang; Zhou, Ting; Yan, Yue; Hou, Xue; Qin, Tao; Dinglin, Xiaoxiao; Tian, Ying; Huang, Peiyu; Huang, Yan; Zhao, Hongyun; Zhang, Li

2014-01-01

Several EGFR-tyrosine kinase inhibitors (EGFR-TKIs) including erlotinib, gefitinib, afatinib and icotinib are currently available as treatment for patients with advanced non-small-cell lung cancer (NSCLC) who harbor EGFR mutations. However, no head to head trials between these TKIs in mutated populations have been reported, which provides room for indirect and integrated comparisons. We searched electronic databases for eligible literatures. Pooled data on objective response rate (ORR), progression free survival (PFS), overall survival (OS) were calculated. Appropriate networks for different outcomes were established to incorporate all evidences. Multiple-treatments comparisons (MTCs) based on Bayesian network integrated the efficacy and specific toxicities of all included treatments. Twelve phase III RCTs that investigated EGFR-TKIs involving 1821 participants with EGFR mutation were included. For mutant patients, the weighted pooled ORR and 1-year PFS of EGFR-TKIs were significant superior to that of standard chemotherapy (ORR: 66.6% vs. 30.9%, OR 5.46, 95%CI 3.59 to 8.30, P<0.00001; 1-year PFS: 42.9% vs. 9.7%, OR 7.83, 95%CI 4.50 to 13.61; P<0.00001) through direct meta-analysis. In the network meta-analyses, no statistically significant differences in efficacy were found between these four TKIs with respect to all outcome measures. Trend analyses of rank probabilities revealed that the cumulative probabilities of being the most efficacious treatments were (ORR, 1-year PFS, 1-year OS, 2-year OS): erlotinib (51%, 38%, 14%, 19%), gefitinib (1%, 6%, 5%, 16%), afatinib (29%, 27%, 30%, 27%) and icotinib (19%, 29%, NA, NA), respectively. However, afatinib and erlotinib showed significant severer rash and diarrhea compared with gefitinib and icotinib. The current study indicated that erlotinib, gefitinib, afatinib and icotinib shared equivalent efficacy but presented different efficacy-toxicity pattern for EGFR-mutated patients. Erlotinib and afatinib revealed potentially better efficacy but significant higher toxicities compared with gefitinib and icotinib.

Bayesian mixture analysis for metagenomic community profiling.

PubMed

Morfopoulou, Sofia; Plagnol, Vincent

2015-09-15

Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix sofia.morfopoulou.10@ucl.ac.uk Supplementary data are available at Bionformatics online. © The Author 2015. Published by Oxford University Press.

Bayesian Community Detection in the Space of Group-Level Functional Differences

PubMed Central

Venkataraman, Archana; Yang, Daniel Y.-J.; Pelphrey, Kevin A.; Duncan, James S.

2017-01-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder. PMID:26955022

Bayesian Community Detection in the Space of Group-Level Functional Differences.

PubMed

Venkataraman, Archana; Yang, Daniel Y-J; Pelphrey, Kevin A; Duncan, James S

2016-08-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Basal Insulin Regimens for Adults with Type 1 Diabetes Mellitus: A Systematic Review and Network Meta-Analysis.

PubMed

Dawoud, Dalia; O'Mahony, Rachel; Wonderling, David; Cobb, Jill; Higgins, Bernard; Amiel, Stephanie A

2018-02-01

To assess the relative efficacy and safety of basal insulin regimens in adults with type 1 diabetes mellitus (T1DM). A systematic review and Bayesian network meta-analysis (NMA) of randomized controlled trials comparing two or more basal insulin regimens were conducted. The following basal insulin regimens were included: Neutral Protamine Hagedorn (iNPH) (once [od], twice [bid], and four times daily [qid]), insulin detemir (iDet) (od and bid), insulin glargine 100 IU (iGlarg) (od), and insulin degludec (iDegl) (od). We searched the following databases: MEDLINE via OVID, Embase via OVID, and the Cochrane Library (Wiley). Study quality was appraised using Cochrane risk-of-bias checklist for randomized controlled trials. Two outcomes (change in hemoglobin A 1c [HbA 1c ] and rate of severe/major hypoglycemia [SH]) were analyzed. Network inconsistency was assessed using Bucher and chi-square tests. Thirty studies met the eligibility criteria. Twenty-five were included in the HbA 1c network and 16 in the SH network. All studies were of moderate quality. No network inconsistency was evident in the HbA 1c network. Of the seven regimens of interest, iDet (bid) had the highest probability of being best (mean change in HbA 1c -0.48; 95% credible interval -0.69 to -0.29). In contrast, the SH network demonstrated both considerable uncertainty and significant network inconsistency (χ 2 test, P = 0.003). Of the specified frequency regimens, iDet (bid) had the highest probability of being the best basal insulin regimen in terms of reduction in HbA 1c . Ranking of the regimens in terms of the SH rate was highly uncertain and no clear conclusion could be made. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

A comparison of effectiveness among frequent treatments of recurrent spontaneous abortion: A Bayesian network meta-analysis.

PubMed

Lv, Sha; Yu, Jing; Xu, Xiaoxiao

2018-04-30

A comprehensive network meta-analysis was designed to clarify contradictions and offer valuable clinical guidance in the treatment of recurrent spontaneous abortion (RSA). The included clinical trials were selected from the relevant medical journal databases and screened. Treatments were ranked by the surface under the cumulative ranking curve. Heat plots were constructed to analyze the inconsistency between direct data and network results, and adjusted funnel plots were constructed to assess publication bias. Forty-nine randomized controlled trials involving a total of 8496 RSA patients were selected. With placebo as control, corticosteroid plus low dose aspirin (LDA) plus unfractionated heparin (UFH), granulocyte colony-stimulating factor (G-CSF) alone, and LDA plus low molecular weight heparin (LMWH) all demonstrated effectiveness in increasing successful live birth rates and reducing the incidences of miscarriage. However, no treatment was demonstrably superior to placebo in terms of pregnancy success. For all 3 endpoints (live birth, abortion and success pregnancy), the adjusted funnel plots were symmetric to zero and indicated no publication bias. In terms of live birth and abortion rates, no treatment outperformed placebo in patients with antiphospholipid syndrome. In consideration of live birth and abortion rates, corticosteroid plus LDA plus UFH appeared to be the optimum treatment strategy; G-CSF was second, followed by LDA with LMWH, LDA plus LMWH plus intravenous immunoglobulin, corticosteroid with LDA and others. Subgroup analysis demonstrated no benefit of antithrombotic therapy in patients with antiphospholipid syndrome. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Efficacy and Acceptability of Glycemic Control of Glucagon-Like Peptide-1 Receptor Agonists among Type 2 Diabetes: A Systematic Review and Network Meta-Analysis.

PubMed

Li, Zhixia; Zhang, Yuan; Quan, Xiaochi; Yang, Zhirong; Zeng, Xiantao; Ji, Linong; Sun, Feng; Zhan, Siyan

2016-01-01

To synthesize current evidence of the impact of Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on hypoglycemia, treatment discontinuation and glycemic level in patients with type 2 diabetes. Systematic review and network meta-analysis. Literature search (Medline, Embase, the Cochrane library), website of clinical trial, bibliographies of published systematic reviews. Randomized controlled trials with available data comparing GLP-1 RAs with placebo or traditional anti-diabetic drugs in patients with type 2 diabetes. Traditional pairwise meta-analyses within DerSimonian-Laird random effects model and network meta-analysis within a Bayesian framework were performed to calculate odds ratios for the incidence of hypoglycemia, treatment discontinuation, HbA1c<7.0% and HbA1c<6.5%. Ranking probabilities for all treatments were estimated to obtain a treatment hierarchy using the surface under the cumulative ranking curve (SUCRA) and mean ranks. 78 trials with 13 treatments were included. Overall, all GLP-1 RAs except for albiglutide increased the risk of hypoglycemia when compared to placebo. Reduction in the incidence of hypoglycemia was found for all GLP-1 RAs versus insulin (except for dulaglutide) and sulphonylureas. For the incidence of treatment discontinuation, increase was found for exenatide, liraglutide, lixisenatide and taspoglutide versus placebo, insulin and sitagliptin. For glycemic level, decrease was found for all GLP-1 RAs versus placebo. Dulaglutide, exenatide long-acting release (exe_lar), liraglutide and taspoglutide had significant lowering effect when compared with sitagliptin (HbA1c<7.0%) and insulin (HbA1c<6.5%). Finally, according to SUCRAs, placebo, thiazolidinediones and albiglutide had the best decrease effect on hypoglycemia; sulphanylureas, sitagliptin and insulin decrease the incidence of treatment discontinuation most; exe_lar and dulaglutide had the highest impact on glycemic level among 13 treatments. Among 13 treatments, GLP-1 RAs had a significant reduction with glycemic level but a slight increase effect on hypoglycemia and treatment discontinuation. While albiglutide had the best decrease effect on hypoglycemia and treatment discontinuation among all GLP-1 RAs. However, further evidence is necessary for more conclusive inferences on mechanisms underlying the rise in hypoglycemia.

Probiotics in Helicobacter pylori eradication therapy: Systematic review and network meta-analysis.

PubMed

Wang, Fan; Feng, Juerong; Chen, Pengfei; Liu, Xiaoping; Ma, Minxing; Zhou, Rui; Chang, Ying; Liu, Jing; Li, Jin; Zhao, Qiu

2017-09-01

Several probiotics were effective in the eradication treatment for Helicobacter pylori (Hp), but their comparative efficacy was unknown. To compare the efficacy of different probiotics when supplemented in Hp eradication therapy. A comprehensive search was conducted to identify all relevant studies in multiple databases and previous meta-analyses. Bayesian network meta-analysis was performed to combine direct and indirect evidence and estimate the relative effects. One hundred and forty studies (44 English and 96 Chinese) were identified with a total of 20,215 patients, and more than 10 probiotic strategies were supplemented in Hp eradication therapy. The rates of eradication and adverse events were 84.1 and 14.4% in probiotic group, while 70.5 and 30.1% in the control group. In general, supplementary probiotics were effective in improving the efficacy of Hp eradication and decreasing the incidence of adverse events, despite of few ineffective subtypes. In triple eradication therapy, there was no significant difference among the effective probiotics, and combined probiotics did not show a better efficacy and tolerance than single use. In triple therapy of 7 days and 14 days, Lactobacillus acidopilus was a slightly better choice, while Saccharomyces boulardii was more applicable for 10-day triple therapy. Compared to placebo, most probiotic strategies were effective when supplemented in Hp eradication therapy. In triple eradication therapy, no probiotic showed a superior efficacy to the others. Compared to single use, combined probiotics could not improve the efficacy or tolerance significantly. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Cost-Effectiveness Analysis of Systemic Therapies in Advanced Pancreatic Cancer in the Canadian Health Care System.

PubMed

Coyle, Doug; Ko, Yoo-Joung; Coyle, Kathryn; Saluja, Ronak; Shah, Keya; Lien, Kelly; Lam, Henry; Chan, Kelvin K W

2017-04-01

To assess the cost-effectiveness of gemcitabine (G), G + 5-fluorouracil, G + capecitabine, G + cisplatin, G + oxaliplatin, G + erlotinib, G + nab-paclitaxel (GnP), and FOLFIRINOX in the treatment of advanced pancreatic cancer from a Canadian public health payer's perspective, using data from a recently published Bayesian network meta-analysis. Analysis was conducted through a three-state Markov model and used data on the progression of disease with treatment from the gemcitabine arms of randomized controlled trials combined with estimates from the network meta-analysis for the newer regimens. Estimates of health care costs were obtained from local providers, and utilities were derived from the literature. The model estimates the effect of treatment regimens on costs and quality-adjusted life-years (QALYs) discounted at 5% per annum. At a willingness-to-pay (WTP) threshold of greater than $30,666 per QALY, FOLFIRINOX would be the most optimal regimen. For a WTP threshold of $50,000 per QALY, the probability that FOLFIRINOX would be optimal was 57.8%. There was no price reduction for nab-paclitaxel when GnP was optimal. From a Canadian public health payer's perspective at the present time and drug prices, FOLFIRINOX is the optimal regimen on the basis of the cost-effectiveness criterion. GnP is not cost-effective regardless of the WTP threshold. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Comparative efficacy and safety of antipsychotics in the treatment of schizophrenia: a network meta-analysis in a Japanese population.

PubMed

Kishi, Taro; Ikuta, Toshikazu; Matsunaga, Shinji; Matsuda, Yuki; Oya, Kazuto; Iwata, Nakao

2017-01-01

The relative efficacy and tolerability of antipsychotics for schizophrenia are considerably well studied. This study aimed to examine whether previous findings could be replicated in a genetically distinct and homogenous group (ie, Japanese patients with schizophrenia) and whether previous findings could be extended to a broader range of antipsychotics with previously unclear relative efficacy and tolerability. Bayesian network meta-analysis was performed in which randomized trials comparing any of the following interventions were included: second-generation antipsychotics, haloperidol, or placebo. The primary outcomes for efficacy and acceptability were the response rate and all-cause discontinuation. The secondary outcomes included the improvement of Positive and Negative Syndrome Scale scores, discontinuation because of adverse events, and individual adverse events. Eighteen relevant studies were identified (total n=3,446; aripiprazole =267, blonanserin =285, clozapine =47, clocapramine =295, haloperidol =857, mosapramine =493, olanzapine =179, paliperidone =136, perospirone =146, placebo =138, quetiapine =212, and risperidone =338; mean study duration =8.33±1.41 weeks). In primary outcomes, olanzapine and paliperidone showed efficacy than placebo, and olanzapine and paliperidone showed superior acceptability compared with placebo. There were differences in the incidences of individual adverse events (the best antipsychotic: extrapyramidal symptoms = olanzapine, hyperprolactinemia- related symptoms = quetiapine, sedation = paliperidone, and weight change = blonanserin) among antipsychotics. Although the current analysis exclusively included Japanese patients with schizophrenia, no remarkable differences were observed in efficacy and safety compared with previous meta-analyses. Diverse hierarchies in safety outcomes also support the implication that individual risk expectations for adverse events can guide clinical decisions. However, the sample size was relatively limited. Additional efficacy and safety data are required to fully obtain a conclusive understanding.

Comparative efficacy and safety of antipsychotics in the treatment of schizophrenia: a network meta-analysis in a Japanese population

PubMed Central

Kishi, Taro; Ikuta, Toshikazu; Matsunaga, Shinji; Matsuda, Yuki; Oya, Kazuto; Iwata, Nakao

2017-01-01

Background The relative efficacy and tolerability of antipsychotics for schizophrenia are considerably well studied. This study aimed to examine whether previous findings could be replicated in a genetically distinct and homogenous group (ie, Japanese patients with schizophrenia) and whether previous findings could be extended to a broader range of antipsychotics with previously unclear relative efficacy and tolerability. Methods Bayesian network meta-analysis was performed in which randomized trials comparing any of the following interventions were included: second-generation antipsychotics, haloperidol, or placebo. The primary outcomes for efficacy and acceptability were the response rate and all-cause discontinuation. The secondary outcomes included the improvement of Positive and Negative Syndrome Scale scores, discontinuation because of adverse events, and individual adverse events. Results Eighteen relevant studies were identified (total n=3,446; aripiprazole =267, blonanserin =285, clozapine =47, clocapramine =295, haloperidol =857, mosapramine =493, olanzapine =179, paliperidone =136, perospirone =146, placebo =138, quetiapine =212, and risperidone =338; mean study duration =8.33±1.41 weeks). In primary outcomes, olanzapine and paliperidone showed efficacy than placebo, and olanzapine and paliperidone showed superior acceptability compared with placebo. There were differences in the incidences of individual adverse events (the best antipsychotic: extrapyramidal symptoms = olanzapine, hyperprolactinemia- related symptoms = quetiapine, sedation = paliperidone, and weight change = blonanserin) among antipsychotics. Conclusion Although the current analysis exclusively included Japanese patients with schizophrenia, no remarkable differences were observed in efficacy and safety compared with previous meta-analyses. Diverse hierarchies in safety outcomes also support the implication that individual risk expectations for adverse events can guide clinical decisions. However, the sample size was relatively limited. Additional efficacy and safety data are required to fully obtain a conclusive understanding. PMID:28553116

Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

ERIC Educational Resources Information Center

Zhang, Zhidong

2016-01-01

This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

Using Discrete Loss Functions and Weighted Kappa for Classification: An Illustration Based on Bayesian Network Analysis

ERIC Educational Resources Information Center

Zwick, Rebecca; Lenaburg, Lubella

2009-01-01

In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…

Testing the Storm et al. (2010) meta-analysis using Bayesian and frequentist approaches: reply to Rouder et al. (2013).

PubMed

Storm, Lance; Tressoldi, Patrizio E; Utts, Jessica

2013-01-01

Rouder, Morey, and Province (2013) stated that (a) the evidence-based case for psi in Storm, Tressoldi, and Di Risio's (2010) meta-analysis is supported only by a number of studies that used manual randomization, and (b) when these studies are excluded so that only investigations using automatic randomization are evaluated (and some additional studies previously omitted by Storm et al., 2010, are included), the evidence for psi is "unpersuasive." Rouder et al. used a Bayesian approach, and we adopted the same methodology, finding that our case is upheld. Because of recent updates and corrections, we reassessed the free-response databases of Storm et al. using a frequentist approach. We discuss and critique the assumptions and findings of Rouder et al. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.

PubMed

Caillet, Pascal; Klemm, Sarah; Ducher, Michel; Aussem, Alexandre; Schott, Anne-Marie

2015-01-01

Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach. EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences. Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density. Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

[Reliability theory based on quality risk network analysis for Chinese medicine injection].

PubMed

Li, Zheng; Kang, Li-Yuan; Fan, Xiao-Hui

2014-08-01

A new risk analysis method based upon reliability theory was introduced in this paper for the quality risk management of Chinese medicine injection manufacturing plants. The risk events including both cause and effect ones were derived in the framework as nodes with a Bayesian network analysis approach. It thus transforms the risk analysis results from failure mode and effect analysis (FMEA) into a Bayesian network platform. With its structure and parameters determined, the network can be used to evaluate the system reliability quantitatively with probabilistic analytical appraoches. Using network analysis tools such as GeNie and AgenaRisk, we are able to find the nodes that are most critical to influence the system reliability. The importance of each node to the system can be quantitatively evaluated by calculating the effect of the node on the overall risk, and minimization plan can be determined accordingly to reduce their influences and improve the system reliability. Using the Shengmai injection manufacturing plant of SZYY Ltd as a user case, we analyzed the quality risk with both static FMEA analysis and dynamic Bayesian Network analysis. The potential risk factors for the quality of Shengmai injection manufacturing were identified with the network analysis platform. Quality assurance actions were further defined to reduce the risk and improve the product quality.

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review.

PubMed

Weir, Christopher J; Butcher, Isabella; Assi, Valentina; Lewis, Stephanie C; Murray, Gordon D; Langhorne, Peter; Brady, Marian C

2018-03-07

Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses.

Meta-analysis of few small studies in orphan diseases.

PubMed

Friede, Tim; Röver, Christian; Wandel, Simon; Neuenschwander, Beat

2017-03-01

Meta-analyses in orphan diseases and small populations generally face particular problems, including small numbers of studies, small study sizes and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children, we investigate the properties of a range of commonly used frequentist and Bayesian procedures in simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random-effects meta-analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between-trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp-Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between-trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half-normal priors (with a scale of 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC).

PubMed

Kwon, Deukwoo; Reis, Isildinha M

2015-08-12

When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed.

Locally advanced squamous cell carcinoma of the head and neck: A systematic review and Bayesian network meta-analysis of the currently available treatment options.

PubMed

Iocca, Oreste; Farcomeni, Alessio; Di Rocco, Arianna; Di Maio, Pasquale; Golusinski, Paweł; Pardiñas López, Simón; Savo, Alfredo; Pellini, Raul; Spriano, Giuseppe

2018-05-01

There are still many unresolved questions in the management of locally advanced Head and Neck Cancer (HNC). Many chemotherapeutic drugs and radiotherapy fractionation schemes are available and not all have been evaluated in head-to-head clinical trials. This systematic review and Bayesian network meta-analysis aims to compare the available treatment strategies and chemotherapeutic options for locally advanced HNC. We performed a search on bibliography databases, trials registries and meetings proceedings for published and unpublished randomized trials from January 1st 2000 to December 1st 2017. Trials had to compare systemic interventions and radiotherapy (RT) approaches for locally advanced, non-metastatic HNC. Trials recruiting patients whose surgery was the first treatment option, sample size less than 20 per arm or that did not use randomization for treatment allocation were excluded from the analysis. Summary estimates on Overall survival (OS), Progression-free survival (PFS) and toxicity outcomes (grade 3-4 mucositis and neutropenia) were extracted from the included studies on a predefined database sheet. Bias was assessed through the Chocrane risk of bias assessment tool. We performed a set of pair-wise meta-analyses using a random effect model. We also performed a random effect network meta-analysis under a Bayesian framework. From the 57 included trials, including 15,723 patients, was possible to conduct analysis on 26 treatments for OS, 22 treatments for PFS and 10 treatments for toxicity. In terms of OS Concurrent chemoradiotherapy (CCRT) with cisplatin (HR 0.70, 95% CrI [credible interval] 0.62-0.78) and cetuximab on top of CCRT (HR 0.7, 95% CrI 0.5-0.97) are clearly superior to conventional RT alone. Induction chemotherapy (IC) with cisplatin and fluorouracil (HR 0.74, 95% CrI 0.52-0.95), IC with docetaxel, cisplatin, fluorouracil (HR 0.55, 95% CrI 0.54-0.89) and IC with paclitaxel, cisplatin, fluorouracil (HR 0.55, 95% CrI 0.34-0.89) before CCRT are all superior to conventional RT. CCRT with cisplatin is also superior to altered fractionation RT (HR 0.74, 95% CrI 0.64-0.84). Altered fractionation RT is not superior to conventional RT (HR 0.95, 95% CrI 0.85-1.06). Regarding PFS, CCRT with cisplatin (HR 0.72, 95% CrI 0.63-0.83), cisplatin and fluorouracil (HR 0.67, 95% CrI 0.5-0.88), carboplatin (HR 0.63, 95% CrI 0.46-0.87), carboplatin and fluorouracil (HR 0.75, 95% CrI 0.56-1), IC with cisplatin and fluorouracil (HR 0.59, 95% CrI 0.45-0.78), IC with docetaxel, cisplatin and fluorouracil (HR 0.53, 95% CrI 0.41-0.68) and IC with paclitaxel, cisplatin and fluorouracil (HR 0.59, 95% CrI 0.35-0.99) are superior to conventional RT and altered fractionation RT. IC with docetaxel, cisplatin and fluorouracil shows a significant superiority against CCRT with cisplatin (HR 0.73 95% CrI 0.58-0.92). Altered fractionation RT is not superior to conventional RT (HR 0.91, 95% CrI 0.81-1.02). Altered fractionation increases the risk of developing grade 3-4 mucositis compared to conventional RT (OR 3.74 95% 1.64-8.67) INTERPRETATION: CCRT with cisplatin remains the gold standard of treatment. Taxane based IC regimens may have a impact on locally advanced disease. Altered fractionation RT is inferior to CCRT and also does not seem to be meaningfully better than conventionally fractionated RT alone. Its role in locally advanced disease should be reevaluated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Classifying emotion in Twitter using Bayesian network

NASA Astrophysics Data System (ADS)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks.

PubMed

Zhang, Jinfen; Teixeira, Ângelo P; Guedes Soares, C; Yan, Xinping; Liu, Kezhong

2016-06-01

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port. © 2016 Society for Risk Analysis.

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was tomore » establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.« less

An investigation of the impact of using different methods for network meta-analysis: a protocol for an empirical evaluation.

PubMed

Karahalios, Amalia Emily; Salanti, Georgia; Turner, Simon L; Herbison, G Peter; White, Ian R; Veroniki, Areti Angeliki; Nikolakopoulou, Adriani; Mckenzie, Joanne E

2017-06-24

Network meta-analysis, a method to synthesise evidence from multiple treatments, has increased in popularity in the past decade. Two broad approaches are available to synthesise data across networks, namely, arm- and contrast-synthesis models, with a range of models that can be fitted within each. There has been recent debate about the validity of the arm-synthesis models, but to date, there has been limited empirical evaluation comparing results using the methods applied to a large number of networks. We aim to address this gap through the re-analysis of a large cohort of published networks of interventions using a range of network meta-analysis methods. We will include a subset of networks from a database of network meta-analyses of randomised trials that have been identified and curated from the published literature. The subset of networks will include those where the primary outcome is binary, the number of events and participants are reported for each direct comparison, and there is no evidence of inconsistency in the network. We will re-analyse the networks using three contrast-synthesis methods and two arm-synthesis methods. We will compare the estimated treatment effects, their standard errors, treatment hierarchy based on the surface under the cumulative ranking (SUCRA) curve, the SUCRA value, and the between-trial heterogeneity variance across the network meta-analysis methods. We will investigate whether differences in the results are affected by network characteristics and baseline risk. The results of this study will inform whether, in practice, the choice of network meta-analysis method matters, and if it does, in what situations differences in the results between methods might arise. The results from this research might also inform future simulation studies.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Intuitive Logic Revisited: New Data and a Bayesian Mixed Model Meta-Analysis

PubMed Central

Singmann, Henrik; Klauer, Karl Christoph; Kellen, David

2014-01-01

Recent research on syllogistic reasoning suggests that the logical status (valid vs. invalid) of even difficult syllogisms can be intuitively detected via differences in conceptual fluency between logically valid and invalid syllogisms when participants are asked to rate how much they like a conclusion following from a syllogism (Morsanyi & Handley, 2012). These claims of an intuitive logic are at odds with most theories on syllogistic reasoning which posit that detecting the logical status of difficult syllogisms requires effortful and deliberate cognitive processes. We present new data replicating the effects reported by Morsanyi and Handley, but show that this effect is eliminated when controlling for a possible confound in terms of conclusion content. Additionally, we reanalyze three studies () without this confound with a Bayesian mixed model meta-analysis (i.e., controlling for participant and item effects) which provides evidence for the null-hypothesis and against Morsanyi and Handley's claim. PMID:24755777

Minimally Invasive Extracorporeal Circulation Circuit Is Not Inferior to Off-Pump Coronary Artery Bypass Grafting: Meta-Analysis Using the Bayesian Method.

PubMed

Winkler, Bernhard; Heinisch, Paul P; Gahl, Brigitta; Aghlmandi, Soheila; Jenni, Hans Jörg; Carrel, Thierry P

2017-01-01

The pathophysiologic side effects of cardiopulmonary bypass have already been identified. Minimally invasive extracorporeal circulation technologies (MiECT) and off-pump coronary artery bypass graft surgery (OPCABG) aim to reduce these problems. This meta-analysis provides a comparison of MiECT and OPCABG in randomized and observational studies. A fully probabilistic, Bayesian approach of primary and secondary endpoints was conducted. MiECT does not give inferior results when compared with OPCABG. However, there is a trend to borderline significantly higher blood loss in this group in randomized controlled trials. The question whether MiECT is equivalent to OPCABG can be answered with the affirmative, but long-term follow-up data are needed to detect any advantage over time. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Which hemostatic device in thyroid surgery? A network meta-analysis of surgical technologies.

PubMed

Garas, George; Okabayashi, Koji; Ashrafian, Hutan; Shetty, Kunal; Palazzo, Fausto; Tolley, Neil; Darzi, Ara; Athanasiou, Thanos; Zacharakis, Emmanouil

2013-09-01

Energy-based hemostatic devices are increasingly being used in thyroid surgery. However, there are several limitations with regard to the existing evidence and a paucity of guidelines on the subject. The goal of this review is to employ the novel evidence synthesis technique of a network meta-analysis to assess the comparative effectiveness of surgical technologies in thyroid surgery and contribute to enhanced governance in the field of thyroid surgery. Articles published between January 2000 and June 2012 were identified from Embase, Medline, Cochrane Library, and PubMed databases. Randomized controlled trials of any size comparing the use of ultrasonic coagulation (harmonic scalpel) or Ligasure either head-to-head or against the "clamp-and-tie" technique were included. Two reviewers independently critically appraised and extracted the data from each study. The number of patients who experienced postoperative events was extracted in dichotomous format or continuous outcomes. Odds ratios were calculated by a Bayesian network meta-analysis, and metaregression was used for pair-wise comparisons. Indirect and direct comparisons were performed and inconsistency was assessed. Thirty-five randomized controlled trials with 2856 patients were included. Ultrasonic coagulation ranked first (followed by Ligasure and then clamp-and-tie) with the lowest risk of postoperative hypoparathyroidism (odds ratio 1.43 [95% confidence interval (CI) 0.77-2.67] and 0.70 [CI 0.43-1.13], ultrasonic coagulation vs. Ligasure and ultrasonic coagulation vs. clamp-and-tie, respectively), least blood loss (-0.25 [CI -0.84 to -0.35] and -1.22 [CI -1.85 to -0.59]), and drain output (0.28 [CI -0.35 to -0.91] and -0.36 [CI -0.70 to -0.03]). From a health technology viewpoint, ultrasonic coagulation was associated with the shortest operative time (-0.66 [CI -1.17 to -0.14] and -1.29 [CI -1.59 to -1.00]) and hospital stay (-0.28 [CI -0.78 to 0.22] and -0.56 [CI -1.28 to 0.15]). The only exception occurs with the clinically important complication of recurrent laryngeal nerve paralysis, where the reverse trend applies (1.36 [CI 0.25-7.46] and 1.74 [CI 0.94-3.26]). The comparative effectiveness of ultrasonic coagulation in thyroid surgery outcomes seems superior to other techniques with the exception of recurrent laryngeal nerve injury. This network meta-analysis, one of a handful in a surgical field, offers preliminary and robust evidence to guide clinical decisions and policy makers to adopt safer thyroid operations.

An integrative computational analysis provides evidence for FBN1-associated network deregulation in trisomy 21.

PubMed

Vilardell, Mireia; Civit, Sergi; Herwig, Ralf

2013-08-15

Although approximately 50% of Down Syndrome (DS) patients have heart abnormalities, they exhibit an overprotection against cardiac abnormalities related with the connective tissue, for example a lower risk of coronary artery disease. A recent study reported a case of a person affected by DS who carried mutations in FBN1, the gene causative for a connective tissue disorder called Marfan Syndrome (MFS). The fact that the person did not have any cardiac alterations suggested compensation effects due to DS. This observation is supported by a previous DS meta-analysis at the molecular level where we have found an overall upregulation of FBN1 (which is usually downregulated in MFS). Additionally, that result was cross-validated with independent expression data from DS heart tissue. The aim of this work is to elucidate the role of FBN1 in DS and to establish a molecular link to MFS and MFS-related syndromes using a computational approach. To reach that, we conducted different analytical approaches over two DS studies (our previous meta-analysis and independent expression data from DS heart tissue) and revealed expression alterations in the FBN1 interaction network, in FBN1 co-expressed genes and FBN1-related pathways. After merging the significant results from different datasets with a Bayesian approach, we prioritized 85 genes that were able to distinguish control from DS cases. We further found evidence for several of these genes (47%), such as FBN1, DCN, and COL1A2, being dysregulated in MFS and MFS-related diseases. Consequently, we further encourage the scientific community to take into account FBN1 and its related network for the study of DS cardiovascular characteristics.

Exposure to isoflavone-containing soy products and endothelial function: a Bayesian meta-analysis of randomized controlled trials.

PubMed

Beavers, D P; Beavers, K M; Miller, M; Stamey, J; Messina, M J

2012-03-01

To determine whether and to what degree exposure to isoflavone-containing soy products affects EF. Endothelial dysfunction has been identified as an independent coronary heart disease risk factor and a strong predictor of long-term cardiovascular morbidity and mortality. Data on the effects of exposure to isoflavone-containing soy products on EF are conflicting. A comprehensive literature search was conducted using the PUBMED database (National Library of Medicine, Bethesda, MD) inclusively through August 21, 2009 on RCTs using the keywords: soy, isoflavone, phytoestrogen, EF, flow mediated vasodilation, and FMD. A Bayesian meta-analysis was conducted to provide a comprehensive account of the effect of isoflavone-containing soy products on EF, as measured by FMD. A total of 17 RCTs were selected as having sufficient data for study inclusion. The overall mean absolute change in FMD (95% Bayesian CI) for isoflavone-containing soy product interventions was 1.15% (-0.52, 2.75). When the effects of separate interventions were considered, the treatment effect for isolated isoflavones was 1.98% (0.07, 3.97) compared to 0.72% (-1.39, 2.90) for isoflavone-containing soy protein. The models were not improved when considering study-specific effects such as cuff measurement location, prescribed dietary modification, and impaired baseline FMD. Cumulative evidence from the RCTs included in this meta-analysis indicates that exposure to soy isoflavones can modestly, but significantly, improve EF as measured by FMD. Therefore, exposure to isoflavone supplements may beneficially influence vascular health. Copyright © 2010 Elsevier B.V. All rights reserved.

Bayesian networks in neuroscience: a survey.

PubMed

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Bayesian networks in neuroscience: a survey

PubMed Central

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied. PMID:25360109

Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; Higgins, Julian P T

2015-01-01

Estimation of between-study heterogeneity is problematic in small meta-analyses. Bayesian meta-analysis is beneficial because it allows incorporation of external evidence on heterogeneity. To facilitate this, we provide empirical evidence on the likely heterogeneity between studies in meta-analyses relating to specific research settings. Our analyses included 6,492 continuous-outcome meta-analyses within the Cochrane Database of Systematic Reviews. We investigated the influence of meta-analysis settings on heterogeneity by modeling study data from all meta-analyses on the standardized mean difference scale. Meta-analysis setting was described according to outcome type, intervention comparison type, and medical area. Predictive distributions for between-study variance expected in future meta-analyses were obtained, which can be used directly as informative priors. Among outcome types, heterogeneity was found to be lowest in meta-analyses of obstetric outcomes. Among intervention comparison types, heterogeneity was lowest in meta-analyses comparing two pharmacologic interventions. Predictive distributions are reported for different settings. In two example meta-analyses, incorporating external evidence led to a more precise heterogeneity estimate. Heterogeneity was influenced by meta-analysis characteristics. Informative priors for between-study variance were derived for each specific setting. Our analyses thus assist the incorporation of realistic prior information into meta-analyses including few studies. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Overlapping community detection in weighted networks via a Bayesian approach

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

2017-02-01

Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

Comparative efficacy and acceptability of psychotherapies for acute anxiety disorders in children and adolescents: study protocol for a network meta-analysis

PubMed Central

Zhang, Yuqing; Zhou, Xinyu; James, Anthony C; Qin, Bin; Whittington, Craig J; Cuijpers, Pim; Del Giovane, Cinzia; Liu, Yiyun; Cohen, David; Weisz, John R; Xie, Peng

2015-01-01

Introduction Anxiety disorders are associated with significant public health burden in young individuals. Cognitive-behavioural therapy (CBT) is the most commonly used psychotherapy for anxiety disorders in children and adolescents, but previous reviews were hindered by a limited number of trials with direct comparisons between different psychotherapies and their deliveries. Consequently, the main aim of this research was to investigate the comparative efficacy and acceptability of various types and deliveries of psychotherapies for anxiety disorders in children and adolescents. Methods and analysis We will systematically search PubMed, EMBASE, Cochrane, Web of Science, PsycINFO, CINAHL, ProQuest Dissertations and LiLACS for randomised controlled trials, regardless of whether participants received blinding or not, published from 1 January 1966 to 30 January 2015 (updated to 1 July 2015), that compared any psychotherapy with either a control condition or an active comparator with different types and/or different delivery formats for the acute treatment of anxiety disorders in children and adolescents. Data extraction, risk of bias and quality assessments will be independently extracted by two reviewers. The primary outcome for efficacy will be mean overall change scores in anxiety symptoms (self-rated or assessor-rated) from baseline to post-treatment between two groups. The acceptability of treatment will be measured as the proportion of patients who discontinued treatment during the acute phase of treatment. We will assess efficacy, based on the standardised mean difference (SMD), and acceptability, based on the OR, using a random-effects network meta-analysis within a Bayesian framework. Subgroup and sensitivity analyses will be conducted to assess the robustness of the findings. Ethics and dissemination No ethical issues are foreseen. The results will be published in a peer-reviewed journal and will be disseminated electronically and in print. The meta-analysis may be updated to inform and guide management of anxiety in children and adolescents. Trial registration number PROSPERO CRD42015016283. PMID:26443658

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

When is hub gene selection better than standard meta-analysis?

PubMed

Langfelder, Peter; Mischel, Paul S; Horvath, Steve

2013-01-01

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Meta-analysis using Dirichlet process.

PubMed

Muthukumarana, Saman; Tiwari, Ram C

2016-02-01

This article develops a Bayesian approach for meta-analysis using the Dirichlet process. The key aspect of the Dirichlet process in meta-analysis is the ability to assess evidence of statistical heterogeneity or variation in the underlying effects across study while relaxing the distributional assumptions. We assume that the study effects are generated from a Dirichlet process. Under a Dirichlet process model, the study effects parameters have support on a discrete space and enable borrowing of information across studies while facilitating clustering among studies. We illustrate the proposed method by applying it to a dataset on the Program for International Student Assessment on 30 countries. Results from the data analysis, simulation studies, and the log pseudo-marginal likelihood model selection procedure indicate that the Dirichlet process model performs better than conventional alternative methods. © The Author(s) 2012.

Adjustment for reporting bias in network meta-analysis of antidepressant trials

PubMed Central

2012-01-01

Background Network meta-analysis (NMA), a generalization of conventional MA, allows for assessing the relative effectiveness of multiple interventions. Reporting bias is a major threat to the validity of MA and NMA. Numerous methods are available to assess the robustness of MA results to reporting bias. We aimed to extend such methods to NMA. Methods We introduced 2 adjustment models for Bayesian NMA. First, we extended a meta-regression model that allows the effect size to depend on its standard error. Second, we used a selection model that estimates the propensity of trial results being published and in which trials with lower propensity are weighted up in the NMA model. Both models rely on the assumption that biases are exchangeable across the network. We applied the models to 2 networks of placebo-controlled trials of 12 antidepressants, with 74 trials in the US Food and Drug Administration (FDA) database but only 51 with published results. NMA and adjustment models were used to estimate the effects of the 12 drugs relative to placebo, the 66 effect sizes for all possible pair-wise comparisons between drugs, probabilities of being the best drug and ranking of drugs. We compared the results from the 2 adjustment models applied to published data and NMAs of published data and NMAs of FDA data, considered as representing the totality of the data. Results Both adjustment models showed reduced estimated effects for the 12 drugs relative to the placebo as compared with NMA of published data. Pair-wise effect sizes between drugs, probabilities of being the best drug and ranking of drugs were modified. Estimated drug effects relative to the placebo from both adjustment models were corrected (i.e., similar to those from NMA of FDA data) for some drugs but not others, which resulted in differences in pair-wise effect sizes between drugs and ranking. Conclusions In this case study, adjustment models showed that NMA of published data was not robust to reporting bias and provided estimates closer to that of NMA of FDA data, although not optimal. The validity of such methods depends on the number of trials in the network and the assumption that conventional MAs in the network share a common mean bias mechanism. PMID:23016799

Data extraction for complex meta-analysis (DECiMAL) guide.

PubMed

Pedder, Hugo; Sarri, Grammati; Keeney, Edna; Nunes, Vanessa; Dias, Sofia

2016-12-13

As more complex meta-analytical techniques such as network and multivariate meta-analyses become increasingly common, further pressures are placed on reviewers to extract data in a systematic and consistent manner. Failing to do this appropriately wastes time, resources and jeopardises accuracy. This guide (data extraction for complex meta-analysis (DECiMAL)) suggests a number of points to consider when collecting data, primarily aimed at systematic reviewers preparing data for meta-analysis. Network meta-analysis (NMA), multiple outcomes analysis and analysis combining different types of data are considered in a manner that can be useful across a range of data collection programmes. The guide has been shown to be both easy to learn and useful in a small pilot study.

Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints

PubMed Central

Thompson, John R; Spata, Enti; Abrams, Keith R

2015-01-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing–remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions. PMID:26271918

Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints.

PubMed

Bujkiewicz, Sylwia; Thompson, John R; Spata, Enti; Abrams, Keith R

2017-10-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing-remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

PubMed

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2017-12-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

Network structure exploration in networks with node attributes

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Questionable assumptions hampered interpretation of a network meta-analysis of primary care depression treatments.

PubMed

Linde, Klaus; Rücker, Gerta; Schneider, Antonius; Kriston, Levente

2016-03-01

We aimed to evaluate the underlying assumptions of a network meta-analysis investigating which depression treatment works best in primary care and to highlight challenges and pitfalls of interpretation under consideration of these assumptions. We reviewed 100 randomized trials investigating pharmacologic and psychological treatments for primary care patients with depression. Network meta-analysis was carried out within a frequentist framework using response to treatment as outcome measure. Transitivity was assessed by epidemiologic judgment based on theoretical and empirical investigation of the distribution of trial characteristics across comparisons. Homogeneity and consistency were investigated by decomposing the Q statistic. There were important clinical and statistically significant differences between "pure" drug trials comparing pharmacologic substances with each other or placebo (63 trials) and trials including a psychological treatment arm (37 trials). Overall network meta-analysis produced results well comparable with separate meta-analyses of drug trials and psychological trials. Although the homogeneity and consistency assumptions were mostly met, we considered the transitivity assumption unjustifiable. An exchange of experience between reviewers and, if possible, some guidance on how reviewers addressing important clinical questions can proceed in situations where important assumptions for valid network meta-analysis are not met would be desirable. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparative effectiveness of immunosuppressants and biologics for inducing and maintaining remission in Crohn's disease: a network meta-analysis.

PubMed

Hazlewood, Glen S; Rezaie, Ali; Borman, Meredith; Panaccione, Remo; Ghosh, Subrata; Seow, Cynthia H; Kuenzig, Ellen; Tomlinson, George; Siegel, Corey A; Melmed, Gil Y; Kaplan, Gilaad G

2015-02-01

There is controversy regarding the best treatment for patients with Crohn's disease because of the lack of direct comparative trials. We compared therapies for induction and maintenance of remission in patients with Crohn's disease, based on direct and indirect evidence. We performed systematic reviews of MEDLINE, EMBASE, and Cochrane Central databases, through June 2014. We identified randomized controlled trials (N = 39) comparing methotrexate, azathioprine/6-mercaptopurine, infliximab, adalimumab, certolizumab, vedolizumab, or combined therapies with placebo or an active agent for induction and maintenance of remission in adult patients with Crohn's disease. Pairwise treatment effects were estimated through a Bayesian random-effects network meta-analysis and reported as odds ratios (OR) with a 95% credible interval (CrI). Infliximab, the combination of infliximab and azathioprine (infliximab + azathioprine), adalimumab, and vedolizumab were superior to placebo for induction of remission. In pair-wise comparisons of anti-tumor necrosis factor agents, infliximab + azathioprine (OR, 3.1; 95% CrI, 1.4-7.7) and adalimumab (OR, 2.1; 95% CrI, 1.0-4.6) were superior to certolizumab for induction of remission. All treatments were superior to placebo for maintaining remission, except for the combination of infliximab and methotrexate. Adalimumab, infliximab, and infliximab + azathioprine were superior to azathioprine/6-mercaptopurine: adalimumab (OR, 2.9; 95% CrI, 1.6-5.1), infliximab (OR, 1.6; 95% CrI, 1.0-2.5), infliximab + azathioprine (OR, 3.0; 95% CrI, 1.7-5.5) for maintenance of remission. Adalimumab and infliximab + azathioprine were superior to certolizumab: adalimumab (OR, 2.5; 95% CrI, 1.4-4.6) and infliximab + azathioprine (OR, 2.6; 95% CrI, 1.3-6.0). Adalimumab was superior to vedolizumab (OR, 2.4; 95% CrI, 1.2-4.6). Based on a network meta-analysis, adalimumab and infliximab + azathioprine are the most effective therapies for induction and maintenance of remission of Crohn's disease. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Comparative Effectiveness of 12 Treatment Strategies for Preventing Contrast-Induced Acute Kidney Injury: A Systematic Review and Bayesian Network Meta-analysis.

PubMed

Su, Xiaole; Xie, Xinfang; Liu, Lijun; Lv, Jicheng; Song, Fujian; Perkovic, Vlado; Zhang, Hong

2017-01-01

To simultaneously evaluate the relative efficacy of multiple pharmacologic strategies for preventing contrast-induced acute kidney injury (AKI). Systematic review containing a Bayesian network meta-analysis of randomized controlled trials. Participants undergoing diagnostic and/or interventional procedures with contrast media. Randomized controlled trials comparing the active drug treatments with each other or with hydration alone. Any of the following drugs in combination with hydration: N-acetylcysteine (NAC), theophylline (aminophylline), fenoldopam, iloprost, alprostadil, prostaglandin E 1 , statins, statins plus NAC, bicarbonate sodium, bicarbonate sodium plus NAC, ascorbic acid (vitamin C), tocopherol (vitamin E), α-lipoic acid, atrial natriuretic peptide, B-type natriuretic peptide, and carperitide. The occurrence of contrast-induced AKI. The trial network included 150 trials with 31,631 participants and 4,182 contrast-induced AKI events assessing 12 different interventions. Compared to hydration, ORs (95% credible intervals) for contrast-induced AKI were 0.31 (0.14-0.60) for high-dose statin plus NAC, 0.37 (0.19-0.64) for high-dose statin alone, 0.37 (0.17-0.72) for prostaglandins, 0.48 (0.26-0.82) for theophylline, 0.62 (0.40-0.88) for bicarbonate sodium plus NAC, 0.67 (0.54-0.81) for NAC alone, 0.64 (0.41-0.95) for vitamins and analogues, 0.70 (0.29-1.37) for natriuretic peptides, 0.69 (0.31-1.37) for fenoldopam, 0.78 (0.59-1.01) for bicarbonate sodium, and 0.98 (0.41-2.07) for low-dose statin. High-dose statin plus NAC or high-dose statin alone were likely to be ranked the best or the second best for preventing contrast-induced AKI. The overall results were not materially changed in metaregressions or subgroup and sensitivity analyses. Patient-level data were unavailable; unable to include some treatment agents; low event rates; imbalanced distribution of participants among treatment strategies. High-dose statins plus hydration with or without NAC might be the preferred treatment strategy to prevent contrast-induced AKI in patients undergoing diagnostic and/or interventional procedures requiring contrast media. Copyright © 2016 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Uncertainty Quantification of Hypothesis Testing for the Integrated Knowledge Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cuellar, Leticia

2012-05-31

The Integrated Knowledge Engine (IKE) is a tool of Bayesian analysis, based on Bayesian Belief Networks or Bayesian networks for short. A Bayesian network is a graphical model (directed acyclic graph) that allows representing the probabilistic structure of many variables assuming a localized type of dependency called the Markov property. The Markov property in this instance makes any node or random variable to be independent of any non-descendant node given information about its parent. A direct consequence of this property is that it is relatively easy to incorporate new evidence and derive the appropriate consequences, which in general is notmore » an easy or feasible task. Typically we use Bayesian networks as predictive models for a small subset of the variables, either the leave nodes or the root nodes. In IKE, since most applications deal with diagnostics, we are interested in predicting the likelihood of the root nodes given new observations on any of the children nodes. The root nodes represent the various possible outcomes of the analysis, and an important problem is to determine when we have gathered enough evidence to lean toward one of these particular outcomes. This document presents criteria to decide when the evidence gathered is sufficient to draw a particular conclusion or decide in favor of a particular outcome by quantifying the uncertainty in the conclusions that are drawn from the data. The material in this document is organized as follows: Section 2 presents briefly a forensics Bayesian network, and we explore evaluating the information provided by new evidence by looking first at the posterior distribution of the nodes of interest, and then at the corresponding posterior odds ratios. Section 3 presents a third alternative: Bayes Factors. In section 4 we finalize by showing the relation between the posterior odds ratios and Bayes factors and showing examples these cases, and in section 5 we conclude by providing clear guidelines of how to use these for the type of Bayesian networks used in IKE.« less

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

PubMed Central

Lindén, Henrik; Lansner, Anders

2016-01-01

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. PMID:27213810

Comparative efficacy of fixed-dose combinations of long-acting muscarinic antagonists and long-acting β2-agonists: a systematic review and network meta-analysis

PubMed Central

Schlueter, Max; Gonzalez-Rojas, N; Baldwin, Michael; Groenke, Lars; Voss, Florian; Reason, Tim

2016-01-01

Background: A number of long-acting muscarinic antagonist (LAMA)/long-acting β2-agonist (LABA) fixed-dose combinations (FDCs) for treatment of moderate-to-very severe chronic obstructive pulmonary disease (COPD) have recently become available, but none have been directly compared in head-to-head randomized controlled trials (RCTs). The purpose of this study was to assess the relative clinical benefit of all currently available LAMA/LABA FDCs using a Bayesian network meta-analysis (NMA). Methods: A systematic literature review identified RCTs investigating the efficacy, safety and quality of life associated with licensed LAMA/LABA FDCs for the treatment of moderate-to-very severe COPD. RCTs were screened for inclusion in the NMA using prespecified eligibility criteria. Data were extracted for outcomes of interest, including change in trough forced expiratory volume in 1 second (tFEV1) from baseline, St. George Respiratory Questionnaire (SGRQ) percentage of responders, Transition Dyspnea Index (TDI) percentage of responders, change in SGRQ score from baseline, change in TDI focal score from baseline, moderate-to-severe exacerbations, all-cause discontinuation, and discontinuation due to adverse events. Results: Following screening, a total of 27 trials from 26 publications with 30,361 subjects were eligible for inclusion in the NMA. Nonsignificant results were seen in most analyses comparing efficacy, exacerbations and discontinuation rates of included LAMA/LABA FDCs (i.e. aclidinium/formoterol 400/12 µg, glycopyrronium/indacaterol 110/50 µg, tiotropium + olodaterol 5/5 µg, umeclidinium/vilanterol 62.5/25 µg). Meta-regression controlling for post-bronchodilator percentage of tFEV1 predicted at baseline as well as meta-regression adjusting for concomitant use of inhaled corticosteroids at baseline was performed to assess the magnitude of effect modification and produced similar results as observed in the base case analysis. Conclusion: All LAMA/LABA FDCs were found to have similar efficacy and safety. Definitive assessment of the relative efficacy of different treatments can only be performed through direct comparison in head-to-head RCTs. In the absence of such data, this indirect comparison may be of value in clinical and health economic decision-making. PMID:26746383

Systematic review and network meta-analysis of combination and monotherapy treatments in disease-modifying antirheumatic drug-experienced patients with rheumatoid arthritis: analysis of American College of Rheumatology criteria scores 20, 50, and 70

PubMed Central

Orme, Michelle E; MacGilchrist, Katherine S; Mitchell, Stephen; Spurden, Dean; Bird, Alex

2012-01-01

Background Biologic disease-modifying antirheumatic drugs (bDMARDs) extend the treatment choices for rheumatoid arthritis patients with suboptimal response or intolerance to conventional DMARDs. The objective of this systematic review and meta-analysis was to compare the relative efficacy of EU-licensed bDMARD combination therapy or monotherapy for patients intolerant of or contraindicated to continued methotrexate. Methods Comprehensive, structured literature searches were conducted in Medline, Embase, and the Cochrane Library, as well as hand-searching of conference proceedings and reference lists. Phase II or III randomized controlled trials reporting American College of Rheumatology (ACR) criteria scores of 20, 50, and 70 between 12 and 30 weeks’ follow-up and enrolling adult patients meeting ACR classification criteria for rheumatoid arthritis previously treated with and with an inadequate response to conventional DMARDs were eligible. To estimate the relative efficacy of treatments whilst preserving the randomized comparisons within each trial, a Bayesian network meta-analysis was conducted in WinBUGS using fixed and random-effects, logit-link models fitted to the binomial ACR 20/50/70 trial data. Results The systematic review identified 10,625 citations, and after a review of 2450 full-text papers, there were 29 and 14 eligible studies for the combination and monotherapy meta-analyses, respectively. In the combination analysis, all licensed bDMARD combinations had significantly higher odds of ACR 20/50/70 compared to DMARDs alone, except for the rituximab comparison, which did not reach significance for the ACR 70 outcome (based on the 95% credible interval). The etanercept combination was significantly better than the tumor necrosis factor-α inhibitors adalimumab and infliximab in improving ACR 20/50/70 outcomes, with no significant differences between the etanercept combination and certolizumab pegol or tocilizumab. Licensed-dose etanercept, adalimumab, and tocilizumab monotherapy were significantly better than placebo in improving ACR 20/50/70 outcomes. Sensitivity analysis indicated that including studies outside the target population could affect the results. Conclusion Licensed bDMARDs are efficacious in patients with an inadequate response to conventional therapy, but tumor necrosis factor-α inhibitor combination therapies are not equally effective. PMID:23269860

Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases.

PubMed

Friede, Tim; Röver, Christian; Wandel, Simon; Neuenschwander, Beat

2017-07-01

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; R code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes. © 2016 The Authors. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1.

PubMed

Jansen, Jeroen P; Fleurence, Rachael; Devine, Beth; Itzler, Robbin; Barrett, Annabel; Hawkins, Neil; Lee, Karen; Boersma, Cornelis; Annemans, Lieven; Cappelleri, Joseph C

2011-06-01

Evidence-based health-care decision making requires comparisons of all relevant competing interventions. In the absence of randomized, controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best choice(s) of treatment. Mixed treatment comparisons, a special case of network meta-analysis, combine direct and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than a traditional meta-analysis. This report from the ISPOR Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on the interpretation of indirect treatment comparisons and network meta-analysis to assist policymakers and health-care professionals in using its findings for decision making. We start with an overview of how networks of randomized, controlled trials allow multiple treatment comparisons of competing interventions. Next, an introduction to the synthesis of the available evidence with a focus on terminology, assumptions, validity, and statistical methods is provided, followed by advice on critically reviewing and interpreting an indirect treatment comparison or network meta-analysis to inform decision making. We finish with a discussion of what to do if there are no direct or indirect treatment comparisons of randomized, controlled trials possible and a health-care decision still needs to be made. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Sizing Up the Milky Way: A Bayesian Mixture Model Meta-analysis of Photometric Scale Length Measurements

NASA Astrophysics Data System (ADS)

Licquia, Timothy C.; Newman, Jeffrey A.

2016-11-01

The exponential scale length (L d ) of the Milky Way’s (MW’s) disk is a critical parameter for describing the global physical size of our Galaxy, important both for interpreting other Galactic measurements and helping us to understand how our Galaxy fits into extragalactic contexts. Unfortunately, current estimates span a wide range of values and are often statistically incompatible with one another. Here, we perform a Bayesian meta-analysis to determine an improved, aggregate estimate for L d , utilizing a mixture-model approach to account for the possibility that any one measurement has not properly accounted for all statistical or systematic errors. Within this machinery, we explore a variety of ways of modeling the nature of problematic measurements, and then employ a Bayesian model averaging technique to derive net posterior distributions that incorporate any model-selection uncertainty. Our meta-analysis combines 29 different (15 visible and 14 infrared) photometric measurements of L d available in the literature; these involve a broad assortment of observational data sets, MW models and assumptions, and methodologies, all tabulated herein. Analyzing the visible and infrared measurements separately yields estimates for L d of {2.71}-0.20+0.22 kpc and {2.51}-0.13+0.15 kpc, respectively, whereas considering them all combined yields 2.64 ± 0.13 kpc. The ratio between the visible and infrared scale lengths determined here is very similar to that measured in external spiral galaxies. We use these results to update the model of the Galactic disk from our previous work, constraining its stellar mass to be {4.8}-1.1+1.5× {10}10 M ⊙, and the MW’s total stellar mass to be {5.7}-1.1+1.5× {10}10 M ⊙.

Inhaled Cannabis for Chronic Neuropathic Pain: A Meta-analysis of Individual Patient Data.

PubMed

Andreae, Michael H; Carter, George M; Shaparin, Naum; Suslov, Kathryn; Ellis, Ronald J; Ware, Mark A; Abrams, Donald I; Prasad, Hannah; Wilsey, Barth; Indyk, Debbie; Johnson, Matthew; Sacks, Henry S

2015-12-01

Chronic neuropathic pain, the most frequent condition affecting the peripheral nervous system, remains underdiagnosed and difficult to treat. Inhaled cannabis may alleviate chronic neuropathic pain. Our objective was to synthesize the evidence on the use of inhaled cannabis for chronic neuropathic pain. We performed a systematic review and a meta-analysis of individual patient data. We registered our protocol with PROSPERO CRD42011001182. We searched in Cochrane Central, PubMed, EMBASE, and AMED. We considered all randomized controlled trials investigating chronic painful neuropathy and comparing inhaled cannabis with placebo. We pooled treatment effects following a hierarchical random-effects Bayesian responder model for the population-averaged subject-specific effect. Our evidence synthesis of individual patient data from 178 participants with 405 observed responses in 5 randomized controlled trials following patients for days to weeks provides evidence that inhaled cannabis results in short-term reductions in chronic neuropathic pain for 1 in every 5 to 6 patients treated (number needed to treat = 5.6 with a Bayesian 95% credible interval ranging between 3.4 and 14). Our inferences were insensitive to model assumptions, priors, and parameter choices. We caution that the small number of studies and participants, the short follow-up, shortcomings in allocation concealment, and considerable attrition limit the conclusions that can be drawn from the review. The Bayes factor is 332, corresponding to a posterior probability of effect of 99.7%. This novel Bayesian meta-analysis of individual patient data from 5 randomized trials suggests that inhaled cannabis may provide short-term relief for 1 in 5 to 6 patients with neuropathic pain. Pragmatic trials are needed to evaluate the long-term benefits and risks of this treatment. Copyright © 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.

Detangling complex relationships in forensic data: principles and use of causal networks and their application to clinical forensic science.

PubMed

Lefèvre, Thomas; Lepresle, Aude; Chariot, Patrick

2015-09-01

The search for complex, nonlinear relationships and causality in data is hindered by the availability of techniques in many domains, including forensic science. Linear multivariable techniques are useful but present some shortcomings. In the past decade, Bayesian approaches have been introduced in forensic science. To date, authors have mainly focused on providing an alternative to classical techniques for quantifying effects and dealing with uncertainty. Causal networks, including Bayesian networks, can help detangle complex relationships in data. A Bayesian network estimates the joint probability distribution of data and graphically displays dependencies between variables and the circulation of information between these variables. In this study, we illustrate the interest in utilizing Bayesian networks for dealing with complex data through an application in clinical forensic science. Evaluating the functional impairment of assault survivors is a complex task for which few determinants are known. As routinely estimated in France, the duration of this impairment can be quantified by days of 'Total Incapacity to Work' ('Incapacité totale de travail,' ITT). In this study, we used a Bayesian network approach to identify the injury type, victim category and time to evaluation as the main determinants of the 'Total Incapacity to Work' (TIW). We computed the conditional probabilities associated with the TIW node and its parents. We compared this approach with a multivariable analysis, and the results of both techniques were converging. Thus, Bayesian networks should be considered a reliable means to detangle complex relationships in data.

Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses

ERIC Educational Resources Information Center

Galbraith, Craig S.; Merrill, Gregory B.; Kline, Doug M.

2012-01-01

In this study we investigate the underlying relational structure between student evaluations of teaching effectiveness (SETEs) and achievement of student learning outcomes in 116 business related courses. Utilizing traditional statistical techniques, a neural network analysis and a Bayesian data reduction and classification algorithm, we find…

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

PubMed Central

Kunkle, Brian W.; Yoo, Changwon; Roy, Deodutta

2013-01-01

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors. PMID:23737970

F-MAP: A Bayesian approach to infer the gene regulatory network using external hints

PubMed Central

Shahdoust, Maryam; Mahjub, Hossein; Sadeghi, Mehdi

2017-01-01

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches. PMID:28938012

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Meta-analyses are no substitute for registered replications: a skeptical perspective on religious priming

PubMed Central

van Elk, Michiel; Matzke, Dora; Gronau, Quentin F.; Guan, Maime; Vandekerckhove, Joachim; Wagenmakers, Eric-Jan

2015-01-01

According to a recent meta-analysis, religious priming has a positive effect on prosocial behavior (Shariff et al., 2015). We first argue that this meta-analysis suffers from a number of methodological shortcomings that limit the conclusions that can be drawn about the potential benefits of religious priming. Next we present a re-analysis of the religious priming data using two different meta-analytic techniques. A Precision-Effect Testing–Precision-Effect-Estimate with Standard Error (PET-PEESE) meta-analysis suggests that the effect of religious priming is driven solely by publication bias. In contrast, an analysis using Bayesian bias correction suggests the presence of a religious priming effect, even after controlling for publication bias. These contradictory statistical results demonstrate that meta-analytic techniques alone may not be sufficiently robust to firmly establish the presence or absence of an effect. We argue that a conclusive resolution of the debate about the effect of religious priming on prosocial behavior – and about theoretically disputed effects more generally – requires a large-scale, preregistered replication project, which we consider to be the sole remedy for the adverse effects of experimenter bias and publication bias. PMID:26441741

Model Diagnostics for Bayesian Networks

ERIC Educational Resources Information Center

Sinharay, Sandip

2006-01-01

Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

Biodegradable-polymer drug-eluting stents vs. bare metal stents vs. durable-polymer drug-eluting stents: a systematic review and Bayesian approach network meta-analysis.

PubMed

Kang, Si-Hyuck; Park, Kyung Woo; Kang, Do-Yoon; Lim, Woo-Hyun; Park, Kyung Taek; Han, Jung-Kyu; Kang, Hyun-Jae; Koo, Bon-Kwon; Oh, Byung-Hee; Park, Young-Bae; Kandzari, David E; Cohen, David J; Hwang, Seung-Sik; Kim, Hyo-Soo

2014-05-01

The aim of this study was to compare the safety and efficacy of biodegradable-polymer (BP) drug-eluting stents (DES), bare metal stents (BMS), and durable-polymer DES in patients undergoing coronary revascularization, we performed a systematic review and network meta-analysis using a Bayesian framework. Study stents included BMS, paclitaxel-eluting (PES), sirolimus-eluting (SES), endeavor zotarolimus-eluting (ZES-E), cobalt-chromium everolimus-eluting (CoCr-EES), platinium-chromium everolimus-eluting (PtCr-EES), resolute zotarolimus-eluting (ZES-R), and BP biolimus-eluting stents (BP-BES). After a systematic electronic search, 113 trials with 90 584 patients were selected. The principal endpoint was definite or probable stent thrombosis (ST) defined according to the Academic Research Consortium within 1 year. Biodegradable polymer-biolimus-eluting stents [OR, 0.56; 95% credible interval (CrI), 0.33-0.90], SES (OR, 0.53; 95% CrI, 0.38-0.73), CoCr-EES (OR, 0.34; 95% CrI, 0.23-0.52), and PtCr-EES (OR, 0.31; 95% CrI, 0.10-0.90) were all superior to BMS in terms of definite or probable ST within 1 year. Cobalt-chromium everolimus-eluting stents demonstrated the lowest risk of ST of all stents at all times after stent implantation. Biodegradable polymer-biolimus-eluting stents was associated with a higher risk of definite or probable ST than CoCr-EES (OR, 1.72; 95% CrI, 1.04-2.98). All DES reduced the need for repeat revascularization, and all but PES reduced the risk of myocardial infarction compared with BMS. All DESs but PES and ZES-E were superior to BMS in terms of ST within 1 year. Cobalt-chromium everolimus-eluting stents was safer than any DES even including BP-BES. Our results suggest that not only the biodegradability of polymer, but the optimal combination of stent alloy, design, strut thickness, polymer, and drug all combined determine the safety of DES.

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

NASA Astrophysics Data System (ADS)

Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming

2017-09-01

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

Bayesian variable selection for post-analytic interrogation of susceptibility loci.

PubMed

Chen, Siying; Nunez, Sara; Reilly, Muredach P; Foulkes, Andrea S

2017-06-01

Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material. © 2016, The International Biometric Society.

Safety profile of biologic drugs in the therapy of Crohn disease: A systematic review and network meta-analysis.

PubMed

Moćko, Paweł; Kawalec, Paweł; Pilc, Andrzej

2016-12-01

Crohn disease (CD) is an inflammatory bowel disease which occurs especially in developed countries of Western Europe and North America. The aim of the study was to compare the safety profile of biologic drugs in patients with CD. A systematic literature search was performed using PubMed, Embase, and CENTRAL databases, until April 27, 2016. We included randomized controlled trials (RCTs) that compared the safety of biologic drugs (infliximab, adalimumab, vedolizumab, certolizumab pegol, and ustekinumab) with one another or with placebo in patients with CD. The network meta-analysis (NMA) was conducted for an induction phase (6-10 weeks) and maintenance phase (52-56 weeks) with a Bayesian hierarchical random effects model in the ADDIS ® software. The PROSPERO registration number was CRD42016032606. Ten RCTs were included in the systematic review with NMA. In the case of the induction phase, the NMA could be conducted for the assessment of the relative safety profile of adalimumab, vedolizumab, certolizumab pegol, and ustekinumab, and in the case of the maintenance phase-of infliximab, adalimumab, and vedolizumab. There were no significant differences in the rate of adverse events in patients treated with biologics. Statistical analysis revealed that vedolizumab had the greatest probability of being the safest treatment in most endpoints in the induction phase and adalimumab-in the maintenance phase. No significant differences between the biologics in the relative safety profile analysis were observed. Further studies are needed to confirm our findings, including head-to-head comparisons between the analyzed biologics. Copyright © 2016 Institute of Pharmacology, Polish Academy of Sciences. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Missing value imputation: with application to handwriting data

NASA Astrophysics Data System (ADS)

Xu, Zhen; Srihari, Sargur N.

2015-01-01

Missing values make pattern analysis difficult, particularly with limited available data. In longitudinal research, missing values accumulate, thereby aggravating the problem. Here we consider how to deal with temporal data with missing values in handwriting analysis. In the task of studying development of individuality of handwriting, we encountered the fact that feature values are missing for several individuals at several time instances. Six algorithms, i.e., random imputation, mean imputation, most likely independent value imputation, and three methods based on Bayesian network (static Bayesian network, parameter EM, and structural EM), are compared with children's handwriting data. We evaluate the accuracy and robustness of the algorithms under different ratios of missing data and missing values, and useful conclusions are given. Specifically, static Bayesian network is used for our data which contain around 5% missing data to provide adequate accuracy and low computational cost.

Antibiotics in aggressive periodontitis, is there a clinical benefit?

PubMed

Rajendra, Anjana; Spivakovsky, Silvia

2016-12-01

Data sourcesMedline, Embase and CENTRAL databases were searched up to December 2014. Unpublished data were sought by searching a database listing unpublished studies OpenGray [http://www.opengrey.eu/], formerly OpenSIGLE.Study selectionRandomised clinical trials assessing treatment of patients with AgP comparing scaling and root planing (SRP) alone with SRP plus a single antibiotic or a combination of drugs with a minimum of three months follow-up were considered. Studies specifically designed to evaluate smokers or subjects with diabetes mellitus and HIV/AIDS were not included.Data extraction and synthesisTwo researchers independently screened titles, abstracts and full texts of the search results. The studies, which fulfilled inclusion criteria, underwent validity assessment and data extraction using a specifically designed form. The quality of included studies was assessed using the Cochranes collaboration tool for risk of bias. Only two of the 11 included trials were considered at a low risk of bias. The data extracted from ten studies was analysed by pair-wise meta-analyses and the data extracted from five studies was included in Bayesian network meta-analyses pooled estimates. The six studies evaluated in the pairwise meta-analyses were excluded in the pooled estimates because four studies included patients with advanced disease only and one study did not present average data for pocket depth (PD) and clinical attachment level (CAL) and another one for using a different mechanical treatment.ResultsFourteen studies reporting 11 randomised clinical trials with a total of 388 patients were included in the review. Nine of 11 studies reported a statistically significant greater gain in full mouth mean clinical attachment (CA) and reduction in probing depth (PD) when a systemic antibiotic was used. From those studies the calculated mean difference for CA gained was 1.08 mm (p < 0.0001) and for PD reduction was 1.05 mm (p< 0.00001) for SRP + Metronidazole (Mtz). For SRP + Mtz+ amoxicillin (Amx) group the mean difference was 0.45 mm for CA gained and 0.53 mm for PD reduction (p<0.00001) than SRP alone/placebo. Bayesian network meta-analysis showed some additional benefits in CA gain and PD reduction when SRP was associated with systemic antibiotics.ConclusionsThe results support a statistically significant benefit of adjunctive systemic antibiotics in the treatment of AgP. The most consistent advantages - reduction in PD and CAL gain - were attained with the use of Mtz and Mtz + Amx. Future RCTs should be designed in order to directly compare these two antibiotic protocols in the treatment of AgP.

Using Bayesian analysis in repeated preclinical in vivo studies for a more effective use of animals.

PubMed

Walley, Rosalind; Sherington, John; Rastrick, Joe; Detrait, Eric; Hanon, Etienne; Watt, Gillian

2016-05-01

Whilst innovative Bayesian approaches are increasingly used in clinical studies, in the preclinical area Bayesian methods appear to be rarely used in the reporting of pharmacology data. This is particularly surprising in the context of regularly repeated in vivo studies where there is a considerable amount of data from historical control groups, which has potential value. This paper describes our experience with introducing Bayesian analysis for such studies using a Bayesian meta-analytic predictive approach. This leads naturally either to an informative prior for a control group as part of a full Bayesian analysis of the next study or using a predictive distribution to replace a control group entirely. We use quality control charts to illustrate study-to-study variation to the scientists and describe informative priors in terms of their approximate effective numbers of animals. We describe two case studies of animal models: the lipopolysaccharide-induced cytokine release model used in inflammation and the novel object recognition model used to screen cognitive enhancers, both of which show the advantage of a Bayesian approach over the standard frequentist analysis. We conclude that using Bayesian methods in stable repeated in vivo studies can result in a more effective use of animals, either by reducing the total number of animals used or by increasing the precision of key treatment differences. This will lead to clearer results and supports the "3Rs initiative" to Refine, Reduce and Replace animals in research. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Pathway analysis of high-throughput biological data within a Bayesian network framework.

PubMed

Isci, Senol; Ozturk, Cengizhan; Jones, Jon; Otu, Hasan H

2011-06-15

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC. Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

PubMed

Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

2017-07-15

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity. Copyright © 2017 Elsevier Inc. All rights reserved.

Age estimation by assessment of pulp chamber volume: a Bayesian network for the evaluation of dental evidence.

PubMed

Sironi, Emanuele; Taroni, Franco; Baldinotti, Claudio; Nardi, Cosimo; Norelli, Gian-Aristide; Gallidabino, Matteo; Pinchi, Vilma

2017-11-14

The present study aimed to investigate the performance of a Bayesian method in the evaluation of dental age-related evidence collected by means of a geometrical approximation procedure of the pulp chamber volume. Measurement of this volume was based on three-dimensional cone beam computed tomography images. The Bayesian method was applied by means of a probabilistic graphical model, namely a Bayesian network. Performance of that method was investigated in terms of accuracy and bias of the decisional outcomes. Influence of an informed elicitation of the prior belief of chronological age was also studied by means of a sensitivity analysis. Outcomes in terms of accuracy were adequate with standard requirements for forensic adult age estimation. Findings also indicated that the Bayesian method does not show a particular tendency towards under- or overestimation of the age variable. Outcomes of the sensitivity analysis showed that results on estimation are improved with a ration elicitation of the prior probabilities of age.

Treatment strategies for coronary in-stent restenosis: systematic review and hierarchical Bayesian network meta-analysis of 24 randomised trials and 4880 patients

PubMed Central

Giacoppo, Daniele; Gargiulo, Giuseppe; Aruta, Patrizia; Capranzano, Piera; Tamburino, Corrado

2015-01-01

Study question What is the most safe and effective interventional treatment for coronary in-stent restenosis? Methods In a hierarchical Bayesian network meta-analysis, PubMed, Embase, Scopus, Cochrane Library, Web of Science, ScienceDirect, and major scientific websites were screened up to 10 August 2015. Randomised controlled trials of patients with any type of coronary in-stent restenosis (either of bare metal stents or drug eluting stents; and either first or recurrent instances) were included. Trials including multiple treatments at the same time in the same group or comparing variants of the same intervention were excluded. Primary endpoints were target lesion revascularisation and late lumen loss, both at six to 12 months. The main analysis was complemented by network subanalyses, standard pairwise comparisons, and subgroup and sensitivity analyses. Study answer and limitations Twenty four trials (4880 patients), including seven interventional treatments, were identified. Compared with plain balloons, bare metal stents, brachytherapy, rotational atherectomy, and cutting balloons, drug coated balloons and drug eluting stents were associated with a reduced risk of target lesion revascularisation and major adverse cardiac events, and with reduced late lumen loss. Treatment ranking indicated that drug eluting stents had the highest probability (61.4%) of being the most effective for target lesion vascularisation; drug coated balloons were similarly indicated as the most effective treatment for late lumen loss (probability 70.3%). The comparative efficacy of drug coated balloons and drug eluting stents was similar for target lesion revascularisation (summary odds ratio 1.10, 95% credible interval 0.59 to 2.01) and late lumen loss reduction (mean difference in minimum lumen diameter 0.04 mm, 95% credible interval −0.20 to 0.10). Risks of death, myocardial infarction, and stent thrombosis were comparable across all treatments, but these analyses were limited by a low number of events. Trials had heterogeneity regarding investigation periods, baseline characteristics, and endpoint reporting, with a lack of information at long term follow-up. Direct and indirect evidence was also inconsistent for the comparison between drug eluting stents and drug coated balloons. What this study adds Compared with other currently available interventional treatments for coronary in-stent restenosis, drug coated balloons and drug eluting stents are associated with superior clinical and angiographic outcomes, with a similar comparative efficacy. Funding, competing interests, data sharing This study received no external funding. The authors declare no competing interests. No additional data available. PMID:26537292

Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies.

PubMed

Li, Hui-Jie; Hou, Xiao-Hui; Liu, Han-Hui; Yue, Chun-Lin; He, Yong; Zuo, Xi-Nian

2015-03-01

Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta-analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting-state fMRI data of 1,000 healthy participants. Thirty-nine task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis. The meta-analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large-scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD-related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD. © 2014 Wiley Periodicals, Inc.

[Efficacy of racecadotril vs. smectite, probiotics or zinc as an integral part of treatment of acute diarrhea in children under five years: A meta-analysis of multiple treatments].

PubMed

Gutiérrez-Castrellón, Pedro; Ortíz-Hernández, Anna Alejandra; Llamosas-Gallardo, Beatriz; Acosta-Bastidas, Mario A; Jiménez-Gutiérrez, Carlos; Diaz-García, Luisa; Anzo-Osorio, Anahí; Estevez-Jiménez, Juliana; Jiménez-Escobar, Irma; Vidal-Vázquez, Rosa Patricia

2015-01-01

Despite major advances in treatment, acute diarrhea continues to be a public health problem in children under five years. There is no systematic approach to treatment and most evidence is assembled comparing active treatment vs. placebo. Systematic review of evidence on efficacy of adjuvants for treatment of acute diarrhea through a network meta-analysis. A systematic search of multiple databases searching clinical trials related to the use of racecadotril, smectite, Lactobacillus GG, Lactobacillus reuteri, Saccharomyces boulardii and zinc as adjuvants in acute diarrhea was done. The primary endpoint was duration of diarrhea. Information is displayed through network meta-analysis.The superiority of each coadjutant was analyzed by Sucra approach. Network meta-analysis showed race cadotril was better when compared with placebo and other adjuvants. Sucra analysis showed racecadotril as the first option followed by smectite and Lactobacillus reuteri. Considering a strategic decision making approach, network meta-analysis allows us to establish the therapeutic superiority of racecadotril as an adjunct for the comprehensive management of acute diarrhea in children aged less than five years.

Meta-connectomics: human brain network and connectivity meta-analyses.

PubMed

Crossley, N A; Fox, P T; Bullmore, E T

2016-04-01

Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as 'meta-connectomics'. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI 'lesions' to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

PubMed

Niu, Zhendong; Nie, Yaoxin; Zhou, Qian; Zhu, Linlin; Wei, Jieyao

2016-05-18

Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California

USGS Publications Warehouse

Halstead, Brian J.; Wylie, Glenn D.; Casazza, Michael L.; Hansen, Eric C.; Scherer, Rick D.; Patterson, Laura C.

2015-08-14

Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.

Risk of gastrointestinal bleeding with direct oral anticoagulants: a systematic review and network meta-analysis.

PubMed

Burr, Nick; Lummis, Katie; Sood, Ruchit; Kane, John Samuel; Corp, Aaron; Subramanian, Venkataraman

2017-02-01

Direct oral anticoagulants are increasingly used for a wide range of indications. However, data are conflicting about the risk of major gastrointestinal bleeding with these drugs. We compared the risk of gastrointestinal bleeding with direct oral anticoagulants, warfarin, and low-molecular-weight heparin. For this systematic review and meta-analysis, we searched MEDLINE and Embase from database inception to April 1, 2016, for prospective and retrospective studies that reported the risk of gastrointestinal bleeding with use of a direct oral anticoagulant compared with warfarin or low-molecular-weight heparin for all indications. We also searched the Cochrane Library for systematic reviews and assessment evaluations, the National Health Service (UK) Economic Evaluation Database, and ISI Web of Science for conference abstracts and proceedings (up to April 1, 2016). The primary outcome was the incidence of major gastrointestinal bleeding, with all gastrointestinal bleeding as a secondary outcome. We did a Bayesian network meta-analysis to produce incidence rate ratios (IRRs) with 95% credible intervals (CrIs). We identified 38 eligible articles, of which 31 were included in the primary analysis, including 287 692 patients exposed to 230 090 years of anticoagulant drugs. The risk of major gastrointestinal bleeding with direct oral anticoagulants did not differ from that with warfarin or low-molecular-weight heparin (factor Xa vs warfarin IRR 0·78 [95% CrI 0·47-1·08]; warfarin vs dabigatran 0·88 [0·59-1·36]; factor Xa vs low-molecular-weight heparin 1·02 [0·42-2·70]; and low-molecular-weight heparin vs dabigatran 0·67 [0·20-1·82]). In the secondary analysis, factor Xa inhibitors were associated with a reduced risk of all severities of gastrointestinal bleeding compared with warfarin (0·25 [0.07-0.76]) or dabigatran (0.24 [0.07-0.77]). Our findings show no increase in risk of major gastrointestinal bleeding with direct oral anticoagulants compared with warfarin or low-molecular-weight heparin. These findings support the continued use of direct oral anticoagulants. Leeds Teaching Hospitals Charitable Foundation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

Using meta-information of a posteriori Bayesian solutions of the hypocentre location task for improving accuracy of location error estimation

NASA Astrophysics Data System (ADS)

Debski, Wojciech

2015-06-01

The spatial location of sources of seismic waves is one of the first tasks when transient waves from natural (uncontrolled) sources are analysed in many branches of physics, including seismology, oceanology, to name a few. Source activity and its spatial variability in time, the geometry of recording network, the complexity and heterogeneity of wave velocity distribution are all factors influencing the performance of location algorithms and accuracy of the achieved results. Although estimating of the earthquake foci location is relatively simple, a quantitative estimation of the location accuracy is really a challenging task even if the probabilistic inverse method is used because it requires knowledge of statistics of observational, modelling and a priori uncertainties. In this paper, we addressed this task when statistics of observational and/or modelling errors are unknown. This common situation requires introduction of a priori constraints on the likelihood (misfit) function which significantly influence the estimated errors. Based on the results of an analysis of 120 seismic events from the Rudna copper mine operating in southwestern Poland, we propose an approach based on an analysis of Shanon's entropy calculated for the a posteriori distribution. We show that this meta-characteristic of the a posteriori distribution carries some information on uncertainties of the solution found.

A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.

PubMed

Ma, Xiaoye; Chen, Yong; Cole, Stephen R; Chu, Haitao

2016-12-01

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. © The Author(s) 2014.

A Hybrid Bayesian Hierarchical Model Combining Cohort and Case-control Studies for Meta-analysis of Diagnostic Tests: Accounting for Partial Verification Bias

PubMed Central

Ma, Xiaoye; Chen, Yong; Cole, Stephen R.; Chu, Haitao

2014-01-01

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. PMID:24862512

Introduction of Bayesian network in risk analysis of maritime accidents in Bangladesh

NASA Astrophysics Data System (ADS)

Rahman, Sohanur

2017-12-01

Due to the unique geographic location, complex navigation environment and intense vessel traffic, a considerable number of maritime accidents occurred in Bangladesh which caused serious loss of life, property and environmental contamination. Based on the historical data of maritime accidents from 1981 to 2015, which has been collected from Department of Shipping (DOS) and Bangladesh Inland Water Transport Authority (BIWTA), this paper conducted a risk analysis of maritime accidents by applying Bayesian network. In order to conduct this study, a Bayesian network model has been developed to find out the relation among parameters and the probability of them which affect accidents based on the accident investigation report of Bangladesh. Furthermore, number of accidents in different categories has also been investigated in this paper. Finally, some viable recommendations have been proposed in order to ensure greater safety of inland vessels in Bangladesh.

Additional considerations are required when preparing a protocol for a systematic review with multiple interventions.

PubMed

Chaimani, Anna; Caldwell, Deborah M; Li, Tianjing; Higgins, Julian P T; Salanti, Georgia

2017-03-01

The number of systematic reviews that aim to compare multiple interventions using network meta-analysis is increasing. In this study, we highlight aspects of a standard systematic review protocol that may need modification when multiple interventions are to be compared. We take the protocol format suggested by Cochrane for a standard systematic review as our reference and compare the considerations for a pairwise review with those required for a valid comparison of multiple interventions. We suggest new sections for protocols of systematic reviews including network meta-analyses with a focus on how to evaluate their assumptions. We provide example text from published protocols to exemplify the considerations. Standard systematic review protocols for pairwise meta-analyses need extensions to accommodate the increased complexity of network meta-analysis. Our suggested modifications are widely applicable to both Cochrane and non-Cochrane systematic reviews involving network meta-analyses. Copyright © 2017 Elsevier Inc. All rights reserved.

Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

PubMed Central

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-01-01

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying. PMID:25393784

Modular analysis of the probabilistic genetic interaction network.

PubMed

Hou, Lin; Wang, Lin; Qian, Minping; Li, Dong; Tang, Chao; Zhu, Yunping; Deng, Minghua; Li, Fangting

2011-03-15

Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.

The Importance of Considering Differences in Study Design in Network Meta-analysis: An Application Using Anti-Tumor Necrosis Factor Drugs for Ulcerative Colitis.

PubMed

Cameron, Chris; Ewara, Emmanuel; Wilson, Florence R; Varu, Abhishek; Dyrda, Peter; Hutton, Brian; Ingham, Michael

2017-11-01

Adaptive trial designs present a methodological challenge when performing network meta-analysis (NMA), as data from such adaptive trial designs differ from conventional parallel design randomized controlled trials (RCTs). We aim to illustrate the importance of considering study design when conducting an NMA. Three NMAs comparing anti-tumor necrosis factor drugs for ulcerative colitis were compared and the analyses replicated using Bayesian NMA. The NMA comprised 3 RCTs comparing 4 treatments (adalimumab 40 mg, golimumab 50 mg, golimumab 100 mg, infliximab 5 mg/kg) and placebo. We investigated the impact of incorporating differences in the study design among the 3 RCTs and presented 3 alternative methods on how to convert outcome data derived from one form of adaptive design to more conventional parallel RCTs. Combining RCT results without considering variations in study design resulted in effect estimates that were biased against golimumab. In contrast, using the 3 alternative methods to convert outcome data from one form of adaptive design to a format more consistent with conventional parallel RCTs facilitated more transparent consideration of differences in study design. This approach is more likely to yield appropriate estimates of comparative efficacy when conducting an NMA, which includes treatments that use an alternative study design. RCTs based on adaptive study designs should not be combined with traditional parallel RCT designs in NMA. We have presented potential approaches to convert data from one form of adaptive design to more conventional parallel RCTs to facilitate transparent and less-biased comparisons.

Meta-analysis of the effect of natural frequencies on Bayesian reasoning.

PubMed

McDowell, Michelle; Jacobs, Perke

2017-12-01

The natural frequency facilitation effect describes the finding that people are better able to solve descriptive Bayesian inference tasks when represented as joint frequencies obtained through natural sampling, known as natural frequencies, than as conditional probabilities. The present meta-analysis reviews 20 years of research seeking to address when, why, and for whom natural frequency formats are most effective. We review contributions from research associated with the 2 dominant theoretical perspectives, the ecological rationality framework and nested-sets theory, and test potential moderators of the effect. A systematic review of relevant literature yielded 35 articles representing 226 performance estimates. These estimates were statistically integrated using a bivariate mixed-effects model that yields summary estimates of average performances across the 2 formats and estimates of the effects of different study characteristics on performance. These study characteristics range from moderators representing individual characteristics (e.g., numeracy, expertise), to methodological differences (e.g., use of incentives, scoring criteria) and features of problem representation (e.g., short menu format, visual aid). Short menu formats (less computationally complex representations showing joint-events) and visual aids demonstrated some of the strongest moderation effects, improving performance for both conditional probability and natural frequency formats. A number of methodological factors (e.g., exposure to both problem formats) were also found to affect performance rates, emphasizing the importance of a systematic approach. We suggest how research on Bayesian reasoning can be strengthened by broadening the definition of successful Bayesian reasoning to incorporate choice and process and by applying different research methodologies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Comparison of multiple interventions for older adults with Alzheimer disease or mild cognitive impairment: A PRISMA-compliant network meta-analysis.

PubMed

Liang, Jing-Hong; Xu, Yong; Lin, Lu; Jia, Rui-Xia; Zhang, Hong-Bo; Hang, Lei

2018-05-01

The increasing prevalence of Alzheimer disease (AD) emphasizes the need for effective treatments. Both pharmacological therapies such as nutrition therapy (NT) and nonpharmacologic therapies including traditional treatment or personalized treatment (e.g., physical exercise, music therapy, computerized cognitive training) have been approved for the treatment of AD or mild cognitive impairment (MCI) in numerous areas. The aim of this study was to compare 4 types of interventions, physical exercise (PE), music therapy (MT), computerized cognitive training (CCT), and NT, in older adults with mild to moderate AD or MCI and identify the most effective intervention for their cognitive function. We used a system of search strategies to identify relevant studies and include randomized controlled trials (RCTs), placebo-controlled trials evaluating the efficacy and safety of 4 interventions in patients with AD or MCI. We updated the relevant studies which were published before March 2017 as a full-text article. Using Bayesian network meta-analysis (NMA), we ranked cognitive ability based objectively on Mini-Mental State Examination (MMSE), and assessed neuropsychiatric symptoms based on Neuropsychiatric Inventory (NPI). Pairwise and network meta-analyses were sequentially performed for efficacy and safety of intervention compared to control group through RCTs included. We included 17 RCTs. Fifteen trials (n = 1747) were pooled for cognition and no obvious heterogeneity was found (I = 21.7%, P = .212) in NMA, the mean difference (MD) of PE (MD = 2.1, confidence interval [CI]: 0.44-3.8) revealed that PE was significantly efficacious in the treatment group in terms of MMSE. Five trials (n = 660) assessed neuropsychiatric symptoms with an obvious heterogeneity (I = 61.6%, P = .034), the MD of CCT (MD = -7.7, CI: -14 to -2.4), revealing that CCT was significantly efficacious in NPI. As the first NMA comparing different interventions for AD and MCI, our study suggests that PE and CCT might have a significant improvement in cognition and neuropsychiatric symptoms respectively. Moreover, nonpharmacological therapies might be better than pharmacological therapies.

Management of Listeria monocytogenes in fermented sausages using the Food Safety Objective concept underpinned by stochastic modeling and meta-analysis.

PubMed

Mataragas, M; Alessandria, V; Rantsiou, K; Cocolin, L

2015-08-01

In the present work, a demonstration is made on how the risk from the presence of Listeria monocytogenes in fermented sausages can be managed using the concept of Food Safety Objective (FSO) aided by stochastic modeling (Bayesian analysis and Monte Carlo simulation) and meta-analysis. For this purpose, the ICMSF equation was used, which combines the initial level (H0) of the hazard and its subsequent reduction (ΣR) and/or increase (ΣI) along the production chain. Each element of the equation was described by a distribution to investigate the effect not only of the level of the hazard, but also the effect of the accompanying variability. The distribution of each element was determined by Bayesian modeling (H0) and meta-analysis (ΣR and ΣI). The output was a normal distribution N(-5.36, 2.56) (log cfu/g) from which the percentage of the non-conforming products, i.e. the fraction above the FSO of 2 log cfu/g, was estimated at 0.202%. Different control measures were examined such as lowering initial L. monocytogenes level and inclusion of an additional killing step along the process resulting in reduction of the non-conforming products from 0.195% to 0.003% based on the mean and/or square-root change of the normal distribution, and 0.001%, respectively. Copyright © 2015 Elsevier Ltd. All rights reserved.

Blast-related mild traumatic brain injury: a Bayesian random-effects meta-analysis on the cognitive outcomes of concussion among military personnel.

PubMed

Karr, Justin E; Areshenkoff, Corson N; Duggan, Emily C; Garcia-Barrera, Mauricio A

2014-12-01

Throughout their careers, many soldiers experience repeated blasts exposures from improvised explosive devices, which often involve head injury. Consequentially, blast-related mild Traumatic Brain Injury (mTBI) has become prevalent in modern conflicts, often occuring co-morbidly with psychiatric illness (e.g., post-traumatic stress disorder [PTSD]). In turn, a growing body of research has begun to explore the cognitive and psychiatric sequelae of blast-related mTBI. The current meta-analysis aimed to evaluate the chronic effects of blast-related mTBI on cognitive performance. A systematic review identified 9 studies reporting 12 samples meeting eligibility criteria. A Bayesian random-effects meta-analysis was conducted with cognitive construct and PTSD symptoms explored as moderators. The overall posterior mean effect size and Highest Density Interval (HDI) came to d = -0.12 [-0.21, -0.04], with executive function (-0.16 [-0.31, 0.00]), verbal delayed memory (-0.19 [-0.44, 0.06]) and processing speed (-0.11 [-0.26, 0.01]) presenting as the most sensitive cognitive domains to blast-related mTBI. When dividing executive function into diverse sub-constructs (i.e., working memory, inhibition, set-shifting), set-shifting presented the largest effect size (-0.33 [-0.55, -0.05]). PTSD symptoms did not predict cognitive effects sizes, β PTSD  = -0.02 [-0.23, 0.20]. The results indicate a subtle, but chronic cognitive impairment following mTBI, especially in set-shifting, a relevant aspect of executive attention. These findings are consistent with past meta-analyses on multiple mTBI and correspond with past neuroimaging research on the cognitive correlates of white matter damage common in mTBI. However, all studies had cross-sectional designs, which resulted in universally low quality ratings and limited the conclusions inferable from this meta-analysis.

Bayesian network modelling of upper gastrointestinal bleeding

NASA Astrophysics Data System (ADS)

Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

2013-09-01

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

Bayesian network prior: network analysis of biological data using external knowledge

PubMed Central

Isci, Senol; Dogan, Haluk; Ozturk, Cengizhan; Otu, Hasan H.

2014-01-01

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods. Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP. Contact: hasan.otu@bilgi.edu.tr Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24215027

Comparative efficacy of disease-modifying therapies for patients with relapsing remitting multiple sclerosis: Systematic review and network meta-analysis.

PubMed

Fogarty, Emer; Schmitz, Susanne; Tubridy, Niall; Walsh, Cathal; Barry, Michael

2016-09-01

Randomised studies have demonstrated efficacy of disease-modifying therapies in relapsing remitting multiple sclerosis (RRMS). However it is unclear how the magnitude of treatment efficacy varies across all currently available therapies. To perform a systematic review and network meta-analysis to evaluate the comparative efficacy of available therapies in reducing relapses and disability progression in RRMS. A systematic review identified 28 randomised, placebo-controlled and direct comparative trials. A network meta-analysis was conducted within a Bayesian framework to estimate comparative annualised relapse rates (ARR) and risks of disability progression (defined by both a 3-month, and 6-month confirmation interval). Potential sources of treatment-effect modification from study-level covariates and baseline risk were evaluated through meta-regression methods. The Surface Under the Cumulative RAnking curve (SUCRA) method was used to provide a ranking of treatments for each outcome. The magnitude of ARR reduction varied between 15-36% for all interferon-beta products, glatiramer acetate and teriflunomide, and from 50 to 69% for alemtuzumab, dimethyl fumarate, fingolimod and natalizumab. The risk of disability progression (3-month) was reduced by 19-28% with interferon-beta products, glatiramer acetate, fingolimod and teriflunomide, by 38-45% for pegylated interferon-beta, dimethyl fumarate and natalizumab and by 68% with alemtuzumab. Broadly similar estimates for the risk of disability progression (6-month), with the exception of interferon-beta-1b 250mcg which was much more efficacious based on this definition. Alemtuzumab and natalizumab had the highest SUCRA scores (97% and 95% respectively) for ARR, while ranking for disability progression varied depending on the definition of the outcome. Interferon-beta-1b 250mcg ranked among the most efficacious treatments for disability progression confirmed after six months (92%) and among the least efficacious when the outcome was confirmed after three months (30%). No significant modification of relative treatment effects was identified from study-level covariates or baseline risk. Compared with placebo, clear reductions in ARR with disease-modifying therapies were accompanied by more uncertain changes in disability progression. The magnitude of the reduction and the uncertainty associated with treatment effects varied between DMTs. While natalizumab and alemtuzumab demonstrated consistently high ranking across outcomes, with older interferon-beta and glatiramer acetate products ranking lowest, variation in disability progression definitions lead to variation in the relative ranking of treatments. Rigorously conducted comparative studies are required to fully evaluate the comparative treatment effects of disease modifying therapies for RRMS. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparison of sedation strategies for critically ill patients: a protocol for a systematic review incorporating network meta-analyses.

PubMed

Hutton, Brian; Burry, Lisa D; Kanji, Salmaan; Mehta, Sangeeta; Guenette, Melanie; Martin, Claudio M; Fergusson, Dean A; Adhikari, Neill K; Egerod, Ingrid; Williamson, David; Straus, Sharon; Moher, David; Ely, E Wesley; Rose, Louise

2016-09-20

Sedatives and analgesics are administered to provide sedation and manage agitation and pain in most critically ill mechanically ventilated patients. Various sedation administration strategies including protocolized sedation and daily sedation interruption are used to mitigate drug pharmacokinetic limitations and minimize oversedation, thereby shortening the duration of mechanical ventilation. At present, it is unclear which strategy is most effective, as few have been directly compared. Our review will use network meta-analysis (NMA) to compare and rank sedation strategies to determine their efficacy and safety for mechanically ventilated patients. We will search the following from 1980 to March 2016: Ovid MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science. We will also search the Cochrane Library, gray literature, and the International Clinical Trials Registry Platform. We will use a validated randomized control trial search filter to identify studies evaluating any strategy to optimize sedation in mechanically ventilated adult patients. Authors will independently extract data from eligible studies in duplicate and complete the Cochrane Risk of Bias tool. Our outcomes of interest include duration of mechanical ventilation, time to first extubation, ICU and hospital length of stay, re-intubation, tracheostomy, mortality, total sedative and opioid exposure, health-related quality of life, and adverse events. To inform our NMA, we will first conduct conventional pair-wise meta-analyses using random-effects models. Where appropriate, we will perform Bayesian NMA using WinBUGS software. There are multiple strategies to optimize sedation for mechanically ventilated patients. Current ICU guidelines recommend protocolized sedation or daily sedation interruption. Our systematic review incorporating NMA will provide a unified analysis of all sedation strategies to determine the relative efficacy and safety of interventions that may not have been compared directly. We will provide knowledge users, decision makers, and professional societies with ranking of multiple sedation strategies to inform future sedation guidelines. PROSPERO CRD42016037480.

Empirical evidence about inconsistency among studies in a pair-wise meta-analysis.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; Higgins, Julian P T

2016-12-01

This paper investigates how inconsistency (as measured by the I 2 statistic) among studies in a meta-analysis may differ, according to the type of outcome data and effect measure. We used hierarchical models to analyse data from 3873 binary, 5132 continuous and 880 mixed outcome meta-analyses within the Cochrane Database of Systematic Reviews. Predictive distributions for inconsistency expected in future meta-analyses were obtained, which can inform priors for between-study variance. Inconsistency estimates were highest on average for binary outcome meta-analyses of risk differences and continuous outcome meta-analyses. For a planned binary outcome meta-analysis in a general research setting, the predictive distribution for inconsistency among log odds ratios had median 22% and 95% CI: 12% to 39%. For a continuous outcome meta-analysis, the predictive distribution for inconsistency among standardized mean differences had median 40% and 95% CI: 15% to 73%. Levels of inconsistency were similar for binary data measured by log odds ratios and log relative risks. Fitted distributions for inconsistency expected in continuous outcome meta-analyses using mean differences were almost identical to those using standardized mean differences. The empirical evidence on inconsistency gives guidance on which outcome measures are most likely to be consistent in particular circumstances and facilitates Bayesian meta-analysis with an informative prior for heterogeneity. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

The Meta-Analytic Big Bang

ERIC Educational Resources Information Center

Shadish, William R.; Lecy, Jesse D.

2015-01-01

This article looks at the impact of meta-analysis and then explores why meta-analysis was developed at the time and by the scholars it did in the social sciences in the 1970s. For the first problem, impact, it examines the impact of meta-analysis using citation network analysis. The impact is seen in the sciences, arts and humanities, and on such…

Pharmacologic therapies for severe steroid refractory hospitalized ulcerative colitis: A network meta-analysis.

PubMed

Komaki, Yuga; Komaki, Fukiko; Micic, Dejan; Yamada, Akihiro; Suzuki, Yasuo; Sakuraba, Atsushi

2017-06-01

A limited option of therapies is available for hospitalized patients with severe steroid refractory ulcerative colitis (UC). Furthermore, there exists a paucity of direct comparisons between them. To provide a comparative evaluation of the efficacy and safety of pharmacologic therapies, we conducted a network meta-analysis combined with a benefit-risk analysis of randomized controlled trials (RCTs) performed in hospitalized patients with severe steroid refractory UC. Electronic databases were searched through November 2015 for RCTs evaluating the efficacy of therapies for severe steroid refractory hospitalized UC. The outcomes were clinical response, colectomy free rate, and severe adverse events leading to discontinuation of therapy. The primary endpoints were the rank of therapies based on network meta-analysis combined with benefit-risk analysis between clinical response and severe adverse events as well as colectomy free rate and severe adverse events. Eight RCTs of 421 patients were identified. Cyclosporine, infliximab, and tacrolimus as well as placebo were included in our analysis. Network meta-analysis with benefit-risk analysis simultaneously assessing clinical response and severe adverse events demonstrated the rank order of efficacy as infliximab, cyclosporine, tacrolimus, and placebo. Similar analysis for colectomy-free rate and severe adverse events demonstrated the same rank order of efficacy. The differences among infliximab, cyclosporine, and tacrolimus were small in all analyses. The results of the present comprehensive benefit-risk assessment using network meta-analysis provide RCT-based evidence on efficacy and safety of infliximab, cyclosporine, and tacrolimus for hospitalized patients with severe steroid refractory UC. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

The meta-analytic big bang.

PubMed

Shadish, William R; Lecy, Jesse D

2015-09-01

This article looks at the impact of meta-analysis and then explores why meta-analysis was developed at the time and by the scholars it did in the social sciences in the 1970s. For the first problem, impact, it examines the impact of meta-analysis using citation network analysis. The impact is seen in the sciences, arts and humanities, and on such contemporaneous developments as multilevel modeling, medical statistics, qualitative methods, program evaluation, and single-case design. Using a constrained snowball sample of citations, we highlight key articles that are either most highly cited or most central to the systematic review network. Then, the article examines why meta-analysis came to be in the 1970s in the social sciences through the work of Gene Glass, Robert Rosenthal, and Frank Schmidt, each of whom developed similar theories of meta-analysis at about the same time. The article ends by explaining how Simonton's chance configuration theory and Campbell's evolutionary epistemology can illuminate why meta-analysis occurred with these scholars when it did and not in medical sciences. Copyright © 2015 John Wiley & Sons, Ltd.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Bayesian survival analysis in clinical trials: What methods are used in practice?

PubMed

Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

2017-02-01

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

Continuously updated network meta-analysis and statistical monitoring for timely decision-making

PubMed Central

Nikolakopoulou, Adriani; Mavridis, Dimitris; Egger, Matthias; Salanti, Georgia

2016-01-01

Pairwise and network meta-analysis (NMA) are traditionally used retrospectively to assess existing evidence. However, the current evidence often undergoes several updates as new studies become available. In each update recommendations about the conclusiveness of the evidence and the need of future studies need to be made. In the context of prospective meta-analysis future studies are planned as part of the accumulation of the evidence. In this setting, multiple testing issues need to be taken into account when the meta-analysis results are interpreted. We extend ideas of sequential monitoring of meta-analysis to provide a methodological framework for updating NMAs. Based on the z-score for each network estimate (the ratio of effect size to its standard error) and the respective information gained after each study enters NMA we construct efficacy and futility stopping boundaries. A NMA treatment effect is considered conclusive when it crosses an appended stopping boundary. The methods are illustrated using a recently published NMA where we show that evidence about a particular comparison can become conclusive via indirect evidence even if no further trials address this comparison. PMID:27587588

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

An innovative and comprehensive technique to evaluate different measures of medication adherence: The network meta-analysis.

PubMed

Tonin, Fernanda S; Wiecek, Elyssa; Torres-Robles, Andrea; Pontarolo, Roberto; Benrimoj, Shalom Charlie I; Fernandez-Llimos, Fernando; Garcia-Cardenas, Victoria

2018-05-19

Poor medication adherence is associated with adverse health outcomes and higher costs of care. However, inconsistencies in the assessment of adherence are found in the literature. To evaluate the effect of different measures of adherence in the comparative effectiveness of complex interventions to enhance patients' adherence to prescribed medications. A systematic review with network meta-analysis was performed. Electronic searches for relevant pairwise meta-analysis including trials of interventions that aimed to improve medication adherence were performed in PubMed. Data extraction was conducted with eligible trials evaluating short-period adherence follow-up (until 3 months) using any measure of adherence: self-report, pill count, or MEMS (medication event monitoring system). To standardize the results obtained with these different measures, an overall composite measure and an objective composite measure were also calculated. Network meta-analyses for each measure of adherence were built. Rank order and surface under the cumulative ranking curve analyses (SUCRA) were performed. Ninety-one trials were included in the network meta-analyses. The five network meta-analyses demonstrated robustness and reliability. Results obtained for all measures of adherence were similar across them and to both composite measures. For both composite measures, interventions comprising economic + technical components were the best option (90% of probability in SUCRA analysis) with statistical superiority against almost all other interventions and against standard care (odds ratio with 95% credibility interval ranging from 0.09 to 0.25 [0.02, 0.98]). The use of network meta-analysis was reliable to compare different measures of adherence of complex interventions in short-periods follow-up. Analyses with longer follow-up periods are needed to confirm these results. Different measures of adherence produced similar results. The use of composite measures revealed reliable alternatives to establish a broader and more detailed picture of adherence. Copyright © 2018 Elsevier Inc. All rights reserved.

Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews

PubMed Central

Turner, Rebecca M; Davey, Jonathan; Clarke, Mike J; Thompson, Simon G; Higgins, Julian PT

2012-01-01

Background Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of heterogeneity in particular areas of health care. Methods Our analyses included 14 886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-study heterogeneity variance. Predictive distributions were obtained for the heterogeneity expected in future meta-analyses. Results Between-study heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10–26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, heterogeneity was on average 75% (95% CI 58–95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for heterogeneity is a log-normal (−2.13, 1.582) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller heterogeneity estimate and a narrower confidence interval for the combined intervention effect. Conclusions Meta-analysis characteristics were strongly associated with the degree of between-study heterogeneity, and predictive distributions for heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies. PMID:22461129

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

Effects of intranasal oxytocin on symptoms of schizophrenia: A multivariate Bayesian meta-analysis.

PubMed

Williams, Donald R; Bürkner, Paul-Christian

2017-01-01

Schizophrenia is a heterogeneous disorder in which psychiatric symptoms are classified into two general subgroups-positive and negative symptoms. Current antipsychotic drugs are effective for treating positive symptoms, whereas negative symptoms are less responsive. Since the neuropeptide oxytocin (OT) has been shown to mediate social behavior in animals and humans, it has been used as an experimental therapeutic for treating schizophrenia and in particular negative symptoms which includes social deficits. Through eight randomized controlled trials (RCTs) and three meta-analyses, evidence for an effect of intranasal OT (IN-OT) has been inconsistent. We therefore conducted an updated meta-analysis that offers several advantages when compared to those done previously: (1) We used a multivariate analysis which allows for comparisons between symptoms and accounts for correlations between symptoms; (2) We controlled for baseline scores; (3) We used a fully Bayesian framework that allows for assessment of evidence in favor of the null hypothesis using Bayes factors; and (4) We addressed inconsistencies in the primary studies and previous meta-analyses. Eight RCTs (n=238) were included in the present study and we found that oxytocin did not improve any aspect of symptomology in schizophrenic patients and there was moderate evidence in favor of the null (no effect of oxytocin) for negative symptoms. Multivariate comparisons between symptom types revealed that oxytocin was not especially beneficial for treating negative symptoms. The effect size estimates were not moderated, publication bias was absent, and our estimates were robust to sensitivity analyses. These results suggest that IN-OT is not an effective therapeutic for schizophrenia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

PubMed

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

PubMed Central

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

Mechanical ventilation strategies for intensive care unit patients without acute lung injury or acute respiratory distress syndrome: a systematic review and network meta-analysis.

PubMed

Guo, Lei; Wang, Weiwei; Zhao, Nana; Guo, Libo; Chi, Chunjie; Hou, Wei; Wu, Anqi; Tong, Hongshuang; Wang, Yue; Wang, Changsong; Li, Enyou

2016-07-22

It has been shown that the application of a lung-protective mechanical ventilation strategy can improve the prognosis of patients with acute lung injury (ALI) or acute respiratory distress syndrome (ARDS). However, the optimal mechanical ventilation strategy for intensive care unit (ICU) patients without ALI or ARDS is uncertain. Therefore, we performed a network meta-analysis to identify the optimal mechanical ventilation strategy for these patients. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library, EMBASE, MEDLINE, CINAHL, and Web of Science for studies published up to July 2015 in which pulmonary compliance or the partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FIO2) ratio was assessed in ICU patients without ALI or ARDS, who received mechanical ventilation via different strategies. The data for study characteristics, methods, and outcomes were extracted. We assessed the studies for eligibility, extracted the data, pooled the data, and used a Bayesian fixed-effects model to combine direct comparisons with indirect evidence. Seventeen randomized controlled trials including a total of 575 patients who received one of six ventilation strategies were included for network meta-analysis. Among ICU patients without ALI or ARDS, strategy C (lower tidal volume (VT) + higher positive end-expiratory pressure (PEEP)) resulted in the highest PaO2/FIO2 ratio; strategy B (higher VT + lower PEEP) was associated with the highest pulmonary compliance; strategy A (lower VT + lower PEEP) was associated with a shorter length of ICU stay; and strategy D (lower VT + zero end-expiratory pressure (ZEEP)) was associated with the lowest PaO2/FiO2 ratio and pulmonary compliance. For ICU patients without ALI or ARDS, strategy C (lower VT + higher PEEP) was associated with the highest PaO2/FiO2 ratio. Strategy B (higher VT + lower PEEP) was superior to the other strategies in improving pulmonary compliance. Strategy A (lower VT + lower PEEP) was associated with a shorter length of ICU stay, whereas strategy D (lower VT + ZEEP) was associated with the lowest PaO2/FiO2 ratio and pulmonary compliance.

Chemoprevention of colorectal cancer in individuals with previous colorectal neoplasia: systematic review and network meta-analysis

PubMed Central

Dulai, Parambir S; Marquez, Evelyn; Khera, Rohan; Prokop, Larry J; Limburg, Paul J; Gupta, Samir; Murad, Mohammad Hassan

2016-01-01

Objective To assess the comparative efficacy and safety of candidate agents (low and high dose aspirin, non-aspirin non-steroidal anti-inflammatory drugs (NSAIDs), calcium, vitamin D, folic acid, alone or in combination) for prevention of advanced metachronous neoplasia (that is, occurring at different times after resection of initial neoplasia) in individuals with previous colorectal neoplasia, through a systematic review and network meta-analysis. Data sources Medline, Embase, Web of Science, from inception to 15 October 2015; clinical trial registries. Study selection Randomized controlled trials in adults with previous colorectal neoplasia, treated with candidate chemoprevention agents, and compared with placebo or another candidate agent. Primary efficacy outcome was risk of advanced metachronous neoplasia; safety outcome was serious adverse events. Data extraction Two investigators identified studies and abstracted data. A Bayesian network meta-analysis was performed and relative ranking of agents was assessed with surface under the cumulative ranking (SUCRA) probabilities (ranging from 1, indicating that the treatment has a high likelihood to be best, to 0, indicating the treatment has a high likelihood to be worst). Quality of evidence was appraised with GRADE criteria. Results 15 randomized controlled trials (12 234 patients) comparing 10 different strategies were included. Compared with placebo, non-aspirin NSAIDs were ranked best for preventing advanced metachronous neoplasia (odds ratio 0.37, 95% credible interval 0.24 to 0.53; SUCRA=0.98; high quality evidence), followed by low-dose aspirin (0.71, 0.41 to 1.23; SUCRA=0.67; low quality evidence). Low dose aspirin, however, was ranked the safest among chemoprevention agents (0.78, 0.43 to 1.38; SUCRA=0.84), whereas non-aspirin NSAIDs (1.23, 0.95 to 1.64; SUCRA=0.26) were ranked low for safety. High dose aspirin was comparable with low dose aspirin in efficacy (1.12, 0.59 to 2.10; SUCRA=0.58) but had an inferior safety profile (SUCRA=0.51). Efficacy of agents for reducing metachronous colorectal cancer could not be estimated. Conclusions Among individuals with previous colorectal neoplasia, non-aspirin NSAIDs are the most effective agents for the prevention of advanced metachronous neoplasia, whereas low dose aspirin has the most favorable risk:benefit profile. Registration PROSPERO (CRD42015029598). PMID:27919915

Effect of Surgical Intervention for Removal of Mandibular Third Molar on Periodontal Healing of Adjacent Mandibular Second Molar: A Systematic Review and Bayesian Network Meta-Analysis.

PubMed

Barbato, Luigi; Kalemaj, Zamira; Buti, Jacopo; Baccini, Michela; La Marca, Michele; Duvina, Marco; Tonelli, Paolo

2016-03-01

The aim of this systematic review is to evaluate and synthesize scientific evidence on the effect of surgical interventions for removal of mandibular third molar (M3M) on periodontal healing of adjacent mandibular second molar (M2M). The protocol was registered at PROSPERO (International Prospective Register of Systematic Reviews) as CRD42012003059. Medline, Cochrane, and EMBASE databases were interrogated to identify randomized controlled trials (RCTs) up to December 22, 2014. Patients with M3Ms fully developed, unilaterally or bilaterally impacted, were considered. Outcomes were clinical attachment level gain (CALg) and probing depth reduction (PDr) with a follow-up ≥ 6 months. Patient-subjective outcomes, such as pain, discomfort, and complications, and financial aspects and chair time, were also explored. A Bayesian network meta-analysis model was used to estimate direct and indirect effects and to establish a ranking of treatments. Sixteen RCTs were included and categorized into four groups investigating the following: 1) regenerative/grafting procedures (10 RCTs); 2) flap design (three RCTs); 3) type of suturing (one RCT); and 4) periodontal care of M2M (two RCTs). Guided tissue regeneration (GTR) with resorbable (GTRr) and non-resorbable (GTRnr) membrane and GTRr with anorganic xenograft (GTRr + AX) showed the highest mean ranking for CALg (2.99, 90% credible interval [CrI] = 1 to 5; 2.80, 90% CrI = 1 to 6; and 2.29, 90% CrI = 1 to 6, respectively) and PDr (2.83, 90% CrI = 1 to 5; 2.52, 90% CrI = 1 to 5; and 2.77, 90% CrI = 1 to 6, respectively). GTRr + AX showed the highest probability (Pr) of being the best treatment for CALg (Pr = 45%) and PDr (Pr = 32%). Direct and network quality of evidence were rated from very low to moderate. To the best of the authors' knowledge, the present review is the first one to evaluate quantitatively and qualitatively the effect of different interventions on periodontal healing distal to the second molar after extraction of the third molar. GTR-based procedures with or without combined grafting therapies provide some adjunctive clinical benefit compared to standard non-regenerative/non-grafting procedures. However, the overall low quality of evidence suggests a low degree of confidence and certainty in treatment effects. Evidence on variations of surgical M3M removal techniques based on flap design, type of suturing, and periodontal care of M2M is limited both qualitatively and quantitatively.

Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension.

PubMed

Thom, Howard H Z; Capkun, Gorana; Cerulli, Annamaria; Nixon, Richard M; Howard, Luke S

2015-04-12

Network meta-analysis (NMA) is a methodology for indirectly comparing, and strengthening direct comparisons of two or more treatments for the management of disease by combining evidence from multiple studies. It is sometimes not possible to perform treatment comparisons as evidence networks restricted to randomized controlled trials (RCTs) may be disconnected. We propose a Bayesian NMA model that allows to include single-arm, before-and-after, observational studies to complete these disconnected networks. We illustrate the method with an indirect comparison of treatments for pulmonary arterial hypertension (PAH). Our method uses a random effects model for placebo improvements to include single-arm observational studies into a general NMA. Building on recent research for binary outcomes, we develop a covariate-adjusted continuous-outcome NMA model that combines individual patient data (IPD) and aggregate data from two-arm RCTs with the single-arm observational studies. We apply this model to a complex comparison of therapies for PAH combining IPD from a phase-III RCT of imatinib as add-on therapy for PAH and aggregate data from RCTs and single-arm observational studies, both identified by a systematic review. Through the inclusion of observational studies, our method allowed the comparison of imatinib as add-on therapy for PAH with other treatments. This comparison had not been previously possible due to the limited RCT evidence available. However, the credible intervals of our posterior estimates were wide so the overall results were inconclusive. The comparison should be treated as exploratory and should not be used to guide clinical practice. Our method for the inclusion of single-arm observational studies allows the performance of indirect comparisons that had previously not been possible due to incomplete networks composed solely of available RCTs. We also built on many recent innovations to enable researchers to use both aggregate data and IPD. This method could be used in similar situations where treatment comparisons have not been possible due to restrictions to RCT evidence and where a mixture of aggregate data and IPD are available.

Systematic Review and Network Meta-analysis of Idiopathic Pulmonary Fibrosis Treatments.

PubMed

Fleetwood, Kelly; McCool, Rachael; Glanville, Julie; Edwards, Susan C; Gsteiger, Sandro; Daigl, Monica; Fisher, Mark

2017-03-01

The antifibrotics pirfenidone and nintedanib are both approved for the treatment of idiopathic pulmonary fibrosis (IPF) by regulatory agencies and are recommended by health technology assessment bodies. Other treatments such as N-acetylcysteine are used in clinical practice but have not received regulatory approval. No head-to-head trials have been conducted to directly compare the efficacy of these therapies in IPF. To compare the efficacy of treatments for IPF. A systematic review was conducted up to April 2015. Phase II/III randomized controlled trials in adults with IPF were eligible. A Bayesian network meta-analysis (NMA) was used to compare pirfenidone, nintedanib, and N-acetylcysteine with respect to forced vital capacity (FVC) and mortality. Nine studies were included in the NMA. For change from baseline in FVC, the NMA indicated that pirfenidone and nintedanib were more effective than placebo after 1 year (pirfenidone vs. placebo: difference = 0.12 liter (L), 95% credible interval [CrI] = 0.03-0.21 L; nintedanib vs. placebo: difference = 0.11 L, 95% CrI = 0.00-0.22 L). There was no evidence that N-acetylcysteine had an effect on FVC compared with placebo (N-acetylcysteine vs. placebo: difference = 0.01 L, 95% CrI = -0.15-0.17 L). Patients treated with pirfenidone also had a lower risk of experiencing a decline in percent predicted FVC of ≥ 10% over 1 year (odds ratio [OR]: 0.58, 95% CrI = 0.40-0.88), whereas there was no conclusive evidence of a difference between nintedanib and placebo (OR: 0.65, 95% CrI = 0.42-1.02). The NMA indicated that pirfenidone reduced all-cause mortality relative to placebo over 1 year (hazard ratio [HR]: 0.52, 95% CrI = 0.28-0.92). There was no evidence of a difference in all-cause mortality between nintedanib and placebo (HR: 0.70, 95% CrI = 0.32-1.55), or N-acetylcysteine and placebo (HR: 2.00, 95% CrI=0.46-8.62). Our primary analysis of the available evidence indicates that over 1 year, pirfenidone and nintedanib are effective at reducing lung-function decline, and pirfenidone may reduce the odds of experiencing a decline in percent predicted FVC of ≥10% compared with placebo in the first year of treatment. The results of our analysis also suggest that pirfenidone improves survival. Fleetwood is an employee of Quantics Consulting. McCool and Glanville are employees of York Health Economics Consortium (YHEC). Quantics and YHEC received funding from F. Hoffmann-La Roche for conducting the systematic review and network meta-analysis reported in this paper. Edwards, Gsteiger, and Daigl are employees of F. Hoffmann-La Roche. Fisher was employed by InterMune UK, a wholly owned Roche subsidiary, until July 2015. He is currently employed by FIECON, which has received funding from F. Hoffmann-La Roche for consulting services. The systematic review and network meta-analysis reported in this paper were conducted by Fleetwood (Quantics Consulting) and McCool and Glanville (YHEC), funded by F. Hoffmann-La Roche. The original network analysis was funded by InterMune. Study concept and design were contributed by Edwards, Gsteiger, and Daigl, along with Fleetwood, McCool, and Glanville. Fleetwood, McCool, and Glanville collected the data, with assistance from Edwards, Gsteiger, and Daigl. Data interpretation was performed by Fleetwood and Fisher, with assistance from the other authors. The manuscript was written by Fleetwood, McCool, and Glanville, with assistance from Edwards, Daigl, and Fisher, and revised by all the authors.

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

PubMed Central

Wu, Xia; Yu, Xinyu; Yao, Li; Li, Rui

2014-01-01

Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states. PMID:25309414

Hypertensive disorders during pregnancy and risk of type 2 diabetes in later life: a systematic review and meta-analysis.

PubMed

Wang, Zengfang; Wang, Zengyan; Wang, Luang; Qiu, Mingyue; Wang, Yangang; Hou, Xu; Guo, Zhong; Wang, Bin

2017-03-01

Many studies assessed the association between hypertensive disorders during pregnancy and risk of type 2 diabetes mellitus in later life, but contradictory findings were reported. A systemic review and meta-analysis was carried out to elucidate type 2 diabetes mellitus risk in women with hypertensive disorders during pregnancy. Pubmed, Embase, and Web of Science were searched for cohort or case-control studies on the association between hypertensive disorders during pregnancy and subsequent type 2 diabetes mellitus. Random-effect model was used to pool risk estimates. Bayesian meta-analysis was carried out to further estimate the type 2 diabetes mellitus risk associated with hypertensive disorders during pregnancy. Seventeen cohort or prospective matched case-control studies were finally included. Those 17 studies involved 2,984,634 women and 46,732 type 2 diabetes mellitus cases. Overall, hypertensive disorders during pregnancy were significantly correlated with type 2 diabetes mellitus risk (relative risk = 1.56, 95 % confidence interval 1.21-2.01, P = 0.001). Preeclampsia was significantly and independently correlated with type 2 diabetes mellitus risk (relative risk = 2.25, 95 % confidence interval 1.73-2.90, P < 0.001). In addition, gestational hypertension was also significantly and independently correlated with subsequent type 2 diabetes mellitus risk (relative risk = 2.06, 95 % confidence interval 1.57-2.69, P < 0.001). The pooled estimates were not significantly altered in the subgroup analyses of studies on preeclampsia or gestational hypertension. Bayesian meta-analysis showed the relative risks of type 2 diabetes mellitus risk for individuals with hypertensive disorders during pregnancy, preeclampsia, and gestational hypertension were 1.59 (95 % credibility interval: 1.11-2.32), 2.27 (95 % credibility interval: 1.67-2.97), and 2.06 (95 % credibility interval: 1.41-2.84), respectively. Publication bias was not evident in the meta-analysis. Preeclampsia and gestational hypertension are independently associated with substantially elevated risk of type 2 diabetes mellitus in later life.

Evaluation of shoulder function in clavicular fracture patients after six surgical procedures based on a network meta-analysis.

PubMed

Huang, Shou-Guo; Chen, Bo; Lv, Dong; Zhang, Yong; Nie, Feng-Feng; Li, Wei; Lv, Yao; Zhao, Huan-Li; Liu, Hong-Mei

2017-01-01

Purpose Using a network meta-analysis approach, our study aims to develop a ranking of the six surgical procedures, that is, Plate, titanium elastic nail (TEN), tension band wire (TBW), hook plate (HP), reconstruction plate (RP) and Knowles pin, by comparing the post-surgery constant shoulder scores in patients with clavicular fracture (CF). Methods A comprehensive search of electronic scientific literature databases was performed to retrieve publications investigating surgical procedures in CF, with the stringent eligible criteria, and clinical experimental studies of high quality and relevance to our area of interest were selected for network meta-analysis. Statistical analyses were conducted using Stata 12.0. Results A total of 19 studies met our inclusion criteria were eventually enrolled into our network meta-analysis, representing 1164 patients who had undergone surgical procedures for CF (TEN group = 240; Plate group = 164; TBW group  =  180; RP group  =  168; HP group  =  245; Knowles pin group  =  167). The network meta-analysis results revealed that RP significantly improved constant shoulder score in patients with CF when compared with TEN, and the post-operative constant shoulder scores in patients with CF after Plate, TBW, HP, Knowles pin and TEN were similar with no statistically significant differences. The treatment relative ranking of predictive probabilities of constant shoulder scores in patients with CF after surgery revealed the surface under the cumulative ranking curves (SUCRA) value is the highest in RP. Conclusion The current network meta-analysis suggests that RP may be the optimum surgical treatment among six inventions for patients with CF, and it can improve the shoulder score of patients with CF. Implications for Rehabilitation RP improves shoulder joint function after surgical procedure. RP achieves stability with minimal complications after surgery. RP may be the optimum surgical treatment for rehabilitation of patients with CF.

Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification

PubMed Central

Yan, Fang; Xu, Kaili; Yao, Xiwen; Li, Yang

2016-01-01

Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures. PMID:27463975

Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

PubMed Central

Antal, Péter; Kiszel, Petra Sz.; Gézsi, András; Hadadi, Éva; Virág, Viktor; Hajós, Gergely; Millinghoffer, András; Nagy, Adrienne; Kiss, András; Semsei, Ágnes F.; Temesi, Gergely; Melegh, Béla; Kisfali, Péter; Széll, Márta; Bikov, András; Gálffy, Gabriella; Tamási, Lilla; Falus, András; Szalai, Csaba

2012-01-01

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance. PMID:22432035

Narcissism and Social Networking Behavior: A Meta-Analysis.

PubMed

Gnambs, Timo; Appel, Markus

2018-04-01

The increasing popularity of social networking sites (SNS) such as Facebook and Twitter has given rise to speculations that the intensity of using these platforms is associated with narcissistic tendencies. However, recent research on this issue has been all but conclusive. We present a three-level, random effects meta-analysis including 289 effect sizes from 57 studies (total N = 25,631) on the association between trait narcissism and social networking behavior. The meta-analysis identified a small to moderate effect of ρ = .17 (τ = .11), 95% CI [.13, .21], for grandiose narcissism that replicated across different social networking platforms, respondent characteristics, and time. Moderator analyses revealed pronounced cultural differences, with stronger associations in power-distant cultures. Moreover, social networking behaviors geared toward self-presentation and the number of SNS friends exhibited stronger effects than usage durations. Overall, the study not only supported but also refined the notion of a relationship between engaging in social networking sites and narcissistic personality traits. © 2017 Wiley Periodicals, Inc.

Systematic review and network meta-analysis comparing clinical outcomes and effectiveness of surgical treatments for haemorrhoids.

PubMed

Simillis, C; Thoukididou, S N; Slesser, A A P; Rasheed, S; Tan, E; Tekkis, P P

2015-12-01

The aim was to compare the clinical outcomes and effectiveness of surgical treatments for haemorrhoids. Randomized clinical trials were identified by means of a systematic review. A Bayesian network meta-analysis was performed using the Markov chain Monte Carlo method in WinBUGS. Ninety-eight trials were included with 7827 participants and 11 surgical treatments for grade III and IV haemorrhoids. Open, closed and radiofrequency haemorrhoidectomies resulted in significantly more postoperative complications than transanal haemorrhoidal dearterialization (THD), LigaSure™ and Harmonic® haemorrhoidectomies. THD had significantly less postoperative bleeding than open and stapled procedures, and resulted in significantly fewer emergency reoperations than open, closed, stapled and LigaSure™ haemorrhoidectomies. Open and closed haemorrhoidectomies resulted in more pain on postoperative day 1 than stapled, THD, LigaSure™ and Harmonic® procedures. After stapled, LigaSure™ and Harmonic® haemorrhoidectomies patients resumed normal daily activities earlier than after open and closed procedures. THD provided the earliest time to first bowel movement. The stapled and THD groups had significantly higher haemorrhoid recurrence rates than the open, closed and LigaSure™ groups. Recurrence of haemorrhoidal symptoms was more common after stapled haemorrhoidectomy than after open and LigaSure™ operations. No significant difference was identified between treatments for anal stenosis, incontinence and perianal skin tags. Open and closed haemorrhoidectomies resulted in more postoperative complications and slower recovery, but fewer haemorrhoid recurrences. THD and stapled haemorrhoidectomies were associated with decreased postoperative pain and faster recovery, but higher recurrence rates. The advantages and disadvantages of each surgical treatment should be discussed with the patient before surgery to allow an informed decision to be made. © 2015 BJS Society Ltd Published by John Wiley & Sons Ltd.

Antithrombotic Treatment for Recurrent Miscarriage

PubMed Central

Zhang, Tianyi; Ye, Xiaofei; Zhu, Tiantian; XIAO, Xiang; Liu, Yuzhou; Wei, Xin; Liu, Yu; Wu, Cheng; Guan, Rui; Li, Xiao; Guo, Xiaojing; Hu, Huili; He, Jia

2015-01-01

Abstract Combined use of heparin and aspirin is frequently prescribed for treatment of recurrent miscarriage (RM) in patients with antiphospholipid syndrome (APS), or in those without apparent cause of RM other than thrombophilia; however, this strategy is largely based on expert opinion and has not been well studied. The option for the use of different antithrombotic therapies to improve live birth remains unclear. In this network meta-analysis, we incorporated direct and indirect evidence to evaluate effects of different antithrombotic treatments on prevention of pregnancy losses. We searched PubMed and Embase for randomized clinical trials comparing effects of at least 2 antithrombotic treatments on live birth in RM patients published from 1965 through the early of May 2015. Potential risk bias of eligible trials was evaluated according to the Cochrane Collaboration guidelines. Bayesian network meta-analysis was used to estimate relative effects on live birth. A total of 19 trials involving 2391 RM patients with or without thrombophilia and 543 with APS were included. No beneficial effect of antithrombotic treatment was observed either in RM patients with or without thrombophilia or in patients with APS; however, for patients with or without thrombophilia, low molecular weight heparin therapy had the greatest probability (61.48%) of being the best option in terms of live birth; for patients with APS, unfractionated heparin plus aspirin was the superior treatment for RM with the highest possibility (75.15%) of being top 2 places for reducing pregnancy losses. Aspirin was inferior in both groups. Our results do not support the use of combined low molecular weight heparin and aspirin for RM treatment, and suggested aspirin may have negative effects for lowering the risk of pregnancy loss. PMID:26559249

Hierarchical cortical transcriptome disorganization in autism.

PubMed

Lombardo, Michael V; Courchesne, Eric; Lewis, Nathan E; Pramparo, Tiziano

2017-01-01

Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology. Two ASD cortical transcriptome datasets were re-analyzed using consensus weighted gene co-expression network analysis (WGCNA) to identify common co-expression modules across datasets. Linear mixed-effect models and Bayesian replication statistics were used to identify replicable differentially expressed modules. Eigengene network analysis was then utilized to identify between-group differences in how co-expression modules interact and cluster into hierarchical meta-modular organization. Protein-protein interaction analyses were also used to determine whether dysregulated co-expression modules show enhanced interactions. We find replicable evidence for 10 gene co-expression modules that are differentially expressed in ASD cortex. Rather than being independent non-interacting sources of pathology, these dysregulated co-expression modules work in synergy and physically interact at the protein level. These systems-level transcriptional signals are characterized by downregulation of synaptic processes coordinated with upregulation of immune/inflammation, response to other organism, catabolism, viral processes, translation, protein targeting and localization, cell proliferation, and vasculature development. Hierarchical organization of meta-modules (clusters of highly correlated modules) is also highly affected in ASD. These findings highlight that dysregulation of the ASD cortical transcriptome is characterized by the dysregulation of multiple coordinated transcriptional programs producing synergistic systems-level effects that cannot be fully appreciated by studying the individual component biological processes in isolation.

Safety Profile of Biologic Drugs in the Therapy of Ulcerative Colitis: A Systematic Review and Network Meta-Analysis.

PubMed

Moćko, Paweł; Kawalec, Paweł; Pilc, Andrzej

2016-08-01

We compared the safety profile of biologic drugs in patients with moderately to severely active ulcerative colitis (UC). A systematic literature search was performed using Medline (PubMed), Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) databases through February 9, 2016. We included randomized controlled trials (RCTs) that compared the safety of biologic drugs (infliximab, adalimumab, golimumab, and vedolizumab) with one another or with placebo in patients with UC. Two reviewers independently conducted the search and selection of studies and rated the risk of bias in each trial. The network meta-analysis (NMA) was conducted for an induction phase (6-8 weeks) and maintenance phase (52-54 weeks) with a Bayesian hierarchical random effects model in Aggregate Data Drug Information System (ADDIS) software. The PROSPERO registration number was CRD42016032607. Seven RCTs were included in the systematic review with NMA. In the case of the induction phase, the NMA could be conducted for the assessment of the relative safety profile of adalimumab, golimumab, and vedolizumab, and in the case of the maintenance phase of infliximab, adalimumab, golimumab, and vedolizumab. The methodological quality of the included RCTs was evaluated as low risk of bias, but high risk of bias in the case of attrition bias (incomplete outcome data) according to the Cochrane criteria. No significant differences were found in the rate of adverse events in patients treated with the reviewed biologics. Vedolizumab was most likely to have the most favorable safety profile in the induction phase as was infliximab for the maintenance phase. The assessment of the relative safety profile revealed no significant differences between the biologic drugs. Further studies are needed to confirm our findings including head-to-head comparisons between the analyzed biologics. © 2016 Pharmacotherapy Publications, Inc.

Pharmacological treatments in asthma-affected horses: A pair-wise and network meta-analysis.

PubMed

Calzetta, L; Roncada, P; di Cave, D; Bonizzi, L; Urbani, A; Pistocchini, E; Rogliani, P; Matera, M G

2017-11-01

Equine asthma is a disease characterised by reversible airflow obstruction, bronchial hyper-responsiveness and airway inflammation following exposure of susceptible horses to specific airborne agents. Although clinical remission can be achieved in a low-airborne dust environment, repeated exacerbations may lead to irreversible airway remodelling. The available data on the pharmacotherapy of equine asthma result from several small studies, and no head-to-head clinical trials have been conducted among the available medications. To assess the impact of the pharmacological interventions in equine asthma and compare the effect of different classes of drugs on lung function. Pair-wise and network meta-analysis. Literature searches for clinical trials on the pharmacotherapy of equine asthma were performed. The risk of publication bias was assessed by funnel plots and Egger's test. Changes in maximum transpulmonary or pleural pressure, pulmonary resistance and dynamic lung compliance vs. control were analysed via random-effects models and Bayesian networks. The results obtained from 319 equine asthma-affected horses were extracted from 32 studies. Bronchodilators, corticosteroids and chromones improved maximum transpulmonary or pleural pressure (range: -8.0 to -21.4 cmH 2 O; P<0.001). Bronchodilators, corticosteroids and furosemide reduced pulmonary resistance (range: -1.2 to -1.9 cmH 2 O/L/s; P<0.001), and weakly increased dynamic lung compliance. Inhaled β 2 -adrenoreceptor (β 2 -AR) agonists and inhaled corticosteroids had the highest probability of being the best therapies. Long-term treatments were more effective than short-term treatments. Weak publication bias was detected. This study demonstrates that long-term treatments with inhaled corticosteroids and long-acting β 2 -AR agonists may represent the first choice for treating equine asthma. Further high quality clinical trials are needed to clarify whether inhaled bronchodilators should be preferred to inhaled corticosteroids or vice versa, and to investigate the potential superiority of combination therapy in equine asthma. © 2017 EVJ Ltd.

Comparison of the efficacy and safety of thrombectomy devices in acute stroke : a network meta-analysis of randomized trials.

PubMed

Saber, Hamidreza; Rajah, Gary B; Kherallah, Riyad Y; Jadhav, Ashutosh P; Narayanan, Sandra

2017-12-15

Mechanical thrombectomy (MT) is increasingly used for large-vessel occlusions (LVO), but randomized clinical trial (RCT) level data with regard to differences in clinical outcomes of MT devices are limited. We conducted a network meta-analysis (NMA) that enables comparison of modern MT devices (Trevo, Solitaire, Aspiration) and strategies (stent retriever vs aspiration) across trials. Relevant RCTs were identified by a systematic review. The efficacy outcome was 90-day functional independence (modified Rankin Scale (mRS) score 0-2). Safety outcomes were 90-day catastrophic outcome (mRS 5-6) and symptomatic intracranial hemorrhage (sICH). Fixed-effect Bayesian NMA was performed to calculate risk estimates and the rank probabilities. In a NMA of six relevant RCTs (SWIFT, TREVO2, EXTEND-IA, SWIFT-PRIME, REVASCAT, THERAPY; total of 871 patients, 472 Solitaire vs medical-only, 108 Aspiration vs medical-only, 178 Trevo vs Merci, and 113 Solitaire vs Merci) with medical-only arm as the reference, Trevo had the greatest functional independence (OR 4.14, 95% credible interval (CrI) 1.41-11.80; top rank probability 92%) followed by Solitaire (OR 2.55, 95% CrI 1.75-3.74; top rank probability 72%). Solitaire and Aspiration devices had the greatest top rank probability with respect to low sICH and catastrophic outcomes (76% and 91%, respectively), but without significant differences between each other. In a separate network of seven RCTs (MR-CLEAN, ESCAPE, EXTEND-IA, SWIFT-PRIME, REVASCAT, THERAPY, ASTER; 1737 patients), first-line stent retriever was associated with a higher top rank probability of functional independence than aspiration (95% vs 54%), with comparable safety outcomes. These findings suggest that Trevo and Solitaire devices are associated with a greater likelihood of functional independence whereas Solitaire and Aspiration devices appear to be safer. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Social Network Changes and Life Events across the Life Span: A Meta-Analysis

ERIC Educational Resources Information Center

Wrzus, Cornelia; Hanel, Martha; Wagner, Jenny; Neyer, Franz J.

2013-01-01

For researchers and practitioners interested in social relationships, the question remains as to how large social networks typically are, and how their size and composition change across adulthood. On the basis of predictions of socioemotional selectivity theory and social convoy theory, we conducted a meta-analysis on age-related social network…

Bayesian networks and statistical analysis application to analyze the diagnostic test accuracy

NASA Astrophysics Data System (ADS)

Orzechowski, P.; Makal, Jaroslaw; Onisko, A.

2005-02-01

The computer aided BPH diagnosis system based on Bayesian network is described in the paper. First result are compared to a given statistical method. Different statistical methods are used successfully in medicine for years. However, the undoubted advantages of probabilistic methods make them useful in application in newly created systems which are frequent in medicine, but do not have full and competent knowledge. The article presents advantages of the computer aided BPH diagnosis system in clinical practice for urologists.

An Intuitive Dashboard for Bayesian Network Inference

NASA Astrophysics Data System (ADS)

Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

2014-03-01

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism

PubMed Central

Venkataraman, Archana; Duncan, James S.; Yang, Daniel Y.-J.; Pelphrey, Kevin A.

2015-01-01

Resting-state functional magnetic resonance imaging (rsfMRI) studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD). Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differences in connectivity to localize a subset of foci that are most affected by ASD. Our approach is entirely data-driven and does not impose spatial constraints on the region foci or dictate the trajectory of altered functional pathways. We apply our method to data from the openly shared Autism Brain Imaging Data Exchange (ABIDE) and pinpoint two intrinsic functional networks that distinguish ASD patients from typically developing controls. One network involves foci in the right temporal pole, left posterior cingulate cortex, left supramarginal gyrus, and left middle temporal gyrus. Automated decoding of this network by the Neurosynth meta-analytic database suggests high-level concepts of “language” and “comprehension” as the likely functional correlates. The second network consists of the left banks of the superior temporal sulcus, right posterior superior temporal sulcus extending into temporo-parietal junction, and right middle temporal gyrus. Associated functionality of these regions includes “social” and “person”. The abnormal pathways emanating from the above foci indicate that ASD patients simultaneously exhibit reduced long-range or inter-hemispheric connectivity and increased short-range or intra-hemispheric connectivity. Our findings reveal new insights into ASD and highlight possible neural mechanisms of the disorder. PMID:26106561

ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling | Office of Cancer Genomics

Cancer.gov

Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives.

Risperidone or Aripiprazole in Children and Adolescents with Autism and/or Intellectual Disability: A Bayesian Meta-Analysis of Efficacy and Secondary Effects

ERIC Educational Resources Information Center

Cohen, David; Raffin, Marie; Canitano, Roberto; Bodeau, Nicolas; Bonnot, Olivier; Perisse, Didier; Consoli, Angele; Laurent, Claudine

2013-01-01

Second-generation antipsychotics (SGAs) induce frequent adverse effects in children and adolescents with each compound appearing to have a specific adverse effect profile. Aripiprazole and risperidone are FDA-approved medications for behavioral disturbances associated with autism and/or intellectual disabilities (ID) in children and adolescents.…

Comparative efficacy and tolerability of antidepressants for major depressive disorder in children and adolescents: a network meta-analysis.

PubMed

Cipriani, Andrea; Zhou, Xinyu; Del Giovane, Cinzia; Hetrick, Sarah E; Qin, Bin; Whittington, Craig; Coghill, David; Zhang, Yuqing; Hazell, Philip; Leucht, Stefan; Cuijpers, Pim; Pu, Juncai; Cohen, David; Ravindran, Arun V; Liu, Yiyun; Michael, Kurt D; Yang, Lining; Liu, Lanxiang; Xie, Peng

2016-08-27

Major depressive disorder is one of the most common mental disorders in children and adolescents. However, whether to use pharmacological interventions in this population and which drug should be preferred are still matters of controversy. Consequently, we aimed to compare and rank antidepressants and placebo for major depressive disorder in young people. We did a network meta-analysis to identify both direct and indirect evidence from relevant trials. We searched PubMed, the Cochrane Library, Web of Science, Embase, CINAHL, PsycINFO, LiLACS, regulatory agencies' websites, and international registers for published and unpublished, double-blind randomised controlled trials up to May 31, 2015, for the acute treatment of major depressive disorder in children and adolescents. We included trials of amitriptyline, citalopram, clomipramine, desipramine, duloxetine, escitalopram, fluoxetine, imipramine, mirtazapine, nefazodone, nortriptyline, paroxetine, sertraline, and venlafaxine. Trials recruiting participants with treatment-resistant depression, treatment duration of less than 4 weeks, or an overall sample size of less than ten patients were excluded. We extracted the relevant information from the published reports with a predefined data extraction sheet, and assessed the risk of bias with the Cochrane risk of bias tool. The primary outcomes were efficacy (change in depressive symptoms) and tolerability (discontinuations due to adverse events). We did pair-wise meta-analyses using the random-effects model and then did a random-effects network meta-analysis within a Bayesian framework. We assessed the quality of evidence contributing to each network estimate using the GRADE framework. This study is registered with PROSPERO, number CRD42015016023. We deemed 34 trials eligible, including 5260 participants and 14 antidepressant treatments. The quality of evidence was rated as very low in most comparisons. For efficacy, only fluoxetine was statistically significantly more effective than placebo (standardised mean difference -0·51, 95% credible interval [CrI] -0·99 to -0·03). In terms of tolerability, fluoxetine was also better than duloxetine (odds ratio [OR] 0·31, 95% CrI 0·13 to 0·95) and imipramine (0·23, 0·04 to 0·78). Patients given imipramine, venlafaxine, and duloxetine had more discontinuations due to adverse events than did those given placebo (5·49, 1·96 to 20·86; 3·19, 1·01 to 18·70; and 2·80, 1·20 to 9·42, respectively). In terms of heterogeneity, the global I(2) values were 33·21% for efficacy and 0% for tolerability. When considering the risk-benefit profile of antidepressants in the acute treatment of major depressive disorder, these drugs do not seem to offer a clear advantage for children and adolescents. Fluoxetine is probably the best option to consider when a pharmacological treatment is indicated. National Basic Research Program of China (973 Program). Copyright © 2016 Elsevier Ltd. All rights reserved.

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

Esophagectomy in patients with liver cirrhosis: a systematic review and Bayesian meta-analysis.

PubMed

Asti, E; Sozzi, M; Bonitta, G; Bernardi, D; Bonavina, L

2018-04-10

Patients with esophageal carcinoma and concomitant liver cirrhosis carry a high operative risk and may be denied esophagectomy. We performed a systematic review of the literature and meta-analysis to investigate postoperative outcomes in these patients. Studies reporting outcomes after esophagectomy in patients with liver cirrhosis were searched in Medline, Embase, Cochrane Library, ISI Web of Science, and Scopus until June 2017, matching the terms "liver cirrhosis", "esophageal neoplasm" and/or "esophageal surgery". Extracted data included study characteristics, demographic and clinical patient characteristics, type of surgical procedure, and postoperative outcomes. A systematic review and Bayesian meta-analysis were performed. Five observational, retrospective and single-arm studies with a total of 157 patients were included. The main cause of death was liver failure followed by pneumonia/sepsis and anastomotic leak. Ascites and pleural effusion were the most frequent postoperative complications (pooled rates 36% and 34%, respectively). The pooled morbidity rate was 74% (95% HPD=46-81%) while the pooled mortality was 18% (95% HPD=17-27%). Study heterogeneity (τ2) was low, ranging from 0.046 to 0.080. An incidental diagnosis of liver cirrhosis was reported in 15.6% of patients in one series. Five-year survival was similar between cirrhotic and non-cirrhotic patients but was statistically significantly higher in patients with MELD score<10. Sound scientific evidence with regard to efficacy and outcomes of esophagectomy in patients with concomitant liver cirrhosis is lacking. There is a need to properly select these frail patients to reduce postoperative morbidity and mortality rates. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Bayesian models: A statistical primer for ecologists

USGS Publications Warehouse

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776

ERIC Educational Resources Information Center

West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.

2010-01-01

A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco…

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

Bayesian Logic Programs for Plan Recognition and Machine Reading

DTIC Science & Technology

2012-12-01

models is that they can handle both uncertainty and structured/ relational data. As a result, they are widely used in domains like social network...data. As a result, they are widely used in domains like social net- work analysis, biological data analysis, and natural language processing. Bayesian...the Story Understanding data set. (b) The logical representation of the observations. (c) The set of ground rules obtained from logical abduction

Efficacy and safety of TNF-α inhibitors for active ankylosing spondylitis patients: Multiple treatment comparisons in a network meta-analysis

NASA Astrophysics Data System (ADS)

Liu, Wei; Wu, Yuan-Hao; Zhang, Lei; Liu, Xiao-Ya; Bin Xue; Bin Liu; Wang, Yi; Ji, Yang

2016-09-01

Ankylosing spondylitis (AS) is an inflammatory rheumatic disease with impact on axial skeleton, peripheral joints and enthuses, and it may result in severe disabilities of those parts. Tumor necrosis factor-α (TNF-α) inhibitors are considered as an effective treatment for patients with active AS. In this study, we conducted a network meta-analysis to compare the clinical outcomes of active AS patients treated with TNF-α inhibitors. Randomized controlled trials (RCTs) evaluating the efficacy and safety of TNF-α inhibitors were retrieved in literature search and selected for meta-analysis. Changes in ASAS20 response, ASAS40 response and BASDAI 50% response were regarded as efficacy outcomes; serious adverse events (SAE) and all cause withdrawals were regarded as safety outcomes. Both traditional pairwise meta-analysis and network meta-analysis were performed. The results showed that adalimumab and infliximab had better clinical outcomes. Infliximab consistently appeared to be the most effective TNF-α inhibitors with a high risk of adverse events for patients with active AS; meanwhile, adalimumab ranked highest with respect to adverse effects with efficacy secondary to infliximab. As a result, we were unable to conclude the optimal TNF-α inhibitor and this issue should be solved by future researchers.

Parameter inference in small world network disease models with approximate Bayesian Computational methods

NASA Astrophysics Data System (ADS)

Walker, David M.; Allingham, David; Lee, Heung Wing Joseph; Small, Michael

2010-02-01

Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.

Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

PubMed

Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

2008-10-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

Comparison of type 2 diabetes mellitus incidence in different phases of hepatitis B virus infection: A meta-analysis.

PubMed

Shen, Yi; Zhang, Sheng; Wang, Xulin; Wang, Yuanyuan; Zhang, Jian; Qin, Gang; Li, Wenchao; Ding, Kun; Zhang, Lei; Liang, Feng

2017-10-01

Because whether hepatitis B virus infection increases the risk of type 2 diabetes mellitus has been a controversial topic, pair-wise and network meta-analyses of published literature were carried out to accurately evaluate the association between different phases of hepatitis B virus infection and the risk of type 2 diabetes mellitus. A comprehensive literature retrieval was conducted from the PubMed, Embase, Cochrane Library and Chinese Database to identify epidemiological studies on the association between hepatitis B virus infection and the risk of type 2 diabetes mellitus that were published from 1999 to 2015. A pair-wise meta-analysis of direct evidence was performed to estimate the pooled odds ratios and 95% confidence intervals. A network meta-analysis was conducted, including the construction of a network plot, inconsistency plot, predictive interval plot, comparison-adjusted funnel plot and rank diagram, to graphically link the direct and indirect comparisons between different hepatitis B virus infective phases. Eighteen publications (n=113 639) describing 32 studies were included in this meta-analysis. In the pair-wise meta-analysis, the pooled odds ratio for type 2 diabetes mellitus in chronic hepatitis B cirrhosis patients was 1.76 (95% confidence interval: 1.44-2.14) when compared with non-cirrhotic chronic hepatitis B patients. In the network meta-analysis, six comparisons of four hepatitis B virus infectious states indicated the following descending order for the risk of type 2 diabetes mellitus: hepatitis B cirrhosis patients, non-cirrhotic chronic hepatitis B patients, hepatitis B virus carriers and non-hepatitis B virus controls. This study suggests that hepatitis B virus infection is not an independent risk factor for type 2 diabetes mellitus, but the development of cirrhosis may increase the incidence of type 2 diabetes mellitus cirrhosis. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Using Bayesian belief networks in adaptive management.

Treesearch

J.B. Nyberg; B.G. Marcot; R. Sulyma

2006-01-01

Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...

Modeling Diagnostic Assessments with Bayesian Networks

ERIC Educational Resources Information Center

Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

2007-01-01

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

Bayesian networks for maritime traffic accident prevention: benefits and challenges.

PubMed

Hänninen, Maria

2014-12-01

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evidence reasoning method for constructing conditional probability tables in a Bayesian network of multimorbidity.

PubMed

Du, Yuanwei; Guo, Yubin

2015-01-01

The intrinsic mechanism of multimorbidity is difficult to recognize and prediction and diagnosis are difficult to carry out accordingly. Bayesian networks can help to diagnose multimorbidity in health care, but it is difficult to obtain the conditional probability table (CPT) because of the lack of clinically statistical data. Today, expert knowledge and experience are increasingly used in training Bayesian networks in order to help predict or diagnose diseases, but the CPT in Bayesian networks is usually irrational or ineffective for ignoring realistic constraints especially in multimorbidity. In order to solve these problems, an evidence reasoning (ER) approach is employed to extract and fuse inference data from experts using a belief distribution and recursive ER algorithm, based on which evidence reasoning method for constructing conditional probability tables in Bayesian network of multimorbidity is presented step by step. A multimorbidity numerical example is used to demonstrate the method and prove its feasibility and application. Bayesian network can be determined as long as the inference assessment is inferred by each expert according to his/her knowledge or experience. Our method is more effective than existing methods for extracting expert inference data accurately and is fused effectively for constructing CPTs in a Bayesian network of multimorbidity.

[Basic concepts for network meta-analysis].

PubMed

Catalá-López, Ferrán; Tobías, Aurelio; Roqué, Marta

2014-12-01

Systematic reviews and meta-analyses have long been fundamental tools for evidence-based clinical practice. Initially, meta-analyses were proposed as a technique that could improve the accuracy and the statistical power of previous research from individual studies with small sample size. However, one of its main limitations has been the fact of being able to compare no more than two treatments in an analysis, even when the clinical research question necessitates that we compare multiple interventions. Network meta-analysis (NMA) uses novel statistical methods that incorporate information from both direct and indirect treatment comparisons in a network of studies examining the effects of various competing treatments, estimating comparisons between many treatments in a single analysis. Despite its potential limitations, NMA applications in clinical epidemiology can be of great value in situations where there are several treatments that have been compared against a common comparator. Also, NMA can be relevant to a research or clinical question when many treatments must be considered or when there is a mix of both direct and indirect information in the body of evidence. Copyright © 2013 Elsevier España, S.L.U. All rights reserved.

Exercise-associated DNA methylation change in skeletal muscle and the importance of imprinted genes: a bioinformatics meta-analysis.

PubMed

Brown, William M

2015-12-01

Epigenetics is the study of processes--beyond DNA sequence alteration--producing heritable characteristics. For example, DNA methylation modifies gene expression without altering the nucleotide sequence. A well-studied DNA methylation-based phenomenon is genomic imprinting (ie, genotype-independent parent-of-origin effects). We aimed to elucidate: (1) the effect of exercise on DNA methylation and (2) the role of imprinted genes in skeletal muscle gene networks (ie, gene group functional profiling analyses). Gene ontology (ie, gene product elucidation)/meta-analysis. 26 skeletal muscle and 86 imprinted genes were subjected to g:Profiler ontology analysis. Meta-analysis assessed exercise-associated DNA methylation change. g:Profiler found four muscle gene networks with imprinted loci. Meta-analysis identified 16 articles (387 genes/1580 individuals) associated with exercise. Age, method, sample size, sex and tissue variation could elevate effect size bias. Only skeletal muscle gene networks including imprinted genes were reported. Exercise-associated effect sizes were calculated by gene. Age, method, sample size, sex and tissue variation were moderators. Six imprinted loci (RB1, MEG3, UBE3A, PLAGL1, SGCE, INS) were important for muscle gene networks, while meta-analysis uncovered five exercise-associated imprinted loci (KCNQ1, MEG3, GRB10, L3MBTL1, PLAGL1). DNA methylation decreased with exercise (60% of loci). Exercise-associated DNA methylation change was stronger among older people (ie, age accounted for 30% of the variation). Among older people, genes exhibiting DNA methylation decreases were part of a microRNA-regulated gene network functioning to suppress cancer. Imprinted genes were identified in skeletal muscle gene networks and exercise-associated DNA methylation change. Exercise-associated DNA methylation modification could rewind the 'epigenetic clock' as we age. CRD42014009800. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Selecting Strategies to Reduce High-Risk Unsafe Work Behaviors Using the Safety Behavior Sampling Technique and Bayesian Network Analysis.

PubMed

Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas; Mohammadfam, Iraj

2017-03-04

High-risk unsafe behaviors (HRUBs) have been known as the main cause of occupational accidents. Considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. The aim of the present study was to find strategies for decreasing the rate of HRUBs using an integrated approach of safety behavior sampling technique and Bayesian networks analysis. A cross-sectional study. The Bayesian network was constructed using a focus group approach. The required data was collected using the safety behavior sampling, and the parameters of the network were estimated using Expectation-Maximization algorithm. Using sensitivity analysis and belief updating, it was determined that which factors had the highest influences on unsafe behavior. Based on BN analyses, safety training was the most important factor influencing employees' behavior at the workplace. High quality safety training courses can reduce the rate of HRUBs about 10%. Moreover, the rate of HRUBs increased by decreasing the age of employees. The rate of HRUBs was higher in the afternoon and last days of a week. Among the investigated variables, training was the most important factor affecting safety behavior of employees. By holding high quality safety training courses, companies would be able to reduce the rate of HRUBs significantly.

A Bayesian network model for predicting pregnancy after in vitro fertilization.

PubMed

Corani, G; Magli, C; Giusti, A; Gianaroli, L; Gambardella, L M

2013-11-01

We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred. © 2013 Elsevier Ltd. All rights reserved.

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

PubMed Central

Weiss, Scott T.

2014-01-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

PubMed

McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

2014-06-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores.

PubMed

Haile, Sarah R; Guerra, Beniamino; Soriano, Joan B; Puhan, Milo A

2017-12-21

Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them. Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined. We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small. We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns.

PubMed

Grapov, Dmitry; Wanichthanarak, Kwanjeera; Fiehn, Oliver

2015-08-15

Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/. ofiehn@ucdavis.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effect of Industry Sponsorship on Dental Restorative Trials.

PubMed

Schwendicke, F; Tu, Y-K; Blunck, U; Paris, S; Göstemeyer, G

2016-01-01

Industry sponsorship was found to potentially introduce bias into clinical trials. We assessed the effects of industry sponsorship on the design, comparator choice, and findings of randomized controlled trials on dental restorative materials. A systematic review was performed via MEDLINE, CENTRAL, and EMBASE. Randomized trials on dental restorative and adhesive materials published 2005 to 2015 were included. The design of sponsored and nonsponsored trials was compared statistically (risk of bias, treatment indication, setting, transferability, sample size). Comparator choice and network geometry of sponsored and nonsponsored trials were assessed via network analysis. Material performance rankings in different trial types were estimated via Bayesian network meta-analysis. Overall, 114 studies were included (15,321 restorations in 5,232 patients). We found 21 and 41 (18% and 36%) trials being clearly or possibly industry sponsored, respectively. Trial design of sponsored and nonsponsored trials did not significantly differ for most assessed items. Sponsored trials evaluated restorations of load-bearing cavities significantly more often than nonsponsored trials, had longer follow-up periods, and showed significantly increased risk of detection bias. Regardless of sponsorship status, comparisons were mainly performed within material classes. The proportion of trials comparing against gold standard restorative or adhesive materials did not differ between trial types. If ranked for performance according to the need to re-treat (best: least re-treatments), most material combinations were ranked similarly in sponsored and nonsponsored trials. The effect of industry sponsorship on dental restorative trials seems limited. © International & American Associations for Dental Research 2015.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

PubMed

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Adjusting for partial verification or workup bias in meta-analyses of diagnostic accuracy studies.

PubMed

de Groot, Joris A H; Dendukuri, Nandini; Janssen, Kristel J M; Reitsma, Johannes B; Brophy, James; Joseph, Lawrence; Bossuyt, Patrick M M; Moons, Karel G M

2012-04-15

A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can bias the accuracy estimates. Numerous methods have been described for correcting this partial verification bias or workup bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial verification or workup bias is present in a subset of the primary studies. The method corrects for verification bias without having to exclude primary studies with verification bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with verification bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients.

Standard dose versus low dose non-vitamin K antagonist oral anticoagulants in Asian patients with atrial fibrillation: A meta-analysis of contemporary randomized controlled trials.

PubMed

Wang, Kang-Ling; Giugliano, Robert P; Goto, Shinya; Chiu, Chun-Chih; Lin, Chun-Yi; Lai, En-Yu; Chiang, Chern-En

2016-12-01

Although randomized controlled trials (RCTs) indicated that standard dose non-vitamin K antagonist oral anticoagulants (NOACs) were more compelling, low dose NOACs are commonly used in clinical practice in Asia. The purpose of this study was to assess the relative therapeutic benefit and risk of standard dose vs low dose NOACs in Asian patients enrolled in contemporary RCTs. We performed a prespecified meta-analysis of 3155 Asian patients with NOACs in the RE-LY (Randomized Evaluation of Long-Term Anticoagulation Therapy) and ENGAGE AF-TIMI 48 (Effective Anticoagulation with Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48) trials. Efficacy and safety with standard dose vs low dose NOACs were compared by risk ratios (RRs) and 95% confidence intervals (CIs) in a random-effects model. An evidence network incorporating additional Asian patients from ROCKET AF, J- ROCKET AF, and ARISTOTLE was constructed with the Bayesian method. Risks of stroke or systemic embolism and ischemic stroke were significantly reduced with standard dose vs low dose NOACs (RR 0.62, 95% CI 0.45-0.85; and RR 0.55, 95% CI 0.38-0.79, respectively). Rates of major, intracranial, and life-threatening bleeding with 2 dosing regimens were broadly similar (RR 1.31, 95% CI 0.74-2.33; RR 1.54, 95% CI 0.72-3.30; and RR 1.49, 95% CI 0.87-2.55, respectively). Absolute rates of all-cause mortality and the net clinical outcome with standard dose NOACs were lower but not statistically significant (absolute reduction 0.4% per year and 1.1% per year, respectively). Network meta-analyses demonstrated that standard dose NOACs had the most favorable risk-benefit profile among oral anticoagulants. In Asian patients, standard dose NOACs represent a more appealing therapeutic option than low dose NOACs, with a significant reduction in ischemic stroke without an excess of major bleeding. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Efficacy and Safety of Different Bisphosphonates for Bone Loss Prevention in Kidney Transplant Recipients: A Network Meta-Analysis of Randomized Controlled Trials

PubMed Central

Yang, Yan; Qiu, Shi; Tang, Xi; Li, Xin-Rui; Deng, Ling-Hui; Wei, Qiang; Fu, Ping

2018-01-01

Background: Mineral and bone disorder is one of the severe complications in kidney transplant recipients (KTRs). Previous studies showed that bisphosphonates had favorable effects on bone mineral density (BMD). We sought to compare different bisphosphonate regimens and rank their strategies. Methods: We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) up to April 01, 2017, for randomized controlled trials (RCTs) comparing bisphosphonate treatments in adult KTRs. The primary outcome was BMD change. We executed the tool recommended by the Cochrane Collaboration to evaluate the risk of bias. We performed pairwise meta-analyses using random effects models and network meta-analysis (NMA) using Bayesian models and assessed the quality of evidence. Results: A total of 21 RCTs (1332 participants) comparing 6 bisphosphonate regimens were included. All bisphosphonates showed a significantly increased percentage change in BMD at the lumbar spine compared to calcium except clodronate. Pamidronate with calcium and Vitamin D analogs showed improved BMD in comparison to clodronate with calcium (mean difference [MD], 9.84; 95% credibility interval [CrI], 1.06–19.70). The combination of calcium and Vitamin D analogs had a significantly lower influence than adding either pamidronate or alendronate (MD, 6.34; 95% CrI, 2.59–11.01 and MD, 6.16; 95% CrI, 0.54–13.24, respectively). In terms of percentage BMD change at the femoral neck, both pamidronate and ibandronate combined with calcium demonstrated a remarkable gain compared with calcium (MD, 7.02; 95% CrI, 0.30–13.29 and MD, 7.30; 95% CrI, 0.32–14.22, respectively). The combination of ibandronate with calcium displayed a significant increase in absolute BMD compared to any other treatments and was ranked best. Conclusions: Our NMA suggested that new-generation bisphosphonates such as ibandronate were more favorable in KTRs to improve BMD. However, the conclusion should be treated with caution due to indirect comparisons. PMID:29578126

Comparative safety and effectiveness of perinatal antiretroviral therapies for HIV-infected women and their children: Systematic review and network meta-analysis including different study designs.

PubMed

Veroniki, Areti Angeliki; Antony, Jesmin; Straus, Sharon E; Ashoor, Huda M; Finkelstein, Yaron; Khan, Paul A; Ghassemi, Marco; Blondal, Erik; Ivory, John D; Hutton, Brian; Gough, Kevin; Hemmelgarn, Brenda R; Lillie, Erin; Vafaei, Afshin; Tricco, Andrea C

2018-01-01

Nearly all newly infected children acquire Human Immunodeficiency virus (HIV) via mother-to-child transmission (MTCT) during pregnancy, labour or breastfeeding from untreated HIV-positive mothers. Antiretroviral therapy (ART) is the standard care for pregnant women with HIV. However, evidence of ART effectiveness and harms in infants and children of HIV-positive pregnant women exposed to ART has been largely inconclusive. The aim of our systematic review and network meta-analysis (NMA) was to evaluate the comparative safety and effectiveness of ART drugs in children exposed to maternal HIV and ART (or no ART/placebo) across different study designs. We searched MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials (inception until December 7, 2015). Primary outcomes were any congenital malformations (CMs; safety), including overall major and minor CMs, and mother-to-child transmission (MTCT; effectiveness). Random-effects Bayesian pairwise meta-analyses and NMAs were conducted. After screening 6,468 citations and 1,373 full-text articles, 90 studies of various study designs and 90,563 patients were included. The NMA on CMs (20 studies, 7,503 children, 16 drugs) found that none of the ART drugs examined here were associated with a significant increase in CMs. However, zidovudine administered with lamivudine and indinavir was associated with increased risk of preterm births, zidovudine administered with nevirapine was associated with increased risk of stillbirths, and lamivudine administered with stavudine and efavirenz was associated with increased risk of low birth weight. A NMA on MTCT (11 studies, 10,786 patients, 6 drugs) found that zidovudine administered once (odds ratio [OR] = 0.39, 95% credible interval [CrI]: 0.19-0.83) or twice (OR = 0.43, 95% CrI: 0.21-0.68) was associated with significantly reduced risk of MTCT. Our findings suggest that ART drugs are not associated with an increased risk of CMs, yet some may increase adverse birth events. Some ART drugs (e.g., zidovudine) effectively reduce MTCT.

Diagnostic accuracy of clinical illness for bovine respiratory disease (BRD) diagnosis in beef cattle placed in feedlots: A systematic literature review and hierarchical Bayesian latent-class meta-analysis.

PubMed

Timsit, E; Dendukuri, N; Schiller, I; Buczinski, S

2016-12-01

Diagnosis of bovine respiratory disease (BRD) in beef cattle placed in feedlots is typically based on clinical illness (CI) detected by pen-checkers. Unfortunately, the accuracy of this diagnostic approach (namely, sensitivity [Se] and specificity [Sp]) remains poorly understood, in part due to the absence of a reference test for ante-mortem diagnosis of BRD. Our objective was to pool available estimates of CI's diagnostic accuracy for BRD diagnosis in feedlot beef cattle while adjusting for the inaccuracy in the reference test. The presence of lung lesions (LU) at slaughter was used as the reference test. A systematic review of the literature was conducted to identify research articles comparing CI detected by pen-checkers during the feeding period to LU at slaughter. A hierarchical Bayesian latent-class meta-analysis was used to model test accuracy. This approach accounted for imperfections of both tests as well as the within and between study variability in the accuracy of CI. Furthermore, it also predicted the Se CI and Sp CI for future studies. Conditional independence between CI and LU was assumed, as these two tests are not based on similar biological principles. Seven studies were included in the meta-analysis. Estimated pooled Se CI and Sp CI were 0.27 (95% Bayesian credible interval: 0.12-0.65) and 0.92 (0.72-0.98), respectively, whereas estimated pooled Se LU and Sp LU were 0.91 (0.82-0.99) and 0.67 (0.64-0.79). Predicted Se CI and Sp CI for future studies were 0.27 (0.01-0.96) and 0.92 (0.14-1.00), respectively. The wide credible intervals around predicted Se CI and Sp CI estimates indicated considerable heterogeneity among studies, which suggests that pooled Se CI and Sp CI are not generalizable to individual studies. In conclusion, CI appeared to have poor Se but high Sp for BRD diagnosis in feedlots. Furthermore, considerable heterogeneity among studies highlighted an urgent need to standardize BRD diagnosis in feedlots. Copyright © 2016 Elsevier B.V. All rights reserved.

Do antibody responses to the influenza vaccine persist year-round in the elderly? A systematic review and meta-analysis.

PubMed

Young, Barnaby; Zhao, Xiahong; Cook, Alex R; Parry, Christopher M; Wilder-Smith, Annelies; I-Cheng, Mark Chen

2017-01-05

The influenza vaccine is less immunogenic in older than younger adults, and the duration of protection is unclear. Determining if protection persists beyond a typical seasonal epidemic is important for climates where influenza virus activity is year-round. A systematic review protocol was developed and registered with PROSPERO [CRD42015023847]. Electronic databases were searched systematically for studies reporting haemagglutination-inhibition (HI) titres 180-360days following vaccination with inactivated trivalent seasonal influenza vaccine, in adults aged ⩾65years. Geometric mean titre (GMT) and seroprotection (HI titre ⩾1:40) at each time point was extracted. A Bayesian model was developed of titre trajectories from pre-vaccination to Day 360. In the meta-analysis, studies were aggregated using a random-effects model to compare pre-vaccination with post-vaccination HI titres at Day 21-42 ('seroconversion'), Day 180 and Day 360. Potential sources of bias were systematically assessed, and heterogeneity explored. 2864 articles were identified in the literature search, of which nineteen met study inclusion/exclusion criteria. Sixteen studies contained analysable data from 2565 subjects. In the Bayesian model, the proportion of subjects seroprotected increased from 41-51% pre-vaccination to 75-78% at seroconversion. Seroprotection subsequently fell below 60% for all serotypes by Day 360: A/H1 42% (95% CI 38-46), A/H3 59% (54-63), B 47% (42-52). The Bayesian model of GMT trajectories revealed a similar pattern. By Day 360, titres were similar to pre-vaccination levels. In the meta-analysis, no significant difference in proportion of subjects seroprotected, 0 (-0.11, 0.11) or in log 2 GMT 0.30 (-0.02, 0.63) was identified by Day 360 compared with pre-vaccination. The quality of this evidence was limited to moderate on account of significant participant dropout. The review found consistent evidence that HI antibody responses following influenza vaccination do not reliably persist year-round in older adults. Alternative vaccination strategies could provide clinical benefits in regions where year-round protection is important. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

PubMed

Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Comparative effectiveness of glycemic control in patients with type 2 diabetes treated with GLP-1 receptor agonists: a network meta-analysis of placebo-controlled and active-comparator trials

PubMed Central

Orme, Michelle E; Nguyen, Hiep; Lu, Jackie Y; Thomas, Susan A

2017-01-01

Background Clinical studies of patients with type 2 diabetes show that GLP-1 receptor agonists (GLP-1 RAs) improve glycemic control and promote weight loss. We conducted a Bayesian network meta-analysis (NMA) of placebo- and active-controlled randomized trials to assess the comparative effectiveness of liraglutide, albiglutide, dulaglutide, and exenatide twice daily and once weekly, with a focus on glycemic control. Materials and methods We searched Medline, Embase, and the Cochrane Library (up to December 2014) for core registration programs for US-approved GLP-1 RAs. Patients reaching an A1C target of <7% were analyzed with a binomial model and change in A1C from baseline with a normal model. A covariate analysis assessed the impact of baseline A1C and treatment background on outcomes. Results The base-case NMA used 23 trials reporting A1C outcomes at ~6 month follow-up. The results, unadjusted and adjusted for baseline A1C, indicated that all GLP-1 RAs resulted in statistically significantly lower A1C at follow-up compared with placebo. The odds of reaching the <7% target were also significantly better compared with placebo. With dulaglutide, exenatide once weekly, and liraglutide, the absolute reduction in A1C at 6 months was 0.9%–1.4%, and was significantly better than exenatide twice daily. Albiglutide was not significantly different from exenatide twice daily. We estimate that ~50% of patients will meet the <7% A1C target within 6 months of commencing GLP-1 RAs. Conclusion This was a comprehensive assessment of the comparative effectiveness of GLP-1 RAs and A1C outcome. GLP-1 RAs are a viable addition to oral antidiabetes therapy, and dulaglutide, exenatide once weekly, and liraglutide are the most effective. PMID:28435304

Comparative effectiveness of different wound dressings for patients with partial-thickness burns: study protocol of a systematic review and a Bayesian framework network meta-analysis

PubMed Central

Jiang, Qiong; Chen, Zhao-Hong; Wang, Shun-Bin; Chen, Xiao-Dong

2017-01-01

Introduction Selecting a suitable wound dressing for patients with partial-thickness burns (PTBs) is important in wound care. However, the comparative effectiveness of different dressings has not been studied. We report the protocol of a network meta-analysis designed to combine direct and indirect evidence of wound dressings in the management of PTB. Methods and analysis We will search for randomised controlled trials (RCTs) evaluating the wound-healing effect of a wound dressing in the management of PTB. Searches will be conducted in MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Cochrane Wounds Group Specialised Register and CINAHL. A comprehensive search strategy is developed to retrieve articles reporting potentially eligible RCTs. Besides, we will contact the experts in the field and review the conference proceedings to locate non-published studies. The reference lists of articles will be reviewed for any candidate studies. Two independent reviewers will screen titles and abstracts of the candidate articles. All eligible RCTs will be obtained in full text to perform a review. Disagreement on eligibility of an RCT will be solved by group discussion. The information of participants, interventions, comparisons and outcomes from included RCTs will be recorded and summarised. The primary outcome is time to complete wound healing. Secondary outcomes include the proportion of burns completely healed at the end of treatment, change in wound surface area at the end of treatment, incidence of adverse events, etc. Ethics and dissemination The result of this review will provide evidence for the comparative effectiveness of different wound dressings in the management of PTB. It will also facilitate decision-making in choosing a suitable wound dressing. We will disseminate the review through a peer-review journal and conference abstracts or posters. Trial registration number PROSPERO CRD42016041574; Pre-results. PMID:28336737

Learning oncogenetic networks by reducing to mixed integer linear programming.

PubMed

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

GetReal in network meta-analysis: a review of the methodology.

PubMed

Efthimiou, Orestis; Debray, Thomas P A; van Valkenhoef, Gert; Trelle, Sven; Panayidou, Klea; Moons, Karel G M; Reitsma, Johannes B; Shang, Aijing; Salanti, Georgia

2016-09-01

Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Advanced obstacle avoidance for a laser based wheelchair using optimised Bayesian neural networks.

PubMed

Trieu, Hoang T; Nguyen, Hung T; Willey, Keith

2008-01-01

In this paper we present an advanced method of obstacle avoidance for a laser based intelligent wheelchair using optimized Bayesian neural networks. Three neural networks are designed for three separate sub-tasks: passing through a door way, corridor and wall following and general obstacle avoidance. The accurate usable accessible space is determined by including the actual wheelchair dimensions in a real-time map used as inputs to each networks. Data acquisitions are performed separately to collect the patterns required for specified sub-tasks. Bayesian frame work is used to determine the optimal neural network structure in each case. Then these networks are trained under the supervision of Bayesian rule. Experiment results showed that compare to the VFH algorithm our neural networks navigated a smoother path following a near optimum trajectory.

Bayesian network modeling applied to coastal geomorphology: lessons learned from a decade of experimentation and application

NASA Astrophysics Data System (ADS)

Plant, N. G.; Thieler, E. R.; Gutierrez, B.; Lentz, E. E.; Zeigler, S. L.; Van Dongeren, A.; Fienen, M. N.

2016-12-01

We evaluate the strengths and weaknesses of Bayesian networks that have been used to address scientific and decision-support questions related to coastal geomorphology. We will provide an overview of coastal geomorphology research that has used Bayesian networks and describe what this approach can do and when it works (or fails to work). Over the past decade, Bayesian networks have been formulated to analyze the multi-variate structure and evolution of coastal morphology and associated human and ecological impacts. The approach relates observable system variables to each other by estimating discrete correlations. The resulting Bayesian-networks make predictions that propagate errors, conduct inference via Bayes rule, or both. In scientific applications, the model results are useful for hypothesis testing, using confidence estimates to gage the strength of tests while applications to coastal resource management are aimed at decision-support, where the probabilities of desired ecosystems outcomes are evaluated. The range of Bayesian-network applications to coastal morphology includes emulation of high-resolution wave transformation models to make oceanographic predictions, morphologic response to storms and/or sea-level rise, groundwater response to sea-level rise and morphologic variability, habitat suitability for endangered species, and assessment of monetary or human-life risk associated with storms. All of these examples are based on vast observational data sets, numerical model output, or both. We will discuss the progression of our experiments, which has included testing whether the Bayesian-network approach can be implemented and is appropriate for addressing basic and applied scientific problems and evaluating the hindcast and forecast skill of these implementations. We will present and discuss calibration/validation tests that are used to assess the robustness of Bayesian-network models and we will compare these results to tests of other models. This will demonstrate how Bayesian networks are used to extract new insights about coastal morphologic behavior, assess impacts to societal and ecological systems, and communicate probabilistic predictions to decision makers.

Clarifying the debate on population-based screening for breast cancer with mammography

PubMed Central

Chen, Tony Hsiu-Hsi; Yen, Amy Ming-Fang; Fann, Jean Ching-Yuan; Gordon, Paula; Chen, Sam Li-Sheng; Chiu, Sherry Yueh-Hsia; Hsu, Chen-Yang; Chang, King-Jen; Lee, Won-Chul; Yeoh, Khay Guan; Saito, Hiroshi; Promthet, Supannee; Hamashima, Chisato; Maidin, Alimin; Robinson, Fredie; Zhao, Li-Zhong

2017-01-01

Abstract Background: The recent controversy about using mammography to screen for breast cancer based on randomized controlled trials over 3 decades in Western countries has not only eclipsed the paradigm of evidence-based medicine, but also puts health decision-makers in countries where breast cancer screening is still being considered in a dilemma to adopt or abandon such a well-established screening modality. Methods: We reanalyzed the empirical data from the Health Insurance Plan trial in 1963 to the UK age trial in 1991 and their follow-up data published until 2015. We first performed Bayesian conjugated meta-analyses on the heterogeneity of attendance rate, sensitivity, and over-detection and their impacts on advanced stage breast cancer and death from breast cancer across trials using Bayesian Poisson fixed- and random-effect regression model. Bayesian meta-analysis of causal model was then developed to assess a cascade of causal relationships regarding the impact of both attendance and sensitivity on 2 main outcomes. Results: The causes of heterogeneity responsible for the disparities across the trials were clearly manifested in 3 components. The attendance rate ranged from 61.3% to 90.4%. The sensitivity estimates show substantial variation from 57.26% to 87.97% but improved with time from 64% in 1963 to 82% in 1980 when Bayesian conjugated meta-analysis was conducted in chronological order. The percentage of over-detection shows a wide range from 0% to 28%, adjusting for long lead-time. The impacts of the attendance rate and sensitivity on the 2 main outcomes were statistically significant. Causal inference made by linking these causal relationships with emphasis on the heterogeneity of the attendance rate and sensitivity accounted for the variation in the reduction of advanced breast cancer (none-30%) and of mortality (none-31%). We estimated a 33% (95% CI: 24–42%) and 13% (95% CI: 6–20%) breast cancer mortality reduction for the best scenario (90% attendance rate and 95% sensitivity) and the poor scenario (30% attendance rate and 55% sensitivity), respectively. Conclusion: Elucidating the scenarios from high to low performance and learning from the experiences of these trials helps screening policy-makers contemplate on how to avoid errors made in ineffective studies and emulate the effective studies to save women lives. PMID:28099330

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Effectiveness of oral hydration in preventing contrast-induced acute kidney injury in patients undergoing coronary angiography or intervention: a pairwise and network meta-analysis.

PubMed

Zhang, Weidai; Zhang, Jiawei; Yang, Baojun; Wu, Kefei; Lin, Hanfei; Wang, Yanping; Zhou, Lihong; Wang, Huatao; Zeng, Chujuan; Chen, Xiao; Wang, Zhixing; Zhu, Junxing; Songming, Chen

2018-06-01

The effectiveness of oral hydration in preventing contrast-induced acute kidney injury (CI-AKI) in patients undergoing coronary angiography or intervention has not been well established. This study aims to evaluate the efficacy of oral hydration compared with intravenous hydration and other frequently used hydration strategies. PubMed, Embase, Web of Science, and the Cochrane central register of controlled trials were searched from inception to 8 October 2017. To be eligible for analysis, studies had to evaluate the relative efficacy of different prophylactic hydration strategies. We selected and assessed the studies that fulfilled the inclusion criteria and carried out a pairwise and network meta-analysis using RevMan5.2 and Aggregate Data Drug Information System 1.16.8 software. A total of four studies (538 participants) were included in our pairwise meta-analysis and 1754 participants from eight studies with four frequently used hydration strategies were included in a network meta-analysis. Pairwise meta-analysis indicated that oral hydration was as effective as intravenous hydration for the prevention of CI-AKI (5.88 vs. 8.43%; odds ratio: 0.73; 95% confidence interval: 0.36-1.47; P>0.05), with no significant heterogeneity between studies. Network meta-analysis showed that there was no significant difference in the prevention of CI-AKI. However, the rank probability plot suggested that oral plus intravenous hydration had a higher probability (51%) of being the best strategy, followed by diuretic plus intravenous hydration (39%) and oral hydration alone (10%). Intravenous hydration alone was the strategy with the highest probability (70%) of being the worst hydration strategy. Our study shows that oral hydration is not inferior to intravenous hydration for the prevention of CI-AKI in patients with normal or mild-to-moderate renal dysfunction undergoing coronary angiography or intervention.

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

NASA Astrophysics Data System (ADS)

Zhang, Chao; Qin, Ting Xin; Huang, Shuai; Wu, Jian Song; Meng, Xin Yan

2018-06-01

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors' effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors' effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors' effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors' effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.

PubMed

Carriger, John F; Barron, Mace G; Newman, Michael C

2016-12-20

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

Calibrating Bayesian Network Representations of Social-Behavioral Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

A Network Meta-Analysis Comparing Effects of Various Antidepressant Classes on the Digit Symbol Substitution Test (DSST) as a Measure of Cognitive Dysfunction in Patients with Major Depressive Disorder.

PubMed

Baune, Bernhard T; Brignone, Mélanie; Larsen, Klaus Groes

2018-02-01

Major depressive disorder is a common condition that often includes cognitive dysfunction. A systematic literature review of studies and a network meta-analysis were carried out to assess the relative effect of antidepressants on cognitive dysfunction in major depressive disorder. MEDLINE, Embase, Cochrane, CDSR, and PsychINFO databases; clinical trial registries; and relevant conference abstracts were searched for randomized controlled trials assessing the effects of antidepressants/placebo on cognition. A network meta-analysis comparing antidepressants was conducted using a random effects model. The database search retrieved 11337 citations, of which 72 randomized controlled trials from 103 publications met the inclusion criteria. The review identified 86 cognitive tests assessing the effect of antidepressants on cognitive functioning. However, the Digit Symbol Substitution Test, which targets multiple domains of cognition and is recognized as being sensitive to change, was the only test that was used across 12 of the included randomized controlled trials and that allowed the construction of a stable network suitable for the network meta-analysis. The interventions assessed included selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, and other non-selective serotonin reuptake inhibitors/serotonin-norepinephrine reuptake inhibitors. The network meta-analysis using the Digit Symbol Substitution Test showed that vortioxetine was the only antidepressant that improved cognitive dysfunction on the Digit Symbol Substitution Test vs placebo {standardized mean difference: 0.325 (95% CI = 0.120; 0.529, P=.009}. Compared with other antidepressants, vortioxetine was statistically more efficacious on the Digit Symbol Substitution Test vs escitalopram, nortriptyline, and the selective serotonin reuptake inhibitor and tricyclic antidepressant classes. This study highlighted the large variability in measures used to assess cognitive functioning. The findings on the Digit Symbol Substitution Test indicate differential effects of various antidepressants on improving cognitive function in patients with major depressive disorder. © The Author 2017. Published by Oxford University Press on behalf of CINP.

A Comparison of General Diagnostic Models (GDM) and Bayesian Networks Using a Middle School Mathematics Test

ERIC Educational Resources Information Center

Wu, Haiyan

2013-01-01

General diagnostic models (GDMs) and Bayesian networks are mathematical frameworks that cover a wide variety of psychometric models. Both extend latent class models, and while GDMs also extend item response theory (IRT) models, Bayesian networks can be parameterized using discretized IRT. The purpose of this study is to examine similarities and…

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

PubMed Central

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

2006-01-01

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

Exercise and other non-pharmaceutical interventions for cancer-related fatigue in patients during or after cancer treatment: a systematic review incorporating an indirect-comparisons meta-analysis

PubMed Central

Meichtry, Andre; Nilsson Balfe, Lina; Knols, Ruud H; Verra, Martin L; Taeymans, Jan

2018-01-01

Aim To assess the relative effects of different types of exercise and other non-pharmaceutical interventions on cancer-related fatigue (CRF) in patients during and after cancer treatment. Design Systematic review and indirect-comparisons meta-analysis. Data sources Articles were searched in PubMed, Cochrane CENTRAL and published meta-analyses. Eligibility criteria for selecting studies Randomised studies published up to January 2017 evaluating different types of exercise or other non-pharmaceutical interventions to reduce CRF in any cancer type during or after treatment. Study appraisal and synthesis Risk of bias assessment with PEDro criteria and random effects Bayesian network meta-analysis. Results We included 245 studies. Comparing the treatments with usual care during cancer treatment, relaxation exercise was the highest ranked intervention with a standardisedmean difference (SMD) of −0.77 (95% Credible Interval (CrI) −1.22 to −0.31), while massage (−0.78; −1.55 to −0.01), cognitive–behavioural therapy combined with physical activity (combined CBT, −0.72; −1.34 to −0.09), combined aerobic and resistance training (−0.67; −1.01 to −0.34), resistance training (−0.53; −1.02 to −0.03), aerobic (−0.53; −0.80 to −0.26) and yoga (−0.51; −1.01 to 0.00) all had moderate-to-large SMDs. After cancer treatment, yoga showed the highest effect (−0.68; −0.93 to −0.43). Combined aerobic and resistance training (−0.50; −0.66 to −0.34), combined CBT (−0.45; −0.70 to −0.21), Tai-Chi (−0.45; −0.84 to −0.06), CBT (−0.42; −0.58 to −0.25), resistance training (−0.35; −0.62 to −0.08) and aerobic (−0.33; −0.51 to −0.16) showed all small-to-moderate SMDs. Conclusions Patients can choose among different effective types of exercise and non-pharmaceutical interventions to reduce CRF. PMID:28501804

The image recognition based on neural network and Bayesian decision

NASA Astrophysics Data System (ADS)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Adaptability and phenotypic stability of common bean genotypes through Bayesian inference.

PubMed

Corrêa, A M; Teodoro, P E; Gonçalves, M C; Barroso, L M A; Nascimento, M; Santos, A; Torres, F E

2016-04-27

This study used Bayesian inference to investigate the genotype x environment interaction in common bean grown in Mato Grosso do Sul State, and it also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 13 common bean genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian inference was effective for the selection of upright common bean genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions. According to Bayesian inference, the EMGOPA-201, BAMBUÍ, CNF 4999, CNF 4129 A 54, and CNFv 8025 genotypes had specific adaptability to favorable environments, while the IAPAR 14 and IAC CARIOCA ETE genotypes had specific adaptability to unfavorable environments.

Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample.

PubMed

Le, Quang A; Doctor, Jason N

2011-05-01

As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations.

MetaNET--a web-accessible interactive platform for biological metabolic network analysis.

PubMed

Narang, Pankaj; Khan, Shawez; Hemrom, Anmol Jaywant; Lynn, Andrew Michael

2014-01-01

Metabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level. While tools for the perturbation of metabolic networks are available, their applications are limited and restricted as they require varied dependencies and often a commercial platform for full functionality. We have developed MetaNET, an open source user-friendly platform-independent and web-accessible resource consisting of several pre-defined workflows for metabolic network analysis. MetaNET is a web-accessible platform that incorporates a range of functions which can be combined to produce different simulations related to metabolic networks. These include (i) optimization of an objective function for wild type strain, gene/catalyst/reaction knock-out/knock-down analysis using flux balance analysis. (ii) flux variability analysis (iii) chemical species participation (iv) cycles and extreme paths identification and (v) choke point reaction analysis to facilitate identification of potential drug targets. The platform is built using custom scripts along with the open-source Galaxy workflow and Systems Biology Research Tool as components. Pre-defined workflows are available for common processes, and an exhaustive list of over 50 functions are provided for user defined workflows. MetaNET, available at http://metanet.osdd.net , provides a user-friendly rich interface allowing the analysis of genome-scale metabolic networks under various genetic and environmental conditions. The framework permits the storage of previous results, the ability to repeat analysis and share results with other users over the internet as well as run different tools simultaneously using pre-defined workflows, and user-created custom workflows.

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PubMed Central

Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

2013-01-01

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

The neural basis of hand gesture comprehension: A meta-analysis of functional magnetic resonance imaging studies.

PubMed

Yang, Jie; Andric, Michael; Mathew, Mili M

2015-10-01

Gestures play an important role in face-to-face communication and have been increasingly studied via functional magnetic resonance imaging. Although a large amount of data has been provided to describe the neural substrates of gesture comprehension, these findings have never been quantitatively summarized and the conclusion is still unclear. This activation likelihood estimation meta-analysis investigated the brain networks underpinning gesture comprehension while considering the impact of gesture type (co-speech gestures vs. speech-independent gestures) and task demand (implicit vs. explicit) on the brain activation of gesture comprehension. The meta-analysis of 31 papers showed that as hand actions, gestures involve a perceptual-motor network important for action recognition. As meaningful symbols, gestures involve a semantic network for conceptual processing. Finally, during face-to-face interactions, gestures involve a network for social emotive processes. Our finding also indicated that gesture type and task demand influence the involvement of the brain networks during gesture comprehension. The results highlight the complexity of gesture comprehension, and suggest that future research is necessary to clarify the dynamic interactions among these networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using data mining techniques to predict the severity of bicycle crashes.

PubMed

Prati, Gabriele; Pietrantoni, Luca; Fraboni, Federico

2017-04-01

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

The application of knowledge synthesis methods in agri-food public health: recent advancements, challenges and opportunities.

PubMed

Young, Ian; Waddell, Lisa; Sanchez, Javier; Wilhelm, Barbara; McEwen, Scott A; Rajić, Andrijana

2014-03-01

Knowledge synthesis refers to the integration of findings from individual research studies on a given topic or question into the global knowledge base. The application of knowledge synthesis methods, particularly systematic reviews and meta-analysis, has increased considerably in the agri-food public health sector over the past decade and this trend is expected to continue. The objectives of our review were: (1) to describe the most promising knowledge synthesis methods and their applicability in agri-food public health, and (2) to summarize the recent advancements, challenges, and opportunities in the use of systematic review and meta-analysis methods in this sector. We performed a structured review of knowledge synthesis literature from various disciplines to address the first objective, and used comprehensive insights and experiences in applying these methods in the agri-food public health sector to inform the second objective. We describe five knowledge synthesis methods that can be used to address various agri-food public health questions or topics under different conditions and contexts. Scoping reviews describe the main characteristics and knowledge gaps in a broad research field and can be used to evaluate opportunities for prioritizing focused questions for related systematic reviews. Structured rapid reviews are streamlined systematic reviews conducted within a short timeframe to inform urgent decision-making. Mixed-method and qualitative reviews synthesize diverse sources of contextual knowledge (e.g. socio-cognitive, economic, and feasibility considerations). Systematic reviews are a structured and transparent method used to summarize and synthesize literature on a clearly-defined question, and meta-analysis is the statistical combination of data from multiple individual studies. We briefly describe and discuss key advancements in the use of systematic reviews and meta-analysis, including: risk-of-bias assessments; an overall quality-of-evidence approach; engagement of stakeholders; Bayesian, multivariate, and network meta-analysis; and synthesis of diagnostic test accuracy studies. We also highlight several challenges and opportunities in the conduct of systematic reviews (e.g. inclusion of grey literature, minimizing language bias, and optimizing search strategies) and meta-analysis (e.g. inclusion of observational studies and approaches to address the insufficient reporting of data and significant heterogeneity). Many of these developments have yet to be comprehensively applied and evaluated in an agri-food public health context, and more research is needed in this area. There is a need to strengthen knowledge synthesis capacity and infrastructure at the regional, national, and international levels in this sector to ensure that the best available knowledge is used to inform future decision-making about agri-food public health issues. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Towards systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies

PubMed Central

Cortese, Samuele; Kelly, Clare; Chabernaud, Camille; Proal, Erika; Di Martino, Adriana; Milham, Michael P.; Castellanos, F. Xavier

2013-01-01

Objective To perform a comprehensive meta-analysis of task-based functional MRI studies of Attention-Deficit/Hyperactivity Disorder (ADHD). Method PubMed, Ovid, EMBASE, Web of Science, ERIC, CINHAL, and NeuroSynth were searched for studies published through 06/30/2011. Significant differences in activation of brain regions between individuals with ADHD and comparisons were detected using activation likelihood estimation meta-analysis (p<0.05, corrected). Dysfunctional regions in ADHD were related to seven reference neuronal systems. We performed a set of meta-analyses focused on age groups (children; adults), clinical characteristics (history of stimulant treatment; presence of psychiatric comorbidities), and specific neuropsychological tasks (inhibition; working memory; vigilance/attention). Results Fifty-five studies were included (39 in children, 16 in adults). In children, hypoactivation in ADHD vs. comparisons was found mostly in systems involved in executive functions (frontoparietal network) and attention (ventral attentional network). Significant hyperactivation in ADHD vs. comparisons was observed predominantly within the default, ventral attention, and somatomotor networks. In adults, ADHD-related hypoactivation was predominant in the frontoparietal system, while ADHD-related hyperactivation was present in the visual, dorsal attention, and default networks. Significant ADHD-related dysfunction largely reflected task features and was detected even in the absence of comorbid mental disorders or history of stimulant treatment. Conclusions A growing literature provides evidence of ADHD-related dysfunction within multiple neuronal systems involved in higher-level cognitive functions but also in sensorimotor processes, including the visual system, and in the default network. This meta-analytic evidence extends early models of ADHD pathophysiology focused on prefrontal-striatal circuits. PMID:22983386

Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Evaluation of uncertainty in the adjustment of fundamental constants

NASA Astrophysics Data System (ADS)

Bodnar, Olha; Elster, Clemens; Fischer, Joachim; Possolo, Antonio; Toman, Blaza

2016-02-01

Combining multiple measurement results for the same quantity is an important task in metrology and in many other areas. Examples include the determination of fundamental constants, the calculation of reference values in interlaboratory comparisons, or the meta-analysis of clinical studies. However, neither the GUM nor its supplements give any guidance for this task. Various approaches are applied such as weighted least-squares in conjunction with the Birge ratio or random effects models. While the former approach, which is based on a location-scale model, is particularly popular in metrology, the latter represents a standard tool used in statistics for meta-analysis. We investigate the reliability and robustness of the location-scale model and the random effects model with particular focus on resulting coverage or credible intervals. The interval estimates are obtained by adopting a Bayesian point of view in conjunction with a non-informative prior that is determined by a currently favored principle for selecting non-informative priors. Both approaches are compared by applying them to simulated data as well as to data for the Planck constant and the Newtonian constant of gravitation. Our results suggest that the proposed Bayesian inference based on the random effects model is more reliable and less sensitive to model misspecifications than the approach based on the location-scale model.

Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

PubMed Central

CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

2014-01-01

Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

Bayesian statistics in medicine: a 25 year review.

PubMed

Ashby, Deborah

2006-11-15

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

Double versus single stenting for coronary bifurcation lesions: a meta-analysis.

PubMed

Katritsis, Demosthenes G; Siontis, George C M; Ioannidis, John P A

2009-10-01

Several trials have addressed whether bifurcation lesions require stenting of both the main vessel and side branch, but uncertainty remains on the benefits of such double versus single stenting of the main vessel only. We have conducted a meta-analysis of randomized trials including patients with coronary bifurcation lesions who were randomly selected to undergo percutaneous coronary intervention by either double or single stenting. Six studies (n=1642 patients) were eligible. There was increased risk of myocardial infarction with double stenting (risk ratio, 1.78; P=0.001 by fixed effects; risk ratio, 1.49 with Bayesian meta-analysis). The summary point estimate suggested also an increased risk of stent thrombosis with double stenting, but the difference was not nominally significant given the sparse data (risk ratio, 1.85; P=0.19). No obvious difference was seen for death (risk ratio, 0.81; P=0.66) and target lesion revascularization (risk ratio, 1.09; P=0.67). Stenting of both the main vessel and side branch in bifurcation lesions may increase myocardial infarction and stent thrombosis risk compared with stenting of the main vessel only.

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.

PubMed

Hutton, Brian; Salanti, Georgia; Caldwell, Deborah M; Chaimani, Anna; Schmid, Christopher H; Cameron, Chris; Ioannidis, John P A; Straus, Sharon; Thorlund, Kristian; Jansen, Jeroen P; Mulrow, Cynthia; Catalá-López, Ferrán; Gøtzsche, Peter C; Dickersin, Kay; Boutron, Isabelle; Altman, Douglas G; Moher, David

2015-06-02

The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.

Bayesian Inference for Time Trends in Parameter Values: Case Study for the Ageing PSA Network of the European Commission

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dana L. Kelly; Albert Malkhasyan

2010-06-01

There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant in time. As a result, Bayesian inference tends to incorporate many years of plant operation, over which there have been significant changes in plant operational and maintenance practices, plant management, etc. These changes can cause significant changes in parameter values over time; however, failure to perform Bayesian inference in the proper time-dependent framework can mask these changes. Failure to question the assumption of constant parameter values, and failure to perform Bayesian inference in the proper time-dependent framework were noted as important issues in NUREG/CR-6813, performedmore » for the U. S. Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards in 2003. That report noted that “industry lacks tools to perform time-trend analysis with Bayesian updating.” This paper describes an application of time-dependent Bayesian inference methods developed for the European Commission Ageing PSA Network. These methods utilize open-source software, implementing Markov chain Monte Carlo sampling. The paper also illustrates the development of a generic prior distribution, which incorporates multiple sources of generic data via weighting factors that address differences in key influences, such as vendor, component boundaries, conditions of the operating environment, etc.« less

System Analysis by Mapping a Fault-tree into a Bayesian-network

NASA Astrophysics Data System (ADS)

Sheng, B.; Deng, C.; Wang, Y. H.; Tang, L. H.

2018-05-01

In view of the limitations of fault tree analysis in reliability assessment, Bayesian Network (BN) has been studied as an alternative technology. After a brief introduction to the method for mapping a Fault Tree (FT) into an equivalent BN, equations used to calculate the structure importance degree, the probability importance degree and the critical importance degree are presented. Furthermore, the correctness of these equations is proved mathematically. Combining with an aircraft landing gear’s FT, an equivalent BN is developed and analysed. The results show that richer and more accurate information have been achieved through the BN method than the FT, which demonstrates that the BN is a superior technique in both reliability assessment and fault diagnosis.

Research on Risk Manage of Power Construction Project Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Jia, Zhengyuan; Fan, Zhou; Li, Yong

With China's changing economic structure and increasingly fierce competition in the market, the uncertainty and risk factors in the projects of electric power construction are increasingly complex, the projects will face huge risks or even fail if we don't consider or ignore these risk factors. Therefore, risk management in the projects of electric power construction plays an important role. The paper emphatically elaborated the influence of cost risk in electric power projects through study overall risk management and the behavior of individual in risk management, and introduced the Bayesian network to the project risk management. The paper obtained the order of key factors according to both scene analysis and causal analysis for effective risk management.

Bayesian network interface for assisting radiology interpretation and education

NASA Astrophysics Data System (ADS)

Duda, Jeffrey; Botzolakis, Emmanuel; Chen, Po-Hao; Mohan, Suyash; Nasrallah, Ilya; Rauschecker, Andreas; Rudie, Jeffrey; Bryan, R. Nick; Gee, James; Cook, Tessa

2018-03-01

In this work, we present the use of Bayesian networks for radiologist decision support during clinical interpretation. This computational approach has the advantage of avoiding incorrect diagnoses that result from known human cognitive biases such as anchoring bias, framing effect, availability bias, and premature closure. To integrate Bayesian networks into clinical practice, we developed an open-source web application that provides diagnostic support for a variety of radiology disease entities (e.g., basal ganglia diseases, bone lesions). The Clinical tool presents the user with a set of buttons representing clinical and imaging features of interest. These buttons are used to set the value for each observed feature. As features are identified, the conditional probabilities for each possible diagnosis are updated in real time. Additionally, using sensitivity analysis, the interface may be set to inform the user which remaining imaging features provide maximum discriminatory information to choose the most likely diagnosis. The Case Submission tools allow the user to submit a validated case and the associated imaging features to a database, which can then be used for future tuning/testing of the Bayesian networks. These submitted cases are then reviewed by an assigned expert using the provided QC tool. The Research tool presents users with cases with previously labeled features and a chosen diagnosis, for the purpose of performance evaluation. Similarly, the Education page presents cases with known features, but provides real time feedback on feature selection.

Meta-analytically informed network analysis of resting state FMRI reveals hyperconnectivity in an introspective socio-affective network in depression.

PubMed

Schilbach, Leonhard; Müller, Veronika I; Hoffstaedter, Felix; Clos, Mareike; Goya-Maldonado, Roberto; Gruber, Oliver; Eickhoff, Simon B

2014-01-01

Alterations of social cognition and dysfunctional interpersonal expectations are thought to play an important role in the etiology of depression and have, thus, become a key target of psychotherapeutic interventions. The underlying neurobiology, however, remains elusive. Based upon the idea of a close link between affective and introspective processes relevant for social interactions and alterations thereof in states of depression, we used a meta-analytically informed network analysis to investigate resting-state functional connectivity in an introspective socio-affective (ISA) network in individuals with and without depression. Results of our analysis demonstrate significant differences between the groups with depressed individuals showing hyperconnectivity of the ISA network. These findings demonstrate that neurofunctional alterations exist in individuals with depression in a neural network relevant for introspection and socio-affective processing, which may contribute to the interpersonal difficulties that are linked to depressive symptomatology.

Meta-Analytically Informed Network Analysis of Resting State fMRI Reveals Hyperconnectivity in an Introspective Socio-Affective Network in Depression

PubMed Central

Schilbach, Leonhard; Müller, Veronika I.; Hoffstaedter, Felix; Clos, Mareike; Goya-Maldonado, Roberto

2014-01-01

Alterations of social cognition and dysfunctional interpersonal expectations are thought to play an important role in the etiology of depression and have, thus, become a key target of psychotherapeutic interventions. The underlying neurobiology, however, remains elusive. Based upon the idea of a close link between affective and introspective processes relevant for social interactions and alterations thereof in states of depression, we used a meta-analytically informed network analysis to investigate resting-state functional connectivity in an introspective socio-affective (ISA) network in individuals with and without depression. Results of our analysis demonstrate significant differences between the groups with depressed individuals showing hyperconnectivity of the ISA network. These findings demonstrate that neurofunctional alterations exist in individuals with depression in a neural network relevant for introspection and socio-affective processing, which may contribute to the interpersonal difficulties that are linked to depressive symptomatology. PMID:24759619

Variable Discretisation for Anomaly Detection using Bayesian Networks

DTIC Science & Technology

2017-01-01

UNCLASSIFIED DST- Group –TR–3328 1 Introduction Bayesian network implementations usually require each variable to take on a finite number of mutually...UNCLASSIFIED Variable Discretisation for Anomaly Detection using Bayesian Networks Jonathan Legg National Security and ISR Division Defence Science...and Technology Group DST- Group –TR–3328 ABSTRACT Anomaly detection is the process by which low probability events are automatically found against a

Impact assessment of extreme storm events using a Bayesian network

USGS Publications Warehouse

den Heijer, C.(Kees); Knipping, Dirk T.J.A.; Plant, Nathaniel G.; van Thiel de Vries, Jaap S. M.; Baart, Fedor; van Gelder, Pieter H. A. J. M.

2012-01-01

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.

Analysis and meta-analysis of single-case designs: an introduction.

PubMed

Shadish, William R

2014-04-01

The last 10 years have seen great progress in the analysis and meta-analysis of single-case designs (SCDs). This special issue includes five articles that provide an overview of current work on that topic, including standardized mean difference statistics, multilevel models, Bayesian statistics, and generalized additive models. Each article analyzes a common example across articles and presents syntax or macros for how to do them. These articles are followed by commentaries from single-case design researchers and journal editors. This introduction briefly describes each article and then discusses several issues that must be addressed before we can know what analyses will eventually be best to use in SCD research. These issues include modeling trend, modeling error covariances, computing standardized effect size estimates, assessing statistical power, incorporating more accurate models of outcome distributions, exploring whether Bayesian statistics can improve estimation given the small samples common in SCDs, and the need for annotated syntax and graphical user interfaces that make complex statistics accessible to SCD researchers. The article then discusses reasons why SCD researchers are likely to incorporate statistical analyses into their research more often in the future, including changing expectations and contingencies regarding SCD research from outside SCD communities, changes and diversity within SCD communities, corrections of erroneous beliefs about the relationship between SCD research and statistics, and demonstrations of how statistics can help SCD researchers better meet their goals. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Efficacy and safety of left atrial appendage closure versus medical treatment in atrial fibrillation: a network meta-analysis from randomised trials.

PubMed

Sahay, Shweta; Nombela-Franco, Luis; Rodes-Cabau, Josep; Jimenez-Quevedo, Pilar; Salinas, Pablo; Biagioni, Corina; Nuñez-Gil, Ivan; Gonzalo, Nieves; de Agustín, Jose Alberto; Del Trigo, Maria; Perez de Isla, Leopoldo; Fernández-Ortiz, Antonio; Escaned, Javier; Macaya, Carlos

2017-01-15

The effectiveness of vitamin K antagonist (VKA) versus placebo and antiplatelet therapy (APT) is well established for stroke prevention in atrial fibrillation (AF). Non-vitamin K antagonist oral anticoagulants (NOAC) are mostly superior to VKA in stroke and intracranial bleeding prevention. Recent randomised controlled trials (RCTs) suggested the non-inferiority of percutaneous left atrial appendage closure (LAAC) versus VKA. However, comparisons between LAAC versus placebo, APT or NOAC are lacking. The purpose of this network meta-analysis was to assess the efficacy and safety of LAAC compared with other strategies for stroke prevention in patients with AF. We pooled together all RCTs comparing warfarin with placebo, APT or NOAC in patients with AF using meta-analysis guidelines. Two major trials of LAAC were also included and a network meta-analysis was performed to compare the impact of LAAC on mortality, stroke/systemic embolism (SE) and major bleeding in relation to medical treatment. The network meta-analysis included 19 RCTs with a total of 87 831 patients with AF receiving anticoagulants, APT, placebo or LAAC. Indirect comparison with network meta-analysis using warfarin as the common comparator revealed efficacy benefit favouring LAAC as compared with placebo (mortality: HR 0.38, 95% CI 0.22 to 0.67, p<0.001; stroke/SE: HR 0.24, 95% CI 0.11 to 0.52, p<0.001) and APT (mortality: HR 0.58, 95% CI 0.37 to 0.91, p=0.0018; stroke/SE: HR 0.44, 95% CI 0.23 to 0.86, p=0.017) and similar to NOAC (mortality: HR 0.76, 95% CI 0.50 to 1.16, p=0.211; stroke/SE: HR 1.01, 95% CI 0.53 to 1.92, p=0.969). LAAC showed comparable rates of major bleeding when compared with placebo (HR 2.33, 95% CI 0.67 to 8.09, p=0.183), APT (HR 0.75, 95% CI 0.30 to 1.88, p=0.542) and NOAC (HR 0.80, 95% CI 0.33 to 1.94, p=0.615). The findings of this meta-analysis suggest that LAAC is superior to placebo and APT, and comparable to NOAC for preventing mortality and stroke or SE, with similar bleeding risk in patients with non-valvular AF. However, these results should be interpreted with caution and more studies are needed to further substantiate this advantage, in view of the wide CIs with some variables in the current meta-analysis. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Acute Antipsychotic Treatment of Children and Adolescents With Schizophrenia-Spectrum Disorders: A Systematic Review and Network Meta-Analysis.

PubMed

Pagsberg, Anne Katrine; Tarp, Simon; Glintborg, Dorte; Stenstrøm, Anne Dorte; Fink-Jensen, Anders; Correll, Christoph Ulrich; Christensen, Robin

2017-03-01

To determine the comparative efficacy and safety of antipsychotics for youth with early-onset schizophrenia using network meta-analytic methods combining direct and indirect trial data. The authors systematically searched MEDLINE, the Cochrane Library, and clinicaltrials.gov and selected randomized controlled trials allocating youth with schizophrenia spectrum disorders to a (non-clozapine) antipsychotic versus placebo or another antipsychotic. Major efficacy outcomes were Positive and Negative Syndrome Scale (PANSS) total and positive symptoms. Major safety outcomes were weight, plasma triglyceride levels, extrapyramidal symptoms, akathisia, and all-cause discontinuation. Sixteen additional outcomes were analyzed. A random-effects arm-based network meta-analysis was applied, and consistency was assessed by pairwise meta-analysis. Confidence in PANSS total estimates was assessed by applying the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Twelve 6- to 12-week trials (N = 2,158; 8-19 years old; 61% boys) involving 8 antipsychotics (aripiprazole, asenapine, paliperidone, risperidone, quetiapine, olanzapine, molindone, and ziprasidone) were analyzed. PANSS total symptom change was comparable among antipsychotics (low- to moderate-quality evidence), except ziprasidone (very low- to low-quality evidence), and all antipsychotics were superior to placebo (low- to high-quality evidence), except ziprasidone and asenapine (low- to moderate-quality evidence). PANSS positive changes and additional efficacy outcomes were comparable among antipsychotics. Weight gain was primarily associated with olanzapine; extrapyramidal symptoms and akathisia were associated with molindone; and prolactin increased with risperidone, paliperidone, and olanzapine. Serious adverse events, discontinuation of treatment, sedation, insomnia, or change in triglycerides did not differ among antipsychotics. This network meta-analysis showed comparable efficacy among antipsychotics for early-onset schizophrenia, except that efficacy appeared inferior for ziprasidone and unclear for asenapine. Adverse reaction profiles varied substantially among the investigated antipsychotics and were largely consistent with prior findings in adults. Protocol registration information-Antipsychotic Treatment for Children With Schizophrenia Spectrum Disorders: Network Meta-Analysis of Randomised Trials; https://www.crd.york.ac.uk/PROSPERO/; CRD42013006676. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Comparative effects of different dietary approaches on blood pressure in hypertensive and pre-hypertensive patients: A systematic review and network meta-analysis.

PubMed

Schwingshackl, Lukas; Chaimani, Anna; Schwedhelm, Carolina; Toledo, Estefania; Pünsch, Marina; Hoffmann, Georg; Boeing, Heiner

2018-05-02

Pairwise meta-analyses have shown beneficial effects of individual dietary approaches on blood pressure but their comparative effects have not been established. Therefore we performed a systematic review of different dietary intervention trials and estimated the aggregate blood pressure effects through network meta-analysis including hypertensive and pre-hypertensive patients. PubMed, Cochrane CENTRAL, and Google Scholar were searched until June 2017. The inclusion criteria were defined as follows: i) Randomized trial with a dietary approach; ii) hypertensive and pre-hypertensive adult patients; and iii) minimum intervention period of 12 weeks. In order to determine the pooled effect of each intervention relative to each of the other intervention for both diastolic and systolic blood pressure (SBP and DBP), random effects network meta-analysis was performed. A total of 67 trials comparing 13 dietary approaches (DASH, low-fat, moderate-carbohydrate, high-protein, low-carbohydrate, Mediterranean, Palaeolithic, vegetarian, low-GI/GL, low-sodium, Nordic, Tibetan, and control) enrolling 17,230 participants were included. In the network meta-analysis, the DASH, Mediterranean, low-carbohydrate, Palaeolithic, high-protein, low-glycaemic index, low-sodium, and low-fat dietary approaches were significantly more effective in reducing SBP (-8.73 to -2.32 mmHg) and DBP (-4.85 to -1.27 mmHg) compared to a control diet. According to the SUCRAs, the DASH diet was ranked the most effective dietary approach in reducing SBP (90%) and DBP (91%), followed by the Palaeolithic, and the low-carbohydrate diet (ranked 3rd for SBP) or the Mediterranean diet (ranked 3rd for DBP). For most comparisons, the credibility of evidence was rated very low to moderate, with the exception for the DASH vs. the low-fat dietary approach for which the quality of evidence was rated high. The present network meta-analysis suggests that the DASH dietary approach might be the most effective dietary measure to reduce blood pressure among hypertensive and pre-hypertensive patients based on high quality evidence.

Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model

PubMed Central

Baskerville, Edward B.; Dobson, Andy P.; Bedford, Trevor; Allesina, Stefano; Anderson, T. Michael; Pascual, Mercedes

2011-01-01

Food webs, networks of feeding relationships in an ecosystem, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. A standard approach in food-web analysis, and network analysis in general, has been to identify compartments, or modules, defined by many links within compartments and few links between them. This approach can identify large habitat boundaries in the network but may fail to identify other important structures. Empirical analyses of food webs have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure using a flexible definition that can describe both functional trophic roles and standard compartments. We apply this method to a newly compiled plant-mammal food web from the Serengeti ecosystem that includes high taxonomic resolution at the plant level, allowing a simultaneous examination of the signature of both habitat and trophic roles in network structure. We find that groups at the plant level reflect habitat structure, coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial pattern, in contrast to the standard compartments typically identified. The network topology supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence. Furthermore, our Bayesian approach provides a powerful, flexible framework for the study of network structure, and we believe it will prove instrumental in a variety of biological contexts. PMID:22219719

How do smoking cessation medicines compare with respect to their neuropsychiatric safety? A protocol for a systematic review, network meta-analysis and cost-effectiveness analysis.

PubMed

Thomas, Kyla H; Caldwell, Deborah; Dalili, Michael N; Gunnell, David; Munafò, Marcus R; Stevenson, Matt; Welton, Nicky J

2017-06-17

Cigarette smoking is one of the leading causes of early death in the UK and worldwide. Public health guidance recommends the use of varenicline, bupropion and nicotine replacement therapy (NRT) as smoking cessation aids in the UK. Additionally, the first electronic cigarette has been licensed for use as a smoking cessation medicine. However, there are ongoing concerns about the safety of these medicines. We present a protocol for a systematic review and network meta-analysis (NMA) to determine how these smoking cessation medicines compare to each other with respect to their neuropsychiatric safety in adult smokers. Secondary aims include updating the evidence regarding the effectiveness and cardiovascular safety of these medicines for use in a cost-effectiveness analysis. We will include randomised controlled trials and observational studies with control groups comparing monotherapy with varenicline, bupropion, NRT or electronic cigarette and combination therapies to each other, placebo or usual care. The primary composite safety outcome will be serious adverse events, defined as events that resulted in death, were life threatening, required hospitalisation or resulted in significant disability or congenital/birth defect. The preferred effectiveness outcome will be sustained smoking cessation defined as abstinence for a minimum of 6 months as determined by biochemical validation. We will include trials identified by previous reviews and search relevant databases for newly published trials as well as contacting study authors to identify unpublished information. We will conduct fixed-effect and random-effect meta-analyses for each pairwise comparison of treatments and outcome; where these estimates differ, we will consider reasons for heterogeneity, quantified using the between-study variance (τ 2 ). For each outcome, we will construct a NMA in a Bayesian framework which will be compared with the pair-wise results, allowing us to rank treatments. The effectiveness estimates from the NMA will be entered into a probabilistic economic model. Ethics approval is not required for this evidence synthesis study as it involves analysis of secondary data from randomised controlled trials and observational studies. The review will make an important contribution to the knowledge base around the effectiveness, safety and cost-effectiveness of smoking cessation medicines. Results will be disseminated to the general public, healthcare practitioners and clinicians, academics, industry and policy makers. CRD42016041302. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Analysis on the urban street network of Korea: Connections between topology and meta-information

NASA Astrophysics Data System (ADS)

Lee, Byoung-Hwa; Jung, Woo-Sung

2018-05-01

Cities consist of infrastructure that enables transportation, which can be considered as topology in abstract terms. Once cities are physically organized in terms of infrastructure, people interact with each other to form the values, which can be regarded as the meta-information of the cities. The topology and meta-information coevolve together as the cities are developed. In this study, we investigate the relationship between the topology and meta-information for a street network, which has aspects of both a complex network and planar graph. The degree of organization of a street structure determines the efficiency and productivity of the city in that they act as blood vessels to transport people, goods, and information. We analyze the topological aspect of a street network using centralities including the betweenness, closeness, straightness, and information. We classify the cities into several groups that share common meta-information based on the centrality, indicating that the topological factor of the street structure is closely related to meta-information through coevolution. We also obtain the coevolution in the planned cities using the regularity. Another footprint is the relation between the street segment length and the population, which shows the sublinear scaling.

Platelet-rich plasma versus autologous blood versus steroid injection in lateral epicondylitis: systematic review and network meta-analysis.

PubMed

Arirachakaran, Alisara; Sukthuayat, Amnat; Sisayanarane, Thaworn; Laoratanavoraphong, Sorawut; Kanchanatawan, Wichan; Kongtharvonskul, Jatupon

2016-06-01

Clinical outcomes between the use of platelet-rich plasma (PRP), autologous blood (AB) and corticosteroid (CS) injection in lateral epicondylitis are still controversial. A systematic review and network meta-analysis of randomized controlled trials was conducted with the aim of comparing relevant clinical outcomes between the use of PRP, AB and CS injection. Medline and Scopus databases were searched from inception to January 2015. A network meta-analysis was performed by applying weight regression for continuous outcomes and a mixed-effect Poisson regression for dichotomous outcomes. Ten of 374 identified studies were eligible. When compared to CS, AB injection showed significantly improved effects with unstandardized mean differences (UMD) in pain visual analog scale (VAS), Disabilities of Arm Shoulder and Hand (DASH), Patient-Related Tennis Elbow Evaluation (PRTEE) score and pressure pain threshold (PPT) of -2.5 (95 % confidence interval, -3.5, -1.5), -25.5 (-33.8, -17.2), -5.3 (-9.1, -1.6) and 9.9 (5.6, 14.2), respectively. PRP injections also showed significantly improved VAS and DASH scores when compared with CS. PRP showed significantly better VAS with UMD when compared to AB injection. AB injection has a higher risk of adverse effects, with a relative risk of 1.78 (1.00, 3.17), when compared to CS. The network meta-analysis suggested no statistically significant difference in multiple active treatment comparisons of VAS, DASH and PRTEE when comparing PRP and AB injections. However, AB injection had improved DASH score and PPT when compared with PRP injection. In terms of adverse effects, AB injection had a higher risk than PRP injection. This network meta-analysis provided additional information that PRP injection can improve pain and lower the risk of complications, whereas AB injection can improve pain, disabilities scores and pressure pain threshold but has a higher risk of complications. Level I evidence.

A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients.

PubMed

Green, Nancy

2005-04-01

We developed a Bayesian network coding scheme for annotating biomedical content in layperson-oriented clinical genetics documents. The coding scheme supports the representation of probabilistic and causal relationships among concepts in this domain, at a high enough level of abstraction to capture commonalities among genetic processes and their relationship to health. We are using the coding scheme to annotate a corpus of genetic counseling patient letters as part of the requirements analysis and knowledge acquisition phase of a natural language generation project. This paper describes the coding scheme and presents an evaluation of intercoder reliability for its tag set. In addition to giving examples of use of the coding scheme for analysis of discourse and linguistic features in this genre, we suggest other uses for it in analysis of layperson-oriented text and dialogue in medical communication.

Bayesian network learning for natural hazard assessments

NASA Astrophysics Data System (ADS)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables and incomplete observations. Further studies rise the challenge of relying on very small data sets. Since parameter estimates for complex models based on few observations are unreliable, it is necessary to focus on simplified, yet still meaningful models. A so called Markov Blanket approach is developed to identify the most relevant model components and to construct a simple Bayesian network based on those findings. Since the proceeding is completely data driven, it can easily be transferred to various applications in natural hazard domains. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training programme GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at Potsdam University.

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

ERIC Educational Resources Information Center

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Dynamic Bayesian network modeling for longitudinal brain morphometry

PubMed Central

Chen, Rong; Resnick, Susan M; Davatzikos, Christos; Herskovits, Edward H

2011-01-01

Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our method uses a dynamic Bayesian network to represent evolving inter-regional dependencies. The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes. We validated our approach by analyzing a simulated atrophy study, and found that this approach requires only a small number of samples to detect the ground-truth temporal model. We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment — the Baltimore Longitudinal Study of Aging. We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group. PMID:21963916

Interactive Planning under Uncertainty with Casual Modeling and Analysis

DTIC Science & Technology

2006-01-01

Tool ( CAT ), a system for creating and analyzing causal models similar to Bayes networks. In order to use CAT as a tool for planning, users go through...an iterative process in which they use CAT to create and an- alyze alternative plans. One of the biggest difficulties is that the number of possible...Causal Analysis Tool ( CAT ), which is a tool for representing and analyzing causal networks sim- ilar to Bayesian networks. In order to represent plans

Comparative effectiveness of different wound dressings for patients with partial-thickness burns: study protocol of a systematic review and a Bayesian framework network meta-analysis.

PubMed

Jiang, Qiong; Chen, Zhao-Hong; Wang, Shun-Bin; Chen, Xiao-Dong

2017-03-22

Selecting a suitable wound dressing for patients with partial-thickness burns (PTBs) is important in wound care. However, the comparative effectiveness of different dressings has not been studied. We report the protocol of a network meta-analysis designed to combine direct and indirect evidence of wound dressings in the management of PTB. We will search for randomised controlled trials (RCTs) evaluating the wound-healing effect of a wound dressing in the management of PTB. Searches will be conducted in MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Cochrane Wounds Group Specialised Register and CINAHL. A comprehensive search strategy is developed to retrieve articles reporting potentially eligible RCTs. Besides, we will contact the experts in the field and review the conference proceedings to locate non-published studies. The reference lists of articles will be reviewed for any candidate studies. Two independent reviewers will screen titles and abstracts of the candidate articles. All eligible RCTs will be obtained in full text to perform a review. Disagreement on eligibility of an RCT will be solved by group discussion. The information of participants, interventions, comparisons and outcomes from included RCTs will be recorded and summarised. The primary outcome is time to complete wound healing. Secondary outcomes include the proportion of burns completely healed at the end of treatment, change in wound surface area at the end of treatment, incidence of adverse events, etc. The result of this review will provide evidence for the comparative effectiveness of different wound dressings in the management of PTB. It will also facilitate decision-making in choosing a suitable wound dressing. We will disseminate the review through a peer-review journal and conference abstracts or posters. PROSPERO CRD42016041574; Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Nephropathy after administration of iso-osmolar and low-osmolar contrast media: evidence from a network meta-analysis.

PubMed

Biondi-Zoccai, Giuseppe; Lotrionte, Marzia; Thomsen, Henrik S; Romagnoli, Enrico; D'Ascenzo, Fabrizio; Giordano, Arturo; Frati, Giacomo

2014-03-15

Contrast-induced nephropathy (CIN) may be a severe complication to the administration of iodine-based contrast media for diagnostic or interventional procedure using radiation exposure. Whether there is a difference in nephrotoxic potential between the various agents is uncertain. We aimed to perform a systematic review and network meta-analysis of randomized trials on iodine-based contrast agents. Randomized trials of low-osmolar or iso-osmolar contrast media were searched in CENTRAL, Google Scholar, MEDLINE/PubMed, and Scopus. Risk of CIN was appraised within a hierarchical Bayesian model computing absolute rates (AR) and odds ratios (OR) with 95% credibility intervals, and probability of being best (Pbest) for each agent. A total of 42 trials (10048 patients) were included focusing on 7 different iodine-based contrast media. Risk of CIN was similarly low with iodixanol (AR=5.7% [2.2%-13.9%], Pbest=18.8%), iomeprol (AR=6.0% [2.2%-15.4%], Pbest=24.8%), iopamidol (AR=6.1% [2.2%-15.5%], Pbest=21.5%), and ioversol (AR=6.0% [2.1%-16.4%], Pbest=31.3%). Conversely, CIN was twice as common with iohexol (AR=11.2% [4.1%-29.5%], Pbest=0.1%) and ioxaglate (AR=11.0% [4.0%-26.9%], Pbest<0.1%), with both proving less safe than iodixanol (respectively OR=2.18 [1.22-3.92] and 2.05 [1.26-3.29]), iomeprol (OR=2.08 [1.04-4.17] and 1.96 [1.06-3.48]) and iopamidol (OR=2.04 [1.15-3.85] and 1.92 [1.06-3.45]). Data on iopromide were less conclusive (AR=6.9% [2.6%-17.1%], Pbest=3.6%). Iodixanol, iomeprol, iopamidol and ioversol are iodine-based contrast media with a similar renal safety profile. Iohexol and ioxaglate have a poorer renal safety profile, whereas further data may be required on iopromide. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Support surfaces for pressure ulcer prevention: A network meta-analysis

PubMed Central

Dumville, Jo C.; Cullum, Nicky

2018-01-01

Background Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult. Objectives To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness. Methods We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence. Main results We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR) 0.42, 95% confidence intervals (CI) 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively). The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence). Conclusions This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard hospital surfaces. Most prevention evidence was of low or very low certainty, and more research is required to reduce these uncertainties. PMID:29474359

Support surfaces for pressure ulcer prevention: A network meta-analysis.

PubMed

Shi, Chunhu; Dumville, Jo C; Cullum, Nicky

2018-01-01

Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult. To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness. We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence. We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR) 0.42, 95% confidence intervals (CI) 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively). The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence). This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard hospital surfaces. Most prevention evidence was of low or very low certainty, and more research is required to reduce these uncertainties.

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

NASA Astrophysics Data System (ADS)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

A Network Meta-Analysis of the Relative Efficacy of Treatments for Actinic Keratosis of the Face or Scalp in Europe

PubMed Central

Vegter, Stefan; Tolley, Keith

2014-01-01

Background Several treatments are available for actinic keratosis (AK) on the face and scalp. Most treatment modalities were compared to placebo and therefore little is known on their relative efficacy. Objectives To compare the different treatments for mild to moderate AK on the face and scalp available in clinical practice in Europe. Methods A network meta-analysis (NMA) was performed on the outcome “complete patient clearance”. Ten treatment modalities were included: two 5-aminolaevulinic acid photodynamic therapies (ALA-PDT), applied as gel (BF-200 ALA) or patch; methyl-aminolevulinate photodynamic therapy (MAL-PDT); three modalities with imiquimod (IMI), applied as a 4-week or 16-week course with 5% imiquimod, or a 2–3 week course with 3.75% imiquimod; cryotherapy; diclofenac 3% in 2.5% hyaluronic acid; 0.5% 5-fluorouracil (5-FU); and ingenol mebutate (IMB). The only data available for 5% 5-FU was from one small study and was determined to be too limited to be reliably included in the analysis. For BF-200 ALA and MAL-PDT, data from illumination with narrow-band lights were selected as these are typically used in clinical practice. The NMA was performed with a random-effects Bayesian model. Results 25 trials on 5,562 patients were included in the NMA. All active treatments were significantly better than placebo. BF-200 ALA showed the highest efficacy compared to placebo to achieve total patient clearance. BF-200 ALA had the highest probability to be the best treatment and the highest SUCRA score (64.8% and 92.1%), followed by IMI 5% 4 weeks (10.1% and 74.2%) and 5-FU 0.5% (7.2% and 66.8%). Conclusions This NMA showed that BF-200 ALA, using narrow-band lights, was the most efficacious treatment for mild to moderate AK on the face and scalp. This analysis is relevant for clinical decision making and health technology assessment, assisting the improved management of AK. PMID:24892649

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Bayesian networks of age estimation and classification based on dental evidence: A study on the third molar mineralization.

PubMed

Sironi, Emanuele; Pinchi, Vilma; Pradella, Francesco; Focardi, Martina; Bozza, Silvia; Taroni, Franco

2018-04-01

Not only does the Bayesian approach offer a rational and logical environment for evidence evaluation in a forensic framework, but it also allows scientists to coherently deal with uncertainty related to a collection of multiple items of evidence, due to its flexible nature. Such flexibility might come at the expense of elevated computational complexity, which can be handled by using specific probabilistic graphical tools, namely Bayesian networks. In the current work, such probabilistic tools are used for evaluating dental evidence related to the development of third molars. A set of relevant properties characterizing the graphical models are discussed and Bayesian networks are implemented to deal with the inferential process laying beyond the estimation procedure, as well as to provide age estimates. Such properties include operationality, flexibility, coherence, transparence and sensitivity. A data sample composed of Italian subjects was employed for the analysis; results were in agreement with previous studies in terms of point estimate and age classification. The influence of the prior probability elicitation in terms of Bayesian estimate and classifies was also analyzed. Findings also supported the opportunity to take into consideration multiple teeth in the evaluative procedure, since it can be shown this results in an increased robustness towards the prior probability elicitation process, as well as in more favorable outcomes from a forensic perspective. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Autologous Transplantation for Newly Diagnosed Multiple Myeloma in the Era of Novel Agent Induction: A Systematic Review and Meta-analysis.

PubMed

Dhakal, Binod; Szabo, Aniko; Chhabra, Saurabh; Hamadani, Mehdi; D'Souza, Anita; Usmani, Saad Z; Sieracki, Rita; Gyawali, Bishal; Jackson, Jeffrey L; Asimakopoulos, Fotis; Hari, Parameswaran N

2018-03-01

The role of high-dose therapy with melphalan followed by autologous stem cell transplant (HDT/ASCT) in patients with multiple myeloma continues to be debated in the context of novel agent induction. To perform a systematic review, conventional meta-analysis, and network meta-analysis of all phase 3 randomized clinical trials (RCTs) evaluating the role of HDT/ASCT. We performed a systematic literature search of Cochrane Central, MEDLINE, and Scopus from January 2000 through April 2017 and relevant annual meeting abstracts from January 2014 to December 2016. The following search terms were used: "myeloma" combined with "autologous," "transplant," "myeloablative," or "stem cell." Phase 3 RCTs comparing HDT/ASCT with standard-dose therapy (SDT) using novel agents were assessed. Studies comparing single HDT/ASCT with bortezomib, lenalidomide, and dexamethasone consolidation and tandem transplantation were included for network meta-analysis. For the random effects meta-analysis, we used hazard ratios (HRs) and corresponding 95% CIs. The primary outcome was progression-free survival (PFS). Overall survival (OS), complete response, and treatment-related mortality were secondary outcomes. A total of 4 RCTs (2421 patients) for conventional meta-analysis and 5 RCTs (3171 patients) for network meta-analysis were selected. The combined odds for complete response were 1.27 (95% CI, 0.97-1.65; P = .07) with HDT/ASCT when compared with SDT. The combined HR for PFS was 0.55 (95% CI, 0.41-0.74; P < .001) and 0.76 for OS (95% CI, 0.42-1.36; P = .20) in favor of HDT. Meta-regression showed that longer follow-up was associated with superior PFS (HR/mo, 0.98; 95% CI, 0.96-0.99; P = .03) and OS (HR/mo, 0.90; 95% CI, 0.84-0.96; P = .002). For PFS, tandem HDT/ASCT had the most favorable HR (0.49; 95% CI, 0.37-0.65) followed by single HDT/ASCT with bortezomib, lenalidomide, and dexamethasone (HR, 0.53; 95% CI, 0.37-0.76) and single HDT/ASCT alone (HR, 0.68; 95% CI, 0.53-0.87) compared with SDT. For OS, none of the HDT/ASCT-based approaches had a significant effect on survival. Treatment-related mortality with HDT/ASCT was minimal (<1%). The results of the conventional meta-analysis and network meta-analysis of all the phase 3 RCTs showed that HDT/ASCT was associated with superior PFS with minimal toxic effects compared with SDT. Both tandem HDT/ASCT and single HDT/ASCT with bortezomib, lenalidomide, and dexamethasone were superior to single HDT/ASCT alone and SDT for PFS, but OS was similar across the 4 approaches. Longer follow-up may better delineate any OS benefit; however, is likely to be affected by effective postrelapse therapy.

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Neurological soft signs are not "soft" in brain structure and functional networks: evidence from ALE meta-analysis.

PubMed

Zhao, Qing; Li, Zhi; Huang, Jia; Yan, Chao; Dazzan, Paola; Pantelis, Christos; Cheung, Eric F C; Lui, Simon S Y; Chan, Raymond C K

2014-05-01

Neurological soft signs (NSS) are associated with schizophrenia and related psychotic disorders. NSS have been conventionally considered as clinical neurological signs without localized brain regions. However, recent brain imaging studies suggest that NSS are partly localizable and may be associated with deficits in specific brain areas. We conducted an activation likelihood estimation meta-analysis to quantitatively review structural and functional imaging studies that evaluated the brain correlates of NSS in patients with schizophrenia and other psychotic disorders. Six structural magnetic resonance imaging (sMRI) and 15 functional magnetic resonance imaging (fMRI) studies were included. The results from meta-analysis of the sMRI studies indicated that NSS were associated with atrophy of the precentral gyrus, the cerebellum, the inferior frontal gyrus, and the thalamus. The results from meta-analysis of the fMRI studies demonstrated that the NSS-related task was significantly associated with altered brain activation in the inferior frontal gyrus, bilateral putamen, the cerebellum, and the superior temporal gyrus. Our findings from both sMRI and fMRI meta-analyses further support the conceptualization of NSS as a manifestation of the "cerebello-thalamo-prefrontal" brain network model of schizophrenia and related psychotic disorders.

MetaUniDec: High-Throughput Deconvolution of Native Mass Spectra

NASA Astrophysics Data System (ADS)

Reid, Deseree J.; Diesing, Jessica M.; Miller, Matthew A.; Perry, Scott M.; Wales, Jessica A.; Montfort, William R.; Marty, Michael T.

2018-04-01

The expansion of native mass spectrometry (MS) methods for both academic and industrial applications has created a substantial need for analysis of large native MS datasets. Existing software tools are poorly suited for high-throughput deconvolution of native electrospray mass spectra from intact proteins and protein complexes. The UniDec Bayesian deconvolution algorithm is uniquely well suited for high-throughput analysis due to its speed and robustness but was previously tailored towards individual spectra. Here, we optimized UniDec for deconvolution, analysis, and visualization of large data sets. This new module, MetaUniDec, centers around a hierarchical data format 5 (HDF5) format for storing datasets that significantly improves speed, portability, and file size. It also includes code optimizations to improve speed and a new graphical user interface for visualization, interaction, and analysis of data. To demonstrate the utility of MetaUniDec, we applied the software to analyze automated collision voltage ramps with a small bacterial heme protein and large lipoprotein nanodiscs. Upon increasing collisional activation, bacterial heme-nitric oxide/oxygen binding (H-NOX) protein shows a discrete loss of bound heme, and nanodiscs show a continuous loss of lipids and charge. By using MetaUniDec to track changes in peak area or mass as a function of collision voltage, we explore the energetic profile of collisional activation in an ultra-high mass range Orbitrap mass spectrometer. [Figure not available: see fulltext.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

PubMed Central

Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-01-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit. PMID:29765629

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Testing moderation in network meta-analysis with individual participant data

PubMed Central

Dagne, Getachew A.; Brown, C. Hendricks; Howe, George; Kellam, Sheppard G.; Liu, Lei

2016-01-01

Summary Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data (IPD) may increase the sensitivity of NMA for detecting moderator effects, as compared to aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new network meta-analysis diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within- and between-trial heterogeneity, and can include participant-level covariates. Within this framework we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to IPD as compared to study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups. PMID:26841367

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

PubMed

Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

2011-02-01

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

The effect of iconicity of visual displays on statistical reasoning: evidence in favor of the null hypothesis.

PubMed

Sirota, Miroslav; Kostovičová, Lenka; Juanchich, Marie

2014-08-01

Knowing which properties of visual displays facilitate statistical reasoning bears practical and theoretical implications. Therefore, we studied the effect of one property of visual diplays - iconicity (i.e., the resemblance of a visual sign to its referent) - on Bayesian reasoning. Two main accounts of statistical reasoning predict different effect of iconicity on Bayesian reasoning. The ecological-rationality account predicts a positive iconicity effect, because more highly iconic signs resemble more individuated objects, which tap better into an evolutionary-designed frequency-coding mechanism that, in turn, facilitates Bayesian reasoning. The nested-sets account predicts a null iconicity effect, because iconicity does not affect the salience of a nested-sets structure-the factor facilitating Bayesian reasoning processed by a general reasoning mechanism. In two well-powered experiments (N = 577), we found no support for a positive iconicity effect across different iconicity levels that were manipulated in different visual displays (meta-analytical overall effect: log OR = -0.13, 95% CI [-0.53, 0.28]). A Bayes factor analysis provided strong evidence in favor of the null hypothesis-the null iconicity effect. Thus, these findings corroborate the nested-sets rather than the ecological-rationality account of statistical reasoning.

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Artificial and Bayesian Neural Networks

PubMed

Korhani Kangi, Azam; Bahrampour, Abbas

2018-02-26

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for predicting survival of gastric cancer patients in Iran. Creative Commons Attribution License

A Bayesian framework to estimate diversification rates and their variation through time and space

PubMed Central

2011-01-01

Background Patterns of species diversity are the result of speciation and extinction processes, and molecular phylogenetic data can provide valuable information to derive their variability through time and across clades. Bayesian Markov chain Monte Carlo methods offer a promising framework to incorporate phylogenetic uncertainty when estimating rates of diversification. Results We introduce a new approach to estimate diversification rates in a Bayesian framework over a distribution of trees under various constant and variable rate birth-death and pure-birth models, and test it on simulated phylogenies. Furthermore, speciation and extinction rates and their posterior credibility intervals can be estimated while accounting for non-random taxon sampling. The framework is particularly suitable for hypothesis testing using Bayes factors, as we demonstrate analyzing dated phylogenies of Chondrostoma (Cyprinidae) and Lupinus (Fabaceae). In addition, we develop a model that extends the rate estimation to a meta-analysis framework in which different data sets are combined in a single analysis to detect general temporal and spatial trends in diversification. Conclusions Our approach provides a flexible framework for the estimation of diversification parameters and hypothesis testing while simultaneously accounting for uncertainties in the divergence times and incomplete taxon sampling. PMID:22013891

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

PubMed Central

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-01

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

PubMed

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-08

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Antiplatelet Agents for the Secondary Prevention of Ischemic Stroke or Transient Ischemic Attack: A Network Meta-Analysis.

PubMed

Wang, Wen; Zhang, Lu; Liu, Weiming; Zhu, Qin; Lan, Qing; Zhao, Jizong

2016-05-01

Stroke can cause high morbidity and mortality, and ischemic stroke (IS) and transient ischemic attack (TIA) patients have a high stroke recurrence rate. Antiplatelet agents are the standard therapy for these patients, but it is often difficult for clinicians to select the best therapy from among the multiple treatment options. We therefore performed a network meta-analysis to estimate the efficacy of antiplatelet agents for secondary prevention of recurrent stroke. We systematically searched 3 databases (PubMed, Embase, and Cochrane) for relevant studies published through August 2015. The primary end points of this meta-analysis were overall stroke, hemorrhagic stroke, and fatal stroke. A total of 30 trials were included in our network meta-analysis and abstracted data. Among the therapies evaluated in the included trials, the estimates for overall stroke and hemorrhagic stroke for cilostazol (Cilo) were significantly better than those for aspirin (odds ratio [OR] = .64, 95% credibility interval [CrI], .45-.91; OR = .23, 95% CrI, .08-.58). The estimate for fatal stroke was highest for Cilo plus aspirin combination therapy, followed by Cilo therapy. The results of our meta-analysis indicate that Cilo significantly improves overall stroke and hemorrhagic stroke in IS or TIA patients and reduces fatal stroke, but with low statistical significance. Our results also show that Cilo was significantly more efficient than other therapies in Asian patients; therefore, future trials should focus on Cilo treatment for secondary prevention of recurrent stroke in non-Asian patients. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Development of a Bayesian Belief Network Runway Incursion and Excursion Model

NASA Technical Reports Server (NTRS)

Green, Lawrence L.

2014-01-01

In a previous work, a statistical analysis of runway incursion (RI) event data was conducted to ascertain the relevance of this data to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to several of the AvSP top ten TC and identified numerous primary causes and contributing factors of RI events. The statistical analysis served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events, also previously reported. Through literature searches and data analysis, this RI event network has now been extended to also model runway excursion (RE) events. These RI and RE event networks have been further modified and vetted by a Subject Matter Expert (SME) panel. The combined system-level BBN model will allow NASA to generically model the causes of RI and RE events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of runway safety incidents/accidents, and to improve runway safety in general. The development and structure of the BBN for both RI and RE events are documented in this paper.

Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site

NASA Astrophysics Data System (ADS)

Maiti, Saumen; Tiwari, Ram Krishna

2010-10-01

A new probabilistic approach based on the concept of Bayesian neural network (BNN) learning theory is proposed for decoding litho-facies boundaries from well-log data. We show that how a multi-layer-perceptron neural network model can be employed in Bayesian framework to classify changes in litho-log successions. The method is then applied to the German Continental Deep Drilling Program (KTB) well-log data for classification and uncertainty estimation in the litho-facies boundaries. In this framework, a posteriori distribution of network parameter is estimated via the principle of Bayesian probabilistic theory, and an objective function is minimized following the scaled conjugate gradient optimization scheme. For the model development, we inflict a suitable criterion, which provides probabilistic information by emulating different combinations of synthetic data. Uncertainty in the relationship between the data and the model space is appropriately taken care by assuming a Gaussian a priori distribution of networks parameters (e.g., synaptic weights and biases). Prior to applying the new method to the real KTB data, we tested the proposed method on synthetic examples to examine the sensitivity of neural network hyperparameters in prediction. Within this framework, we examine stability and efficiency of this new probabilistic approach using different kinds of synthetic data assorted with different level of correlated noise. Our data analysis suggests that the designed network topology based on the Bayesian paradigm is steady up to nearly 40% correlated noise; however, adding more noise (˜50% or more) degrades the results. We perform uncertainty analyses on training, validation, and test data sets with and devoid of intrinsic noise by making the Gaussian approximation of the a posteriori distribution about the peak model. We present a standard deviation error-map at the network output corresponding to the three types of the litho-facies present over the entire litho-section of the KTB. The comparisons of maximum a posteriori geological sections constructed here, based on the maximum a posteriori probability distribution, with the available geological information and the existing geophysical findings suggest that the BNN results reveal some additional finer details in the KTB borehole data at certain depths, which appears to be of some geological significance. We also demonstrate that the proposed BNN approach is superior to the conventional artificial neural network in terms of both avoiding "over-fitting" and aiding uncertainty estimation, which are vital for meaningful interpretation of geophysical records. Our analyses demonstrate that the BNN-based approach renders a robust means for the classification of complex changes in the litho-facies successions and thus could provide a useful guide for understanding the crustal inhomogeneity and the structural discontinuity in many other tectonically complex regions.

MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification.

PubMed

Burgess, K E V; Borutzki, Y; Rankin, N; Daly, R; Jourdan, F

2017-12-15

Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them. We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships. We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset. Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Space Shuttle RTOS Bayesian Network

NASA Technical Reports Server (NTRS)

Morris, A. Terry; Beling, Peter A.

2001-01-01

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores. Using a prioritization of measures from the decision-maker, trade-offs between the scores are used to rank order the available set of RTOS candidates.

GAD vaccine reduces insulin loss in recently diagnosed type 1 diabetes: findings from a Bayesian meta-analysis.

PubMed

Beam, Craig A; MacCallum, Colleen; Herold, Kevan C; Wherrett, Diane K; Palmer, Jerry; Ludvigsson, Johnny

2017-01-01

GAD is a major target of the autoimmune response that occurs in type 1 diabetes mellitus. Randomised controlled clinical trials of a GAD + alum vaccine in human participants have so far given conflicting results. In this study, we sought to see whether a clearer answer to the question of whether GAD65 has an effect on C-peptide could be reached by combining individual-level data from the randomised controlled trials using Bayesian meta-analysis to estimate the probability of a positive biological effect (a reduction in C-peptide loss compared with placebo approximately 1 year after the GAD vaccine). We estimate that there is a 98% probability that 20 μg GAD with alum administered twice yields a positive biological effect. The effect is probably a 15-20% reduction in the loss of C-peptide at approximately 1 year after treatment. This translates to an annual expected loss of between -0.250 and -0.235 pmol/ml in treated patients compared with an expected 2 h AUC loss of -0.294 pmol/ml at 1 year for untreated newly diagnosed patients. The biological effect of this vaccination should be developed further in order to reach clinically desirable reductions in insulin loss in patients recently diagnosed with type 1 diabetes.

Development of an Integrated Team Training Design and Assessment Architecture to Support Adaptability in Healthcare Teams

DTIC Science & Technology

2016-10-01

and implementation of embedded, adaptive feedback and performance assessment. The investigators also initiated work designing a Bayesian Belief ...training; Teamwork; Adaptive performance; Leadership; Simulation; Modeling; Bayesian belief networks (BBN) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Trauma teams Team training Teamwork Adaptability Adaptive performance Leadership Simulation Modeling Bayesian belief networks (BBN) 6

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

PubMed Central

Shah, Abhik; Woolf, Peter

2009-01-01

Summary In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541

A Bayesian approach to landscape ecological risk assessment applied to the upper Grande Ronde watershed, Oregon

Treesearch

Kimberley K. Ayre; Wayne G. Landis

2012-01-01

We present a Bayesian network model based on the ecological risk assessment framework to evaluate potential impacts to habitats and resources resulting from wildfire, grazing, forest management activities, and insect outbreaks in a forested landscape in northeastern Oregon. The Bayesian network structure consisted of three tiers of nodes: landscape disturbances,...

A Meta-Analysis of the Regulatory Focus Nomological Network: Work-Related Antecedents and Consequences

ERIC Educational Resources Information Center

Gorman, C. Allen; Meriac, John P.; Overstreet, Benjamin L.; Apodaca, Steven; McIntyre, Ashley L.; Park, Paul; Godbey, Jennifer N.

2012-01-01

Regulatory focus theory (Higgins, 1997, 1998) has received a great deal of recent attention in the organizational behavior literature. Despite the amount of new evidence surrounding regulatory focus and its relationships with other variables, a quantitative summary of this literature is lacking. The authors used meta-analysis to summarize…

Gender-specific estimates of COPD prevalence: a systematic review and meta-analysis.

PubMed

Ntritsos, Georgios; Franek, Jacob; Belbasis, Lazaros; Christou, Maria A; Markozannes, Georgios; Altman, Pablo; Fogel, Robert; Sayre, Tobias; Ntzani, Evangelia E; Evangelou, Evangelos

2018-01-01

COPD has been perceived as being a disease of older men. However, >7 million women are estimated to live with COPD in the USA alone. Despite a growing body of literature suggesting an increasing burden of COPD in women, the evidence is limited. To assess and synthesize the available evidence among population-based epidemiologic studies and calculate the global prevalence of COPD in men and women. A systematic review and meta-analysis reporting gender-specific prevalence of COPD was undertaken. Gender-specific prevalence estimates were abstracted from relevant studies. Associated patient characteristics as well as custom variables pertaining to the diagnostic method and other important epidemiologic covariates were also collected. A Bayesian random-effects meta-analysis was performed investigating gender-specific prevalence of COPD stratified by age, geography, calendar time, study setting, diagnostic method, and disease severity. Among 194 eligible studies, summary prevalence was 9.23% (95% credible interval [CrI]: 8.16%-10.36%) in men and 6.16% (95% CrI: 5.41%-6.95%) in women. Gender prevalences varied widely by the World Health Organization Global Burden of Disease subregions, with the highest female prevalence found in North America (8.07% vs 7.30%) and in participants in urban settings (13.03% vs 8.34%). Meta-regression indicated that age ≥40 and bronchodilator testing contributed most significantly to heterogeneity of prevalence estimates across studies. We conducted the largest ever systematic review and meta-analysis of global prevalence of COPD and the first large gender-specific review. These results will increase awareness of COPD as a critical woman's health issue.

Identification of Common Neural Circuit Disruptions in Cognitive Control Across Psychiatric Disorders.

PubMed

McTeague, Lisa M; Huemer, Julia; Carreon, David M; Jiang, Ying; Eickhoff, Simon B; Etkin, Amit

2017-07-01

Cognitive deficits are a common feature of psychiatric disorders. The authors investigated the nature of disruptions in neural circuitry underlying cognitive control capacities across psychiatric disorders through a transdiagnostic neuroimaging meta-analysis. A PubMed search was conducted for whole-brain functional neuroimaging articles published through June 2015 that compared activation in patients with axis I disorders and matched healthy control participants during cognitive control tasks. Tasks that probed performance or conflict monitoring, response inhibition or selection, set shifting, verbal fluency, and recognition or working memory were included. Activation likelihood estimation meta-analyses were conducted on peak voxel coordinates. The 283 experiments submitted to meta-analysis included 5,728 control participants and 5,493 patients with various disorders (schizophrenia, bipolar or unipolar depression, anxiety disorders, and substance use disorders). Transdiagnostically abnormal activation was evident in the left prefrontal cortex as well as the anterior insula, the right ventrolateral prefrontal cortex, the right intraparietal sulcus, and the midcingulate/presupplementary motor area. Disruption was also observed in a more anterior cluster in the dorsal cingulate cortex, which overlapped with a network of structural perturbation that the authors previously reported in a transdiagnostic meta-analysis of gray matter volume. These findings demonstrate a common pattern of disruption across major psychiatric disorders that parallels the "multiple-demand network" observed in intact cognition. This network interfaces with the anterior-cingulo-insular or "salience network" demonstrated to be transdiagnostically vulnerable to gray matter reduction. Thus, networks intrinsic to adaptive, flexible cognition are vulnerable to broad-spectrum psychopathology. Dysfunction in these networks may reflect an intermediate transdiagnostic phenotype, which could be leveraged to advance therapeutics.

Constraints on the nuclear equation of state from nuclear masses and radii in a Thomas-Fermi meta-modeling approach

NASA Astrophysics Data System (ADS)

Chatterjee, D.; Gulminelli, F.; Raduta, Ad. R.; Margueron, J.

2017-12-01

The question of correlations among empirical equation of state (EoS) parameters constrained by nuclear observables is addressed in a Thomas-Fermi meta-modeling approach. A recently proposed meta-modeling for the nuclear EoS in nuclear matter is augmented with a single finite size term to produce a minimal unified EoS functional able to describe the smooth part of the nuclear ground state properties. This meta-model can reproduce the predictions of a large variety of models, and interpolate continuously between them. An analytical approximation to the full Thomas-Fermi integrals is further proposed giving a fully analytical meta-model for nuclear masses. The parameter space is sampled and filtered through the constraint of nuclear mass reproduction with Bayesian statistical tools. We show that this simple analytical meta-modeling has a predictive power on masses, radii, and skins comparable to full Hartree-Fock or extended Thomas-Fermi calculations with realistic energy functionals. The covariance analysis on the posterior distribution shows that no physical correlation is present between the different EoS parameters. Concerning nuclear observables, a strong correlation between the slope of the symmetry energy and the neutron skin is observed, in agreement with previous studies.

Bayesian estimation inherent in a Mexican-hat-type neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

Risk analysis of emergent water pollution accidents based on a Bayesian Network.

PubMed

Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie

2016-01-01

To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

PubMed Central

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

PubMed

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Therapeutic effects of different drugs on obstructive sleep apnea/hypopnea syndrome in children.

PubMed

Zhang, Jing; Chen, Jie; Yin, Yong; Zhang, Lei; Zhang, Hao

2017-12-01

This study aimed to compare the therapeutic effects of different drugs on obstructive sleep apnea/hypopnea syndrome (OSAHS) in children by using a network meta-analysis approach. PubMed, Embase and Cochrane Library were searched from the inception of each database to November 2015. Randomized controlled trials (RCTs) concerning the comparisons in the therapeutic effects of eight placebo-controlled drugs on OSAHS in children were included in this study. Network meta-analysis combined direct evidence and indirect evidence to evaluate the weighted mean difference (WMD) and surface under the cumulative ranking curves (SUCRA) of therapeutic effects of eight drugs on OSAHS in children. A total of seven RCTs were finally incorporated into our network meta-analysis. Pairwise meta-analysis results revealed that therapeutic effect of placebo was significantly poorer than that of intranasal mometasone furoate, montelukast, budesonide and fluticasone concerning apnea hypopnea index (AHI) value [WMD=1.40, 95% confidence interval (CI)=1.17-1.63; WMD=2.80, 95% CI=1.01-4.59; WMD=3.50, 95% CI=3.34-3.66; WMD=7.20, 95% CI=5.26-9.14, respectively], and fluticasone is better than placebo concerning sleep efficiency (WMD=3.50, 95% CI=2.42-4.58); regarding visual analogue scale, the therapeutic effect of placebo was poorer compared with sucralfate and clindamycin (WMD=1.94, 95% CI=1.13-2.75; WMD=1.06, 95% CI=0.22-1.90), and sucralfate is better than clindamycin (WMD=-0.88, 95% CI=-1.65 to -0.11). However, network meta-analysis results showed no obvious difference in the therapeutic effects of different drugs on OSAHS regarding AHI and sleep efficiency. Furthermore, the best SUCRA value was very high for fluticasone concerning AHI (86.6%) and budesonide concerning sleep efficiency (94.0%) for OSAHS treatment. Fluticasone and budesonide have relatively good effects in the treatment of OSAHS in children, thus providing an important guiding significance for the treatment of OSAHS in children.

A hierarchical framework for investigating epiphyte assemblages: networks, meta-communities, and scale.

PubMed

Burns, K C; Zotz, G

2010-02-01

Epiphytes are an important component of many forested ecosystems, yet our understanding of epiphyte communities lags far behind that of terrestrial-based plant communities. This discrepancy is exacerbated by the lack of a theoretical context to assess patterns in epiphyte community structure. We attempt to fill this gap by developing an analytical framework to investigate epiphyte assemblages, which we then apply to a data set on epiphyte distributions in a Panamanian rain forest. On a coarse scale, interactions between epiphyte species and host tree species can be viewed as bipartite networks, similar to pollination and seed dispersal networks. On a finer scale, epiphyte communities on individual host trees can be viewed as meta-communities, or suites of local epiphyte communities connected by dispersal. Similar analytical tools are typically employed to investigate species interaction networks and meta-communities, thus providing a unified analytical framework to investigate coarse-scale (network) and fine-scale (meta-community) patterns in epiphyte distributions. Coarse-scale analysis of the Panamanian data set showed that most epiphyte species interacted with fewer host species than expected by chance. Fine-scale analyses showed that epiphyte species richness on individual trees was lower than null model expectations. Therefore, epiphyte distributions were clumped at both scales, perhaps as a result of dispersal limitations. Scale-dependent patterns in epiphyte species composition were observed. Epiphyte-host networks showed evidence of negative co-occurrence patterns, which could arise from adaptations among epiphyte species to avoid competition for host species, while most epiphyte meta-communities were distributed at random. Application of our "meta-network" analytical framework in other locales may help to identify general patterns in the structure of epiphyte assemblages and their variation in space and time.

An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

PubMed

Hippert, Henrique S; Taylor, James W

2010-04-01

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

Effectiveness and safety of treatments used for the management of patent ductus arteriosus (PDA) in preterm infants: a protocol for a systematic review and network meta-analysis.

PubMed

Mitra, Souvik; Florez, Ivan D; Tamayo, Maria E; Aune, Dagfinn; Mbuagbaw, Lawrence; Veroniki, Areti-Angeliki; Thabane, Lehana

2016-07-25

Management of patent ductus arteriosus (PDA) in preterm infants is one of the most controversial topics in neonatal medicine. The availability of different pharmacotherapeutic options often poses a practical challenge to the practising neonatologist as to which one to choose as a therapeutic option. Our objectives are to determine the relative merits of the available pharmacotherapeutic options for the management of PDA. We will conduct a systematic review of all randomised controlled trials evaluating the use of intravenous or oral: indomethacin, ibuprofen and acetaminophen for the treatment of PDA in preterm infants. The primary outcome is failure of closure of the PDA. Secondary outcomes are neonatal mortality, need for surgical closure, duration of ventilator support, chronic lung disease, intraventricular haemorrhage, periventricular leukomalacia, necrotising enterocolitis, gastrointestinal bleeding, time to full enteral feeds and oliguria. We will search Medline, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) as well as grey literature resources. Two reviewers will independently screen titles and abstracts, review full texts, extract information, and assess the risk of bias (ROB) and the confidence in the estimate (with Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach). Subgroup analysis according to gestational age, birth weight, different doses of interventions, time of administration of the first dose of the intervention, and echocardiographic definition of haemodynamically significant PDA and ROB are planned. We will perform a Bayesian network meta-analysis to combine the pooled direct and indirect treatment effect estimates for each outcome, if adequate data are available. The results will help to reduce the uncertainty about the safety and effectiveness of the interventions, will identify knowledge gaps or will encourage further research for other therapeutic options. Therefore, its results will be disseminated through peer-reviewed publications and conference presentations. On the basis of the nature of its design, no ethics approval is necessary for this study. CRD42015015797. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Characterization of the SOS meta-regulon in the human gut microbiome.

PubMed

Cornish, Joseph P; Sanchez-Alberola, Neus; O'Neill, Patrick K; O'Keefe, Ronald; Gheba, Jameel; Erill, Ivan

2014-05-01

Data from metagenomics projects remain largely untapped for the analysis of transcriptional regulatory networks. Here, we provide proof-of-concept that metagenomic data can be effectively leveraged to analyze regulatory networks by characterizing the SOS meta-regulon in the human gut microbiome. We combine well-established in silico and in vitro techniques to mine the human gut microbiome data and determine the relative composition of the SOS network in a natural setting. Our analysis highlights the importance of translesion synthesis as a primary function of the SOS response. We predict the association of this network with three novel protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification, and we confirm binding of the SOS response transcriptional repressor to sites in the promoter of a cell wall biogenesis enzyme, a phage integrase and a death-on-curing protein. We discuss the implications of these findings and the potential for this approach for metagenome analysis.

Common quandaries and their practical solutions in Bayesian network modeling

Treesearch

Bruce G. Marcot

2017-01-01

Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation,along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures,...

Calcium and Vitamin D Supplementation for Prevention of Preeclampsia: A Systematic Review and Network Meta-Analysis

PubMed Central

Khaing, Win; Vallibhakara, Sakda Arj-Ong; Tantrakul, Visasiri; Vallibhakara, Orawin; Rattanasiri, Sasivimol; McEvoy, Mark; Attia, John; Thakkinstian, Ammarin

2017-01-01

Vitamin D supplementation effects with or without calcium in pregnancy for reducing risk of preeclampsia and gestational or pregnancy induced hypertension are controversial. Literature was systematically searched in Medline, Scopus and Cochrane databases from inception to July 2017. Only randomized controlled trials (RCTs) in English were selected if they had any pair of interventions (calcium, vitamin D, both, or placebo). Systematic review with two-step network-meta-analysis was used to indirectly estimate supplementary effects. Twenty-seven RCTs with 28,000 women were eligible. A direct meta-analysis suggested that calcium, vitamin D, and calcium plus vitamin D could lower risk of preeclampsia when compared to placebo with the pooled risk ratios (RRs) of 0.54 (0.41, 0.70), 0.47 (0.24, 0.89) and 0.50 (0.32, 0.78), respectively. Results of network meta-analysis were similar with the corresponding RRs of 0.49 (0.35, 0.69), 0.43 (0.17, 1.11), and 0.57 (0.30, 1.10), respectively. None of the controls were significant. Efficacy of supplementation, which was ranked by surface under cumulative ranking probabilities, were: vitamin D (47.4%), calcium (31.6%) and calcium plus vitamin D (19.6%), respectively. Calcium supplementation may be used for prevention for preeclampsia. Vitamin D might also worked well but further large scale RCTs are warranted to confirm our findings. PMID:29057843

Calcium and Vitamin D Supplementation for Prevention of Preeclampsia: A Systematic Review and Network Meta-Analysis.

PubMed

Khaing, Win; Vallibhakara, Sakda Arj-Ong; Tantrakul, Visasiri; Vallibhakara, Orawin; Rattanasiri, Sasivimol; McEvoy, Mark; Attia, John; Thakkinstian, Ammarin

2017-10-18

Vitamin D supplementation effects with or without calcium in pregnancy for reducing risk of preeclampsia and gestational or pregnancy induced hypertension are controversial. Literature was systematically searched in Medline, Scopus and Cochrane databases from inception to July 2017. Only randomized controlled trials (RCTs) in English were selected if they had any pair of interventions (calcium, vitamin D, both, or placebo). Systematic review with two-step network-meta-analysis was used to indirectly estimate supplementary effects. Twenty-seven RCTs with 28,000 women were eligible. A direct meta-analysis suggested that calcium, vitamin D, and calcium plus vitamin D could lower risk of preeclampsia when compared to placebo with the pooled risk ratios (RRs) of 0.54 (0.41, 0.70), 0.47 (0.24, 0.89) and 0.50 (0.32, 0.78), respectively. Results of network meta-analysis were similar with the corresponding RRs of 0.49 (0.35, 0.69), 0.43 (0.17, 1.11), and 0.57 (0.30, 1.10), respectively. None of the controls were significant. Efficacy of supplementation, which was ranked by surface under cumulative ranking probabilities, were: vitamin D (47.4%), calcium (31.6%) and calcium plus vitamin D (19.6%), respectively. Calcium supplementation may be used for prevention for preeclampsia. Vitamin D might also worked well but further large scale RCTs are warranted to confirm our findings.

Exercise and BMI z-score in overweight and obese children and adolescents: protocol for a systematic review and network meta-analysis of randomised trials.

PubMed

Kelley, George A; Kelley, Kristi S

2016-04-15

While overweight and obesity in children and adolescents is a major global health problem, the effects of exercise on overweight and obesity in children and adolescents are not well established despite numerous studies on this topic. The purpose of this study is to use the network meta-analytic approach to determine the effects of exercise (aerobic, strength training or both) on body mass index (BMI) z-score in overweight and obese children and adolescents. Randomised exercise intervention trials >4 weeks, published in any language between 1 January 1990 and 31 September 2015, and which include direct and/or indirect evidence, will be included. Studies will be retrieved by searching 6 electronic databases, cross-referencing and expert review. Dual abstraction of data will occur. The primary outcome will be changes in BMI z-score while the secondary outcome will be changes in body weight in kilograms (kg). Risk of bias will be assessed using the Cochrane risk of bias assessment instrument while confidence in the cumulative evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) instrument for network meta-analysis. Network meta-analysis will be performed using multivariate random-effects meta-regression models. The surface under the cumulative ranking curve will be used to provide a hierarchy of exercise treatments (aerobic, strength training or both). The results of this study will be presented at a professional conference and published in a peer-reviewed journal. CRD42015026377. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Integrative Analysis of Transcription Factor Combinatorial Interactions Using a Bayesian Tensor Factorization Approach

PubMed Central

Ye, Yusen; Gao, Lin; Zhang, Shihua

2017-01-01

Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions. PMID:29033978

Integrative Analysis of Transcription Factor Combinatorial Interactions Using a Bayesian Tensor Factorization Approach.

PubMed

Ye, Yusen; Gao, Lin; Zhang, Shihua

2017-01-01

Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions.

Multi-ethnic fine-mapping of 14 central adiposity loci.

PubMed

Liu, Ching-Ti; Buchkovich, Martin L; Winkler, Thomas W; Heid, Iris M; Borecki, Ingrid B; Fox, Caroline S; Mohlke, Karen L; North, Kari E; Adrienne Cupples, L

2014-09-01

The Genetic Investigation of Anthropometric Traits (GIANT) consortium identified 14 loci in European Ancestry (EA) individuals associated with waist-to-hip ratio (WHR) adjusted for body mass index. These loci are wide and narrowing the signals remains necessary. Twelve of 14 loci identified in GIANT EA samples retained strong associations with WHR in our joint EA/individuals of African Ancestry (AA) analysis (log-Bayes factor >6.1). Trans-ethnic analyses at five loci (TBX15-WARS2, LYPLAL1, ADAMTS9, LY86 and ITPR2-SSPN) substantially narrowed the signals to smaller sets of variants, some of which are in regions that have evidence of regulatory activity. By leveraging varying linkage disequilibrium structures across different populations, single-nucleotide polymorphisms (SNPs) with strong signals and narrower credible sets from trans-ethnic meta-analysis of central obesity provide more precise localizations of potential functional variants and suggest a possible regulatory role. Meta-analysis results for WHR were obtained from 77 167 EA participants from GIANT and 23 564 AA participants from the African Ancestry Anthropometry Genetics Consortium. For fine mapping we interrogated SNPs within ± 250 kb flanking regions of 14 previously reported index SNPs from loci discovered in EA populations by performing trans-ethnic meta-analysis of results from the EA and AA meta-analyses. We applied a Bayesian approach that leverages allelic heterogeneity across populations to combine meta-analysis results and aids in fine-mapping shared variants at these locations. We annotated variants using information from the ENCODE Consortium and Roadmap Epigenomics Project to prioritize variants for possible functionality. Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers.

PubMed

Irigoyen, Antonio; Jimenez-Luna, Cristina; Benavides, Manuel; Caba, Octavio; Gallego, Javier; Ortuño, Francisco Manuel; Guillen-Ponce, Carmen; Rojas, Ignacio; Aranda, Enrique; Torres, Carolina; Prados, Jose

2018-01-01

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Bayesian networks in overlay recipe optimization

NASA Astrophysics Data System (ADS)

Binns, Lewis A.; Reynolds, Greg; Rigden, Timothy C.; Watkins, Stephen; Soroka, Andrew

2005-05-01

Currently, overlay measurements are characterized by "recipe", which defines both physical parameters such as focus, illumination et cetera, and also the software parameters such as algorithm to be used and regions of interest. Setting up these recipes requires both engineering time and wafer availability on an overlay tool, so reducing these requirements will result in higher tool productivity. One of the significant challenges to automating this process is that the parameters are highly and complexly correlated. At the same time, a high level of traceability and transparency is required in the recipe creation process, so a technique that maintains its decisions in terms of well defined physical parameters is desirable. Running time should be short, given the system (automatic recipe creation) is being implemented to reduce overheads. Finally, a failure of the system to determine acceptable parameters should be obvious, so a certainty metric is also desirable. The complex, nonlinear interactions make solution by an expert system difficult at best, especially in the verification of the resulting decision network. The transparency requirements tend to preclude classical neural networks and similar techniques. Genetic algorithms and other "global minimization" techniques require too much computational power (given system footprint and cost requirements). A Bayesian network, however, provides a solution to these requirements. Such a network, with appropriate priors, can be used during recipe creation / optimization not just to select a good set of parameters, but also to guide the direction of search, by evaluating the network state while only incomplete information is available. As a Bayesian network maintains an estimate of the probability distribution of nodal values, a maximum-entropy approach can be utilized to obtain a working recipe in a minimum or near-minimum number of steps. In this paper we discuss the potential use of a Bayesian network in such a capacity, reducing the amount of engineering intervention. We discuss the benefits of this approach, especially improved repeatability and traceability of the learning process, and quantification of uncertainty in decisions made. We also consider the problems associated with this approach, especially in detailed construction of network topology, validation of the Bayesian network and the recipes it generates, and issues arising from the integration of a Bayesian network with a complex multithreaded application; these primarily relate to maintaining Bayesian network and system architecture integrity.

Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Webb-Robertson, Bobbie-Jo M.; Corley, Courtney D.; McCue, Lee Ann

The field of bioforensics is focused on the analysis of evidence from a biocrime. Existing laboratory analyses can identify the specific strain of an organism in the evidence, as well signatures of the specific culture batch of organisms, such as low-frequency contaminants or indicators of growth and processing methods. To link these disparate types of physical data to potential suspects, investigators may need to identify institutions or individuals whose access to strains and culturing practices match those identified from the evidence. In this work we present a Bayesian statistical network to fuse different types of analytical measurements that predict themore » production environment of a Yersinia pestis sample under investigation with automated test processing of scientific publications to identify institutions with a history of growing Y. pestis under similar conditions. Furthermore, the textual and experimental signatures were evaluated recursively to determine the overall sensitivity of the network across all levels of false positives. We illustrate that institutions associated with several specific culturing practices can be accurately selected based on the experimental signature from only a few analytical measurements. These findings demonstrate that similar Bayesian networks can be generated generically for many organisms of interest and their deployment is not prohibitive due to either computational or experimental factors.« less

Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Yoon, Jung-Ho; Yoon, Sang Jun; Lim, Johan; Kim, You-Sun; Kwon, Sung Won; Park, Jeong Hill

2016-01-01

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates. PMID:26870956

Default network activation during episodic and semantic memory retrieval: A selective meta-analytic comparison.

PubMed

Kim, Hongkeun

2016-01-08

It remains unclear whether and to what extent the default network subregions involved in episodic memory (EM) and semantic memory (SM) processes overlap or are separated from one another. This study addresses this issue through a controlled meta-analysis of functional neuroimaging studies involving healthy participants. Various EM and SM task paradigms differ widely in the extent of default network involvement. Therefore, the issue at hand cannot be properly addressed without some control for this factor. In this regard, this study employs a two-stage analysis: a preliminary meta-analysis to select EM and SM task paradigms that recruit relatively extensive default network regions and a main analysis to compare the selected task paradigms. Based on a within-EM comparison, the default network contributed more to recollection/familiarity effects than to old/new effects, and based on a within-SM comparison, it contributed more to word/pseudoword effects than to semantic/phonological effects. According to a direct comparison of recollection/familiarity and word/pseudoword effects, each involving a range of default network regions, there were more overlaps than separations in default network subregions involved in these two effects. More specifically, overlaps included the bilateral posterior cingulate/retrosplenial cortex, left inferior parietal lobule, and left anteromedial prefrontal regions, whereas separations included only the hippocampal formation and the parahippocampal cortex region, which was unique to recollection/familiarity effects. These results indicate that EM and SM retrieval processes involving strong memory signals recruit extensive and largely overlapping default network regions and differ mainly in distinct contributions of hippocampus and parahippocampal regions to EM retrieval. Copyright © 2015 Elsevier Ltd. All rights reserved.

A random effects meta-analysis model with Box-Cox transformation.

PubMed

Yamaguchi, Yusuke; Maruo, Kazushi; Partlett, Christopher; Riley, Richard D

2017-07-19

In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval. We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable. A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I 2 from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and prediction intervals from the normal random effects model. The random effects meta-analysis with the Box-Cox transformation may be an important tool for examining robustness of traditional meta-analysis results against skewness on the observed treatment effect estimates. Further critical evaluation of the method is needed.

Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

PubMed

Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

2016-08-01

Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

NASA Technical Reports Server (NTRS)

Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

2009-01-01

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Thirty Years of Evidence on the Efficacy of Drug Treatments for Chronic Heart Failure With Reduced Ejection Fraction

PubMed Central

Earley, Amy; Voors, Adriaan A.; Senni, Michele; McMurray, John J.V.; Deschaseaux, Celine; Cope, Shannon

2017-01-01

Background— Treatments that reduce mortality and morbidity in patients with heart failure with reduced ejection fraction, including angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), β-blockers (BB), mineralocorticoid receptor antagonists (MRA), and angiotensin receptor–neprilysin inhibitors (ARNI), have not been studied in a head-to-head fashion. This network meta-analysis aimed to compare the efficacy of these drugs and their combinations regarding all-cause mortality in patients with heart failure with reduced ejection fraction. Methods and Results— A systematic literature review identified 57 randomized controlled trials published between 1987 and 2015, which were compared in terms of study and patient characteristics, baseline risk, outcome definitions, and the observed treatment effects. Despite differences identified in terms of study duration, New York Heart Association class, ejection fraction, and use of background digoxin, a network meta-analysis was considered feasible and all trials were analyzed simultaneously. The random-effects network meta-analysis suggested that the combination of ACEI+BB+MRA was associated with a 56% reduction in mortality versus placebo (hazard ratio 0.44, 95% credible interval 0.26–0.66); ARNI+BB+MRA was associated with the greatest reduction in all-cause mortality versus placebo (hazard ratio 0.37, 95% credible interval 0.19–0.65). A sensitivity analysis that did not account for background therapy suggested that ARNI monotherapy is more efficacious than ACEI or ARB monotherapy. Conclusions— The network meta-analysis showed that treatment with ACEI, ARB, BB, MRA, and ARNI and their combinations were better than the treatment with placebo in reducing all-cause mortality, with the exception of ARB monotherapy and ARB plus ACEI. The combination of ARNI+BB+MRA resulted in the greatest mortality reduction. PMID:28087688

Survival After Neoadjuvant and Adjuvant Treatments Compared to Surgery Alone for Resectable Esophageal Carcinoma: A Network Meta-analysis.

PubMed

Pasquali, Sandro; Yim, Guang; Vohra, Ravinder S; Mocellin, Simone; Nyanhongo, Donald; Marriott, Paul; Geh, Ju Ian; Griffiths, Ewen A

2017-03-01

This network meta-analysis compared overall survival after neoadjuvant or adjuvant chemotherapy (CT), radiotherapy (RT), or combinations of both (chemoradiotherapy, CRT) or surgery alone to identify the most effective approach. The optimal treatment for resectable esophageal cancer is unknown. A search for randomized controlled trials reporting on neoadjuvant and adjuvant therapies was conducted. Using a network meta-analysis, treatments were ranked based on their effectiveness for improving survival. In 33 eligible randomized controlled trials, 6072 patients were randomized to receive either surgery alone (N = 2459) or neoadjuvant CT (N = 1332), RT (N = 58), and CRT (N = 1196) followed by surgery or surgery followed by adjuvant CT (N = 542), RT (N = 383), and CRT (N = 102). Twenty-one comparisons were generated. Neoadjuvant CRT followed by surgery compared with surgery alone was the only treatment to significantly improve survival [hazard ratio (HR) = 0.77, 95% confidence interval (CI): 0.68-0.87]. When trials were grouped considering neoadjuvant and adjuvant therapies and surgery alone, neoadjuvant therapies combined with surgery compared with surgery alone showed a survival advantage (HR = 0.83, 95% CI 0.76-0.90), whereas surgery along with adjuvant therapies showed no significant survival advantage (HR = 0.87, 95% CI 0.67-1.14). A subgroup analysis of neoadjuvant therapies showed a superior effectiveness of neoadjuvant CRT and surgery compared with surgery alone (HR = 0.77, 95% CI 0.68-0.87). This network meta-analysis showed neoadjuvant CRT followed by surgery to be the most effective strategy in improving survival of resectable esophageal cancer. Resources should be focused on developing the most effective neoadjuvant CRT regimens for both adenocarcinomas and squamous cell carcinomas of the esophagus.

An ecosystem service approach to support integrated pond management: a case study using Bayesian belief networks--highlighting opportunities and risks.

PubMed

Landuyt, Dries; Lemmens, Pieter; D'hondt, Rob; Broekx, Steven; Liekens, Inge; De Bie, Tom; Declerck, Steven A J; De Meester, Luc; Goethals, Peter L M

2014-12-01

Freshwater ponds deliver a broad range of ecosystem services (ESS). Taking into account this broad range of services to attain cost-effective ESS delivery is an important challenge facing integrated pond management. To assess the strengths and weaknesses of an ESS approach to support decisions in integrated pond management, we applied it on a small case study in Flanders, Belgium. A Bayesian belief network model was developed to assess ESS delivery under three alternative pond management scenarios: intensive fish farming (IFF), extensive fish farming (EFF) and nature conservation management (NCM). A probabilistic cost-benefit analysis was performed that includes both costs associated with pond management practices and benefits associated with ESS delivery. Whether or not a particular ESS is included in the analysis affects the identification of the most preferable management scenario by the model. Assessing the delivery of a more complete set of ecosystem services tends to shift the results away from intensive management to more biodiversity-oriented management scenarios. The proposed methodology illustrates the potential of Bayesian belief networks. BBNs facilitate knowledge integration and their modular nature encourages future model expansion to more encompassing sets of services. Yet, we also illustrate the key weaknesses of such exercises, being that the choice whether or not to include a particular ecosystem service may determine the suggested optimal management practice. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coles, Garill A.; Brothers, Alan J.; Olson, Jarrod

A Bayesian network (BN) model of social factors can support proliferation assessments by estimating the likelihood that a state will pursue a nuclear weapon. Social factors including political, economic, nuclear capability, security, and national identity and psychology factors may play as important a role in whether a State pursues nuclear weapons as more physical factors. This paper will show how using Bayesian reasoning on a generic case of a would-be proliferator State can be used to combine evidence that supports proliferation assessment. Theories and analysis by political scientists can be leveraged in a quantitative and transparent way to indicate proliferationmore » risk. BN models facilitate diagnosis and inference in a probabilistic environment by using a network of nodes and acyclic directed arcs between the nodes whose connections, or absence of, indicate probabilistic relevance, or independence. We propose a BN model that would use information from both traditional safeguards and the strengthened safeguards associated with the Additional Protocol to indicate countries with a high risk of proliferating nuclear weapons. This model could be used in a variety of applications such a prioritization tool and as a component of state safeguards evaluations. This paper will discuss the benefits of BN reasoning, the development of Pacific Northwest National Laboratory’s (PNNL) BN state proliferation model and how it could be employed as an analytical tool.« less

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

NASA Technical Reports Server (NTRS)

Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

2009-01-01

This CD contains files that support the talk (see CASI ID 20100021404). There are 24 models that relate to the ADAPT system and 1 Excel worksheet. In the paper an investigation into the use of Bayesian networks to construct large-scale diagnostic systems is described. The high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems are described in the talk. The data in the CD are the models of the 24 different power systems.

Hierarchy Bayesian model based services awareness of high-speed optical access networks

NASA Astrophysics Data System (ADS)

Bai, Hui-feng

2018-03-01

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit (ONU) and to perform complex services awareness from the whole view of system in optical line terminal (OLT). Simulation results show that the proposed scheme is able to achieve better quality of services (QoS), in terms of packet loss rate and time delay.

Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling

DTIC Science & Technology

2016-05-31

and included explosives such as TATP, HMTD, RDX, RDX, ammonium nitrate , potassium perchlorate, potassium nitrate , sugar, and TNT. The approach...Distribution Unlimited UU UU UU UU 31-05-2016 15-Apr-2014 14-Jan-2015 Final Report: Technical Topic 3.2.2. d Bayesian and Non- parametric Statistics...of Papers published in non peer-reviewed journals: Final Report: Technical Topic 3.2.2. d Bayesian and Non-parametric Statistics: Integration of Neural

Time Spent on Social Network Sites and Psychological Well-Being: A Meta-Analysis.

PubMed

Huang, Chiungjung

2017-06-01

This meta-analysis examines the relationship between time spent on social networking sites and psychological well-being factors, namely self-esteem, life satisfaction, loneliness, and depression. Sixty-one studies consisting of 67 independent samples involving 19,652 participants were identified. The mean correlation between time spent on social networking sites and psychological well-being was low at r = -0.07. The correlations between time spent on social networking sites and positive indicators (self-esteem and life satisfaction) were close to 0, whereas those between time spent on social networking sites and negative indicators (depression and loneliness) were weak. The effects of publication outlet, site on which users spent time, scale of time spent, and participant age and gender were not significant. As most included studies used student samples, future research should be conducted to examine this relationship for adults.

Meta-Analysis of Functional Neuroimaging and Cognitive Control Studies in Schizophrenia: Preliminary Elucidation of a Core Dysfunctional Timing Network

PubMed Central

Alústiza, Irene; Radua, Joaquim; Albajes-Eizagirre, Anton; Domínguez, Manuel; Aubá, Enrique; Ortuño, Felipe

2016-01-01

Timing and other cognitive processes demanding cognitive control become interlinked when there is an increase in the level of difficulty or effort required. Both functions are interrelated and share neuroanatomical bases. A previous meta-analysis of neuroimaging studies found that people with schizophrenia had significantly lower activation, relative to normal controls, of most right hemisphere regions of the time circuit. This finding suggests that a pattern of disconnectivity of this circuit, particularly in the supplementary motor area, is a trait of this mental disease. We hypothesize that a dysfunctional temporal/cognitive control network underlies both cognitive and psychiatric symptoms of schizophrenia and that timing dysfunction is at the root of the cognitive deficits observed. The goal of our study was to look, in schizophrenia patients, for brain structures activated both by execution of cognitive tasks requiring increased effort and by performance of time perception tasks. We conducted a signed differential mapping (SDM) meta-analysis of functional neuroimaging studies in schizophrenia patients assessing the brain response to increasing levels of cognitive difficulty. Then, we performed a multimodal meta-analysis to identify common brain regions in the findings of that SDM meta-analysis and our previously-published activation likelihood estimate (ALE) meta-analysis of neuroimaging of time perception in schizophrenia patients. The current study supports the hypothesis that there exists an overlap between neural structures engaged by both timing tasks and non-temporal cognitive tasks of escalating difficulty in schizophrenia. The implication is that a deficit in timing can be considered as a trait marker of the schizophrenia cognitive profile. PMID:26925013

ABERRANT RESTING-STATE BRAIN ACTIVITY IN POSTTRAUMATIC STRESS DISORDER: A META-ANALYSIS AND SYSTEMATIC REVIEW.

PubMed

Koch, Saskia B J; van Zuiden, Mirjam; Nawijn, Laura; Frijling, Jessie L; Veltman, Dick J; Olff, Miranda

2016-07-01

About 10% of trauma-exposed individuals develop PTSD. Although a growing number of studies have investigated resting-state abnormalities in PTSD, inconsistent results suggest a need for a meta-analysis and a systematic review. We conducted a systematic literature search in four online databases using keywords for PTSD, functional neuroimaging, and resting-state. In total, 23 studies matched our eligibility criteria. For the meta-analysis, we included 14 whole-brain resting-state studies, reporting data on 663 participants (298 PTSD patients and 365 controls). We used the activation likelihood estimation approach to identify concurrence of whole-brain hypo- and hyperactivations in PTSD patients during rest. Seed-based studies could not be included in the quantitative meta-analysis. Therefore, a separate qualitative systematic review was conducted on nine seed-based functional connectivity studies. The meta-analysis showed consistent hyperactivity in the ventral anterior cingulate cortex and the parahippocampus/amygdala, but hypoactivity in the (posterior) insula, cerebellar pyramis and middle frontal gyrus in PTSD patients, compared to healthy controls. Partly concordant with these findings, the systematic review on seed-based functional connectivity studies showed enhanced salience network (SN) connectivity, but decreased default mode network (DMN) connectivity in PTSD. Combined, these altered resting-state connectivity and activity patterns could represent neurobiological correlates of increased salience processing and hypervigilance (SN), at the cost of awareness of internal thoughts and autobiographical memory (DMN) in PTSD. However, several discrepancies between findings of the meta-analysis and systematic review were observed, stressing the need for future studies on resting-state abnormalities in PTSD patients. © 2016 Wiley Periodicals, Inc.

The complex genetics of gait speed: genome-wide meta-analysis approach

PubMed Central

Lunetta, Kathryn L.; Smith, Jennifer A.; Eicher, John D.; Vered, Rotem; Deelen, Joris; Arnold, Alice M.; Buchman, Aron S.; Tanaka, Toshiko; Faul, Jessica D.; Nethander, Maria; Fornage, Myriam; Adams, Hieab H.; Matteini, Amy M.; Callisaya, Michele L.; Smith, Albert V.; Yu, Lei; De Jager, Philip L.; Evans, Denis A.; Gudnason, Vilmundur; Hofman, Albert; Pattie, Alison; Corley, Janie; Launer, Lenore J.; Knopman, Davis S.; Parimi, Neeta; Turner, Stephen T.; Bandinelli, Stefania; Beekman, Marian; Gutman, Danielle; Sharvit, Lital; Mooijaart, Simon P.; Liewald, David C.; Houwing-Duistermaat, Jeanine J.; Ohlsson, Claes; Moed, Matthijs; Verlinden, Vincent J.; Mellström, Dan; van der Geest, Jos N.; Karlsson, Magnus; Hernandez, Dena; McWhirter, Rebekah; Liu, Yongmei; Thomson, Russell; Tranah, Gregory J.; Uitterlinden, Andre G.; Weir, David R.; Zhao, Wei; Starr, John M.; Johnson, Andrew D.; Ikram, M. Arfan; Bennett, David A.; Cummings, Steven R.; Deary, Ian J.; Harris, Tamara B.; Kardia, Sharon L. R.; Mosley, Thomas H.; Srikanth, Velandai K.; Windham, Beverly G.; Newman, Ann B.; Walston, Jeremy D.; Davies, Gail; Evans, Daniel S.; Slagboom, Eline P.; Ferrucci, Luigi; Kiel, Douglas P.; Murabito, Joanne M.; Atzmon, Gil

2017-01-01

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging. PMID:28077804

Association of variants in innate immune genes with asthma and eczema

PubMed Central

Sharma, Sunita; Poon, Audrey; Himes, Blanca E.; Lasky-Su, Jessica; Sordillo, Joanne E.; Belanger, Kathleen; Milton, Donald K.; Bracken, Michael B.; Triche, Elizabeth W.; Leaderer, Brian P.; Gold, Diane R.; Litonjua, Augusto A.

2012-01-01

Background The innate immune pathway is important in the pathogenesis of asthma and eczema. However, only a few variants in these genes have been associated with either disease. We investigate the association between polymorphisms of genes in the innate immune pathway with childhood asthma and eczema. In addition, we compare individual associations with those discovered using a multivariate approach. Methods Using a novel method, case control based association testing (C2BAT), 569 single nucleotide polymorphisms (SNPs) in 44 innate immune genes were tested for association with asthma and eczema in children from the Boston Home Allergens and Asthma Study and the Connecticut Childhood Asthma Study. The screening algorithm was used to identify the top SNPs associated with asthma and eczema. We next investigated the interaction of innate immune variants with asthma and eczema risk using Bayesian networks. Results After correction for multiple comparisons, 7 SNPs in 6 genes (CARD25, TGFB1, LY96, ACAA1, DEFB1, and IFNG) were associated with asthma (adjusted p-value<0.02), while 5 SNPs in 3 different genes (CD80, STAT4, and IRAKI) were significantly associated with eczema (adjusted p-value < 0.02). None of these SNPs were associated with both asthma and eczema. Bayesian network analysis identified 4 SNPs that were predictive of asthma and 10 SNPs that predicted eczema. Of the genes identified using Bayesian networks, only CD80 was associated with eczema in the single-SNP study. Using novel methodology that allows for screening and replication in the same population, we have identified associations of innate immune genes with asthma and eczema. Bayesian network analysis suggests that additional SNPs influence disease susceptibility via SNP interactions. Conclusion Our findings suggest that innate immune genes contribute to the pathogenesis of asthma and eczema, and that these diseases likely have different genetic determinants. PMID:22192168

Prediction of road accidents: A Bayesian hierarchical approach.

PubMed

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available. Copyright © 2012 Elsevier Ltd. All rights reserved.

Explaining Inference on a Population of Independent Agents Using Bayesian Networks

ERIC Educational Resources Information Center

Sutovsky, Peter

2013-01-01

The main goal of this research is to design, implement, and evaluate a novel explanation method, the hierarchical explanation method (HEM), for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak…

Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks

PubMed Central

2017-01-01

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing—with its unique statistical properties—became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca. PMID:28817636

Improved head direction command classification using an optimised Bayesian neural network.

PubMed

Nguyen, Son T; Nguyen, Hung T; Taylor, Philip B; Middleton, James

2006-01-01

Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.

Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.

PubMed

Ramachandran, Parameswaran; Sánchez-Taltavull, Daniel; Perkins, Theodore J

2017-01-01

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.

bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.

PubMed

Franzin, Alberto; Sambo, Francesco; Di Camillo, Barbara

2017-04-15

A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice. The software is implemented in R and C and is available on CRAN under a GPL licence. francesco.sambo@unipd.it. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Mengshoel, Ole

2008-01-01

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Cervical disc arthroplasty for symptomatic cervical disc disease: Traditional and Bayesian meta-analysis with trial sequential analysis.

PubMed

Kan, Shun-Li; Yuan, Zhi-Fang; Ning, Guang-Zhi; Liu, Fei-Fei; Sun, Jing-Cheng; Feng, Shi-Qing

2016-11-01

Cervical disc arthroplasty (CDA) has been designed as a substitute for anterior cervical discectomy and fusion (ACDF) in the treatment of symptomatic cervical disc disease (CDD). Several researchers have compared CDA with ACDF for the treatment of symptomatic CDD; however, the findings of these studies are inconclusive. Using recently published evidence, this meta-analysis was conducted to further verify the benefits and harms of using CDA for treatment of symptomatic CDD. Relevant trials were identified by searching the PubMed, EMBASE, and Cochrane Library databases. Outcomes were reported as odds ratio or standardized mean difference. Both traditional frequentist and Bayesian approaches were used to synthesize evidence within random-effects models. Trial sequential analysis (TSA) was applied to test the robustness of our findings and obtain more conservative estimates. Nineteen trials were included. The findings of this meta-analysis demonstrated better overall, neck disability index (NDI), and neurological success; lower NDI and neck and arm pain scores; higher 36-Item Short Form Health Survey (SF-36) Physical Component Summary (PCS) and Mental Component Summary (MCS) scores; more patient satisfaction; greater range of motion at the operative level; and fewer secondary surgical procedures (all P < 0.05) in the CDA group compared with the ACDF group. CDA was not significantly different from ACDF in the rate of adverse events (P > 0.05). TSA of overall success suggested that the cumulative z-curve crossed both the conventional boundary and the trial sequential monitoring boundary for benefit, indicating sufficient and conclusive evidence had been ascertained. For treating symptomatic CDD, CDA was superior to ACDF in terms of overall, NDI, and neurological success; NDI and neck and arm pain scores; SF-36 PCS and MCS scores; patient satisfaction; ROM at the operative level; and secondary surgical procedures rate. Additionally, there was no significant difference between CDA and ACDF in the rate of adverse events. However, as the CDA procedure is a relatively newer operative technique, long-term results and evaluation are necessary before CDA is routinely used in clinical practice. Copyright © 2016 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models

PubMed Central

Hobbs, Brian P.; Sargent, Daniel J.; Carlin, Bradley P.

2014-01-01

Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously difficult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and current data are often assumed to be fully homogeneous, but this can have drastic consequences for power and Type I error if the historical information is biased. In this paper, we propose empirical and fully Bayesian modifications of the commensurate prior model (Hobbs et al., 2011) extending Pocock (1976), and evaluate their frequentist and Bayesian properties for incorporating patient-level historical data using general and generalized linear mixed regression models. Our proposed commensurate prior models lead to preposterior admissible estimators that facilitate alternative bias-variance trade-offs than those offered by pre-existing methodologies for incorporating historical data from a small number of historical studies. We also provide a sample analysis of a colon cancer trial comparing time-to-disease progression using a Weibull regression model. PMID:24795786

The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach.

PubMed

Guo, Qiang; Xu, Pengpeng; Pei, Xin; Wong, S C; Yao, Danya

2017-02-01

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inferring Alcoholism SNPs and Regulatory Chemical Compounds Based on Ensemble Bayesian Network.

PubMed

Chen, Huan; Sun, Jiatong; Jiang, Hong; Wang, Xianyue; Wu, Lingxiang; Wu, Wei; Wang, Qh

2017-01-01

The disturbance of consciousness is one of the most common symptoms of those have alcoholism and may cause disability and mortality. Previous studies indicated that several single nucleotide polymorphisms (SNP) increase the susceptibility of alcoholism. In this study, we utilized the Ensemble Bayesian Network (EBN) method to identify causal SNPs of alcoholism based on the verified GAW14 data. We built a Bayesian network combining random process and greedy search by using Genetic Analysis Workshop 14 (GAW14) dataset to establish EBN of SNPs. Then we predicted the association between SNPs and alcoholism by determining Bayes' prior probability. Thirteen out of eighteen SNPs directly connected with alcoholism were found concordance with potential risk regions of alcoholism in OMIM database. As many SNPs were found contributing to alteration on gene expression, known as expression quantitative trait loci (eQTLs), we further sought to identify chemical compounds acting as regulators of alcoholism genes captured by causal SNPs. Chloroprene and valproic acid were identified as the expression regulators for genes C11orf66 and SALL3 which were captured by alcoholism SNPs, respectively. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Field Synopsis and Re-analysis of Systematic Meta-analyses of Genetic Association Studies in Multiple Sclerosis: a Bayesian Approach.

PubMed

Park, Jae Hyon; Kim, Joo Hi; Jo, Kye Eun; Na, Se Whan; Eisenhut, Michael; Kronbichler, Andreas; Lee, Keum Hwa; Shin, Jae Il

2018-07-01

To provide an up-to-date summary of multiple sclerosis-susceptible gene variants and assess the noteworthiness in hopes of finding true associations, we investigated the results of 44 meta-analyses on gene variants and multiple sclerosis published through December 2016. Out of 70 statistically significant genotype associations, roughly a fifth (21%) of the comparisons showed noteworthy false-positive rate probability (FPRP) at a statistical power to detect an OR of 1.5 and at a prior probability of 10 -6 assumed for a random single nucleotide polymorphism. These associations (IRF8/rs17445836, STAT3/rs744166, HLA/rs4959093, HLA/rs2647046, HLA/rs7382297, HLA/rs17421624, HLA/rs2517646, HLA/rs9261491, HLA/rs2857439, HLA/rs16896944, HLA/rs3132671, HLA/rs2857435, HLA/rs9261471, HLA/rs2523393, HLA-DRB1/rs3135388, RGS1/rs2760524, PTGER4/rs9292777) also showed a noteworthy Bayesian false discovery probability (BFDP) and one additional association (CD24 rs8734/rs52812045) was also noteworthy via BFDP computation. Herein, we have identified several noteworthy biomarkers of multiple sclerosis susceptibility. We hope these data are used to study multiple sclerosis genetics and inform future screening programs.

How Acute Total Sleep Loss Affects the Attending Brain: A Meta-Analysis of Neuroimaging Studies

PubMed Central

Ma, Ning; Dinges, David F.; Basner, Mathias; Rao, Hengyi

2015-01-01

Study Objectives: Attention is a cognitive domain that can be severely affected by sleep deprivation. Previous neuroimaging studies have used different attention paradigms and reported both increased and reduced brain activation after sleep deprivation. However, due to large variability in sleep deprivation protocols, task paradigms, experimental designs, characteristics of subject populations, and imaging techniques, there is no consensus regarding the effects of sleep loss on the attending brain. The aim of this meta-analysis was to identify brain activations that are commonly altered by acute total sleep deprivation across different attention tasks. Design: Coordinate-based meta-analysis of neuroimaging studies of performance on attention tasks during experimental sleep deprivation. Methods: The current version of the activation likelihood estimation (ALE) approach was used for meta-analysis. The authors searched published articles and identified 11 sleep deprivation neuroimaging studies using different attention tasks with a total of 185 participants, equaling 81 foci for ALE analysis. Results: The meta-analysis revealed significantly reduced brain activation in multiple regions following sleep deprivation compared to rested wakefulness, including bilateral intraparietal sulcus, bilateral insula, right prefrontal cortex, medial frontal cortex, and right parahippocampal gyrus. Increased activation was found only in bilateral thalamus after sleep deprivation compared to rested wakefulness. Conclusion: Acute total sleep deprivation decreases brain activation in the fronto-parietal attention network (prefrontal cortex and intraparietal sulcus) and in the salience network (insula and medial frontal cortex). Increased thalamic activation after sleep deprivation may reflect a complex interaction between the de-arousing effects of sleep loss and the arousing effects of task performance on thalamic activity. Citation: Ma N, Dinges DF, Basner M, Rao H. How acute total sleep loss affects the attending brain: a meta-analysis of neuroimaging studies. SLEEP 2015;38(2):233–240. PMID:25409102

A Dynamic Bayesian Network Model for the Production and Inventory Control

NASA Astrophysics Data System (ADS)

Shin, Ji-Sun; Takazaki, Noriyuki; Lee, Tae-Hong; Kim, Jin-Il; Lee, Hee-Hyol

In general, the production quantities and delivered goods are changed randomly and then the total stock is also changed randomly. This paper deals with the production and inventory control using the Dynamic Bayesian Network. Bayesian Network is a probabilistic model which represents the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of a lower limit and a ceiling of the total stock to certain values is shown.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

Cultural Geography Model Validation

DTIC Science & Technology

2010-03-01

the Cultural Geography Model (CGM), a government owned, open source multi - agent system utilizing Bayesian networks, queuing systems, the Theory of...referent determined either from theory or SME opinion. 4. CGM Overview The CGM is a government-owned, open source, data driven multi - agent social...HSCB, validation, social network analysis ABSTRACT: In the current warfighting environment , the military needs robust modeling and simulation (M&S

Impact of trucking network flow on preferred biorefinery locations in the southern United States

Treesearch

Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen

2017-01-01

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...

Impact of censoring on learning Bayesian networks in survival modelling.

PubMed

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from data can be used to learn from censored survival data in the presence of light censoring (up to 20%) by treating censored cases as event-free. Given intermediate or heavy censoring, the learnt models become tuned to the majority class and would thus require a different approach.

Systematic review with meta-analysis: comparative efficacy of immunosuppressants and biologics for reducing hospitalisation and surgery in Crohn's disease and ulcerative colitis.

PubMed

Mao, E J; Hazlewood, G S; Kaplan, G G; Peyrin-Biroulet, L; Ananthakrishnan, A N

2017-01-01

Crohn's disease (CD) and ulcerative colitis (UC) have a progressive course leading to hospitalisation and surgery. The ability of existing therapies to alter disease course is not clearly defined. To investigate the comparative efficacy of currently available inflammatory bowel disease (IBD) therapies to reduce hospitalisation and surgery. We conducted a systematic review in MEDLINE/PubMed for randomised controlled trials (RCT) published between January 1980 and May 2016 examining efficacy of biological or immunomodulator therapy in IBD. We performed direct comparisons of pooled proportions of hospitalisation and surgery. Pair-wise comparisons using a random-effects Bayesian network meta-analysis were performed to assess comparative efficacy of different treatments. We identified seven randomised controlled trials (5 CD; 2 UC) comparing three biologics and one immunomodulator with placebo. In CD, anti-TNF biologics significantly reduced hospitalisation [Odds ratio (OR) 0.46, 95% confidence interval (CI) 0.36-0.60] and surgery (OR 0.23, 95% CI 0.13-0.42) compared to placebo. No statistically significant reduction was noted with azathioprine or vedolizumab. Azathioprine was inferior to both infliximab and adalimumab in preventing CD-related hospitalisation (>97.5% probability). Anti-TNF biologics significantly reduced hospitalisation (OR 0.48, 95% CI 0.29-0.80) and surgery (OR 0.67, 95% CI 0.46-0.97) in UC. There were no statistically significant differences in the pair-wise comparisons between active treatments. In CD and UC, anti-TNF biologics are efficacious in reducing the odds of hospitalisation by half and surgery by 33-77%. Azathioprine and vedolizumab were not associated with a similar improvement, but robust conclusions may be limited due to paucity of RCTs. © 2016 John Wiley & Sons Ltd.

Trans-ethnic meta-analysis of white blood cell phenotypes

PubMed Central

Keller, Margaux F.; Reiner, Alexander P.; Okada, Yukinori; van Rooij, Frank J.A.; Johnson, Andrew D.; Chen, Ming-Huei; Smith, Albert V.; Morris, Andrew P.; Tanaka, Toshiko; Ferrucci, Luigi; Zonderman, Alan B.; Lettre, Guillaume; Harris, Tamara; Garcia, Melissa; Bandinelli, Stefania; Qayyum, Rehan; Yanek, Lisa R.; Becker, Diane M.; Becker, Lewis C.; Kooperberg, Charles; Keating, Brendan; Reis, Jared; Tang, Hua; Boerwinkle, Eric; Kamatani, Yoichiro; Matsuda, Koichi; Kamatani, Naoyuki; Nakamura, Yusuke; Kubo, Michiaki; Liu, Simin; Dehghan, Abbas; Felix, Janine F.; Hofman, Albert; Uitterlinden, André G.; van Duijn, Cornelia M.; Franco, Oscar H.; Longo, Dan L.; Singleton, Andrew B.; Psaty, Bruce M.; Evans, Michelle K.; Cupples, L. Adrienne; Rotter, Jerome I.; O'Donnell, Christopher J.; Takahashi, Atsushi; Wilson, James G.; Ganesh, Santhi K.; Nalls, Mike A.

2014-01-01

White blood cell (WBC) count is a common clinical measure used as a predictor of certain aspects of human health, including immunity and infection status. WBC count is also a complex trait that varies among individuals and ancestry groups. Differences in linkage disequilibrium structure and heterogeneity in allelic effects are expected to play a role in the associations observed between populations. Prior genome-wide association study (GWAS) meta-analyses have identified genomic loci associated with WBC and its subtypes, but much of the heritability of these phenotypes remains unexplained. Using GWAS summary statistics for over 50 000 individuals from three diverse populations (Japanese, African-American and European ancestry), a Bayesian model methodology was employed to account for heterogeneity between ancestry groups. This approach was used to perform a trans-ethnic meta-analysis of total WBC, neutrophil and monocyte counts. Ten previously known associations were replicated and six new loci were identified, including several regions harboring genes related to inflammation and immune cell function. Ninety-five percent credible interval regions were calculated to narrow the association signals and fine-map the putatively causal variants within loci. Finally, a conditional analysis was performed on the most significant SNPs identified by the trans-ethnic meta-analysis (MA), and nine secondary signals within loci previously associated with WBC or its subtypes were identified. This work illustrates the potential of trans-ethnic analysis and ascribes a critical role to multi-ethnic cohorts and consortia in exploring complex phenotypes with respect to variants that lie outside the European-biased GWAS pool. PMID:25096241

Distributed multisensory integration in a recurrent network model through supervised learning

NASA Astrophysics Data System (ADS)

Wang, He; Wong, K. Y. Michael

Sensory integration between different modalities has been extensively studied. It is suggested that the brain integrates signals from different modalities in a Bayesian optimal way. However, how the Bayesian rule is implemented in a neural network remains under debate. In this work we propose a biologically plausible recurrent network model, which can perform Bayesian multisensory integration after trained by supervised learning. Our model is composed of two modules, each for one modality. We assume that each module is a recurrent network, whose activity represents the posterior distribution of each stimulus. The feedforward input on each module is the likelihood of each modality. Two modules are integrated through cross-links, which are feedforward connections from the other modality, and reciprocal connections, which are recurrent connections between different modules. By stochastic gradient descent, we successfully trained the feedforward and recurrent coupling matrices simultaneously, both of which resembles the Mexican-hat. We also find that there are more than one set of coupling matrices that can approximate the Bayesian theorem well. Specifically, reciprocal connections and cross-links will compensate each other if one of them is removed. Even though trained with two inputs, the network's performance with only one input is in good accordance with what is predicted by the Bayesian theorem.

Comparison of Risk of Carpal Tunnel Syndrome in Patients with Distal Radius Fractures After 7 Treatments

PubMed Central

Zhao, Huan-Li; Wang, Gui-Bin; Jia, Yue-Qing; Zhu, Shi-Cai; Zhang, Feng-Fang; Liu, Hong-Mei

2015-01-01

Background To compare risk of carpal tunnel syndrome (CTS) in distal radius fracture (DRF) patients after 7 treatments using bridging external fixation (BrEF), non-bridging external fixation (non-BrEF), plaster fixation, K-wire fixation, dorsal plating fixation, volar plating fixation, and dorsal and volar plating by performing a network meta-analysis. Material/Methods An exhaustive search of electronic databases identified randomized controlled trails (RCTs) closely related to our study topic. The published articles were screened, based on predefined inclusion and exclusion criteria, to select high-quality studies for the present network meta-analysis. Data extracted from the selected studies were analyzed using STATA version 12.0 software. Results The literature search and selection process identified 12 eligible RCTs that contained a total of 1370 DRF patients (394 patients with BrEF, 377 patients with non-BrEF, 89 patients with K-wire fixation, 192 patients with plaster fixation, 42 patients with dorsal plating fixation, 152 patients with volar plating fixation, and 124 patients with dorsal and volar plating fixation). Our network meta-analysis results demonstrated no significant differences in CTS risk among the 7 treatments (P>0.05). The value of surface under the cumulative ranking curve (SUCRA), however, suggested that dorsal plating fixation is the optimal treatment, with the lowest risk of CTS in DRF patients (dorsal plating fixation: 89.2%; dorsal and volar plating: 57.8%; plaster fixation: 50.9%; non-BrEF: 50.6%; volar plating fixation: 39.6%; BrEF: 38.4%; K-wire fixation: 23.6%). Conclusions Our network meta-analysis provides evidence that dorsal plating fixation significantly decreases the risk of CTS and could be the method of choice in DRF patients. PMID:26391617

Asian Perspectives on Diagnostic and Therapeutic Strategies in Inflammatory Bowel Disease: Report and Analysis of a Survey with Questionnaires.

PubMed

Yoshida, Atsushi; Ueno, Fumiaki; Morizane, Toshio; Joh, Takashi; Kamiya, Takeshi; Takahashi, Shin''ichi; Tokunaga, Kengo; Iwakiri, Ryuichi; Kinoshita, Yoshikazu; Suzuki, Hidekazu; Naito, Yuji; Uchiyama, Kazuhiko; Fukodo, Shin; Chan, Francis K L; Halm, Ki-Baik; Kachintorn, Udom; Fock, Kwong Ming; Rani, Abdul Aziz; Syam, Ari Fahrial; Sollano, Jose D; Zhu, Qi

2017-01-01

Diagnostic and therapeutic strategies in inflammatory bowel disease (IBD) vary among countries in terms of availability of modalities, affordability of health care resource, health care policy and cultural background. This may be the case in different countries in Eastern Asia. The aim of this study was to determine and understand the differences in diagnostic and therapeutic strategies of IBD between Japan and the rest of Asian countries (ROA). Questionnaires with regard to clinical practice in IBD were distributed to members of the International Gastroenterology Consensus Symposium Study Group. The responders were allowed to select multiple items for each question, as multiple modalities are frequently utilized in the diagnosis and the management of IBD. Dependency and independency of selected items for each question were evaluated by the Bayesian network analysis. The selected diagnostic modalities were not very different between Japan and ROA, except for those related to small bowel investigations. Balloon-assisted enteroscopy and small bowel follow through are frequently used in Japan, while CT/MR enterography is popular in ROA. Therapeutic modalities for IBD depend on availability of such modalities in clinical practice. As far as modalities commonly available in both regions are concerned, there seemed to be similarity in the selection of each therapeutic modality. However, evaluation of dependency of separate therapeutic modalities by Bayesian network analysis disclosed some difference in therapeutic strategies between Japan and ROA. Although selected modalities showed some similarity, Bayesian network analysis elicited certain differences in the clinical approaches combining multiple modalities in various aspects of IBD between Japan and ROA. © 2016 S. Karger AG, Basel.

Mapping Creativity: Creativity Measurements Network Analysis

ERIC Educational Resources Information Center

Pinheiro, Igor Reszka; Cruz, Roberto Moraes

2014-01-01

This article borrowed network analysis tools to discover how the construct formed by the set of all measures of creativity configures itself. To this end, using a variant of the meta-analytical method, a database was compiled simulating 42,381 responses to 974 variables centered on 64 creativity measures. Results, although preliminary, indicate…

Systematic review and network meta-analysis of tedizolid for the treatment of acute bacterial skin and skin structure infections caused by MRSA.

PubMed

McCool, Rachael; Gould, Ian M; Eales, Jacqui; Barata, Teresa; Arber, Mick; Fleetwood, Kelly; Glanville, Julie; Kauf, Teresa L

2017-01-07

Tedizolid, the active moiety of tedizolid phosphate, is approved in the United States, the European Union, Canada and a number of other countries for the treatment of acute bacterial skin and skin structure infections (ABSSSI) caused by certain susceptible bacteria, including methicillin-resistant Staphylococcus aureus (MRSA). This network meta-analysis (NMA) evaluates the comparative effectiveness of tedizolid and other antibacterials indicated for the treatment of ABSSSI caused by MRSA. Systematic review of 10 databases was undertaken to inform an NMA to estimate the relative effectiveness of tedizolid and established monotherapy comparators (ceftaroline, daptomycin, linezolid, teicoplanin, tigecycline, vancomycin) for treating MRSA-associated ABSSSI. Randomized controlled trials enrolling adults with ABSSSI or complicated skin and skin structure infections caused by suspected/documented MRSA were eligible for inclusion. Networks were developed based on similarity of study design, patient characteristics, outcome measures and available data. Outcomes of interest included clinical response at end of therapy (EOT), post-therapy evaluation (PTE) or test-of-cure assessment and treatment discontinuations resulting from adverse events (AEs). Bayesian NMA was conducted for each outcome using fixed-effects and random effects models. Literature searches identified 3,618 records; 15 trials met the inclusion criteria and were considered suitable for NMA comparison. In fixed-effects models, tedizolid had higher odds of clinical response at EOT (odds ratio [OR], 1.7; credible interval, 1.0, 3.0) and PTE than vancomycin (OR, 1.6; credible interval, 1.1, 2.5). No differences in odds of clinical response at EOT or PTE were observed between tedizolid and other comparators. There was no evidence of a difference among treatments for discontinuation due to AEs. Results from random effects and fixed-effects models were generally consistent. Tedizolid was superior to vancomycin for clinical response at EOT and PTE. There was no evidence of a difference between tedizolid and other comparators and no evidence of a difference between tedizolid and all comparators when evaluating discontinuation due to AEs. These findings suggest that tedizolid provides an alternative option for the management of serious skin infections caused by suspected or documented MRSA. This study is subject to the limitations inherent in all NMAs, and the results should be interpreted accordingly.

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuesong; Liang, Faming; Yu, Beibei

2011-11-09

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associatedmore » with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.« less

Evolution of Associative Learning in Chemical Networks

PubMed Central

McGregor, Simon; Vasas, Vera; Husbands, Phil; Fernando, Chrisantha

2012-01-01

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ‘memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells. PMID:23133353

Bayesian networks improve causal environmental assessments for evidence-based policy

EPA Science Inventory

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the p...

Functional changes of neural circuits in stroke patients with dysphagia: A meta-analysis.

PubMed

Liu, Lu; Xiao, Yuan; Zhang, Wenjing; Yao, Li; Gao, Xin; Chandan, Shah; Lui, Su

2017-08-01

Dysphagia is a common problem in stroke patients with unclear pathogenesis. Several recent functional magnetic resonance imaging (fMRI) studies had been carried out to explore the cerebral functional changes in dysphagic stroke patients. The aim of this study was to analysis these imaging findings using a meta-analysis. We used seed-based d mapping (SDM) to conduct a meta-analysis for dysphagic stroke patients prior to any kind of special treatment for dysphagia. A systematic search was conducted for the relevant studies. SDM meta-analysis method was used to examine regions of increased and decreased functional activation between dysphagic stroke patients and healthy controls. Finally, six studies including 81 stroke patients with dysphagia and 78 healthy controls met the inclusion standards. When compared with healthy controls, stroke patients with dysphagia showed hyperactivation in left cingulate gyrus, left precentral gyrus and right posterior cingulate gyrus, and hypoactivation in right cuneus and left middle frontal gyrus. The hyperactivity of precentral gyrus is crucial in stroke patients with dysphagia and may be associated with the severity of stroke. Besides the motor areas, the default-mode network regions (DMN) and affective network regions (AN) circuits are also involved in dysphagia after stroke. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

PubMed

Zhang, Chengxin; Zheng, Wei; Freddolino, Peter L; Zhang, Yang

2018-03-10

Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/. Copyright © 2018. Published by Elsevier Ltd.

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era.

PubMed

Zador, Zsolt; Huang, Wendy; Sperrin, Matthew; Lawton, Michael T

2018-06-01

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

Quantum Bayesian networks with application to games displaying Parrondo's paradox

NASA Astrophysics Data System (ADS)

Pejic, Michael

Bayesian networks and their accompanying graphical models are widely used for prediction and analysis across many disciplines. We will reformulate these in terms of linear maps. This reformulation will suggest a natural extension, which we will show is equivalent to standard textbook quantum mechanics. Therefore, this extension will be termed quantum. However, the term quantum should not be taken to imply this extension is necessarily only of utility in situations traditionally thought of as in the domain of quantum mechanics. In principle, it may be employed in any modelling situation, say forecasting the weather or the stock market---it is up to experiment to determine if this extension is useful in practice. Even restricting to the domain of quantum mechanics, with this new formulation the advantages of Bayesian networks can be maintained for models incorporating quantum and mixed classical-quantum behavior. The use of these will be illustrated by various basic examples. Parrondo's paradox refers to the situation where two, multi-round games with a fixed winning criteria, both with probability greater than one-half for one player to win, are combined. Using a possibly biased coin to determine the rule to employ for each round, paradoxically, the previously losing player now wins the combined game with probabilitygreater than one-half. Using the extended Bayesian networks, we will formulate and analyze classical observed, classical hidden, and quantum versions of a game that displays this paradox, finding bounds for the discrepancy from naive expectations for the occurrence of the paradox. A quantum paradox inspired by Parrondo's paradox will also be analyzed. We will prove a bound for the discrepancy from naive expectations for this paradox as well. Games involving quantum walks that achieve this bound will be presented.

Reevaluating the effectiveness of n-back training on transfer through the Bayesian lens: Support for the null.

PubMed

Dougherty, Michael R; Hamovitz, Toby; Tidwell, Joe W

2016-02-01

A recent meta-analysis by Au et al. Psychonomic Bulletin & Review, 22, 366-377, (2015) reviewed the n-back training paradigm for working memory (WM) and evaluated whether (when aggregating across existing studies) there was evidence that gains obtained for training tasks transferred to gains in fluid intelligence (Gf). Their results revealed an overall effect size of g = 0.24 for the effect of n-back training on Gf. We reexamine the data through a Bayesian lens, to evaluate the relative strength of the evidence for the alternative versus null hypotheses, contingent on the type of control condition used. We find that studies using a noncontact (passive) control group strongly favor the alternative hypothesis that training leads to transfer but that studies using active-control groups show modest evidence in favor of the null. We discuss these findings in the context of placebo effects.

Interventions for obtaining and maintaining employment in adults with severe mental illness, a network meta-analysis.

PubMed

Suijkerbuijk, Yvonne B; Schaafsma, Frederieke G; van Mechelen, Joost C; Ojajärvi, Anneli; Corbière, Marc; Anema, Johannes R

2017-09-12

People with severe mental illness show high rates of unemployment and work disability, however, they often have a desire to participate in employment. People with severe mental illness used to be placed in sheltered employment or were enrolled in prevocational training to facilitate transition to a competitive job. Now, there are also interventions focusing on rapid search for a competitive job, with ongoing support to keep the job, known as supported employment. Recently, there has been a growing interest in combining supported employment with other prevocational or psychiatric interventions. To assess the comparative effectiveness of various types of vocational rehabilitation interventions and to rank these interventions according to their effectiveness to facilitate competitive employment in adults with severe mental illness. In November 2016 we searched CENTRAL, MEDLINE, Embase, PsychINFO, and CINAHL, and reference lists of articles for randomised controlled trials and systematic reviews. We identified systematic reviews from which to extract randomised controlled trials. We included randomised controlled trials and cluster-randomised controlled trials evaluating the effect of interventions on obtaining competitive employment for adults with severe mental illness. We included trials with competitive employment outcomes. The main intervention groups were prevocational training programmes, transitional employment interventions, supported employment, supported employment augmented with other specific interventions, and psychiatric care only. Two authors independently identified trials, performed data extraction, including adverse events, and assessed trial quality. We performed direct meta-analyses and a network meta-analysis including measurements of the surface under the cumulative ranking curve (SUCRA). We assessed the quality of the evidence for outcomes within the network meta-analysis according to GRADE. We included 48 randomised controlled trials involving 8743 participants. Of these, 30 studied supported employment, 13 augmented supported employment, 17 prevocational training, and 6 transitional employment. Psychiatric care only was the control condition in 13 studies. Direct comparison meta-analysis of obtaining competitive employmentWe could include 18 trials with short-term follow-up in a direct meta-analysis (N = 2291) of the following comparisons. Supported employment was more effective than prevocational training (RR 2.52, 95% CI 1.21 to 5.24) and transitional employment (RR 3.49, 95% CI 1.77 to 6.89) and prevocational training was more effective than psychiatric care only (RR 8.96, 95% CI 1.77 to 45.51) in obtaining competitive employment.For the long-term follow-up direct meta-analysis, we could include 22 trials (N = 5233). Augmented supported employment (RR 4.32, 95% CI 1.49 to 12.48), supported employment (RR 1.51, 95% CI 1.36 to 1.68) and prevocational training (RR 2.19, 95% CI 1.07 to 4.46) were more effective than psychiatric care only. Augmented supported employment was more effective than supported employment (RR 1.94, 95% CI 1.03 to 3.65), transitional employment (RR 2.45, 95% CI 1.69 to 3.55) and prevocational training (RR 5.42, 95% CI 1.08 to 27.11). Supported employment was more effective than transitional employment (RR 3.28, 95% CI 2.13 to 5.04) and prevocational training (RR 2.31, 95% CI 1.85 to 2.89). Network meta-analysis of obtaining competitive employmentWe could include 22 trials with long-term follow-up in a network meta-analysis.Augmented supported employment was the most effective intervention versus psychiatric care only in obtaining competitive employment (RR 3.81, 95% CI 1.99 to 7.31, SUCRA 98.5, moderate-quality evidence), followed by supported employment (RR 2.72 95% CI 1.55 to 4.76; SUCRA 76.5, low-quality evidence).Prevocational training (RR 1.26, 95% CI 0.73 to 2.19; SUCRA 40.3, very low-quality evidence) and transitional employment were not considerably different from psychiatric care only (RR 1.00,95% CI 0.51 to 1.96; SUCRA 17.2, low-quality evidence) in achieving competitive employment, but prevocational training stood out in the SUCRA value and rank.Augmented supported employment was slightly better than supported employment, but not significantly (RR 1.40, 95% CI 0.92 to 2.14). The SUCRA value and mean rank were higher for augmented supported employment.The results of the network meta-analysis of the intervention subgroups favoured augmented supported employment interventions, but also cognitive training. However, supported employment augmented with symptom-related skills training showed the best results (RR compared to psychiatric care only 3.61 with 95% CI 1.03 to 12.63, SUCRA 80.3).We graded the quality of the evidence of the network ranking as very low because of potential risk of bias in the included studies, inconsistency and publication bias. Direct meta-analysis of maintaining competitive employment Based on the direct meta-analysis of the short-term follow-up of maintaining employment, supported employment was more effective than: psychiatric care only, transitional employment, prevocational training, and augmented supported employment.In the long-term follow-up direct meta-analysis, augmented supported employment was more effective than prevocational training (MD 22.79 weeks, 95% CI 15.96 to 29.62) and supported employment (MD 10.09, 95% CI 0.32 to 19.85) in maintaining competitive employment. Participants receiving supported employment worked more weeks than those receiving transitional employment (MD 17.36, 95% CI 11.53 to 23.18) or prevocational training (MD 11.56, 95% CI 5.99 to 17.13).We did not find differences between interventions in the risk of dropouts or hospital admissions. Supported employment and augmented supported employment were the most effective interventions for people with severe mental illness in terms of obtaining and maintaining employment, based on both the direct comparison analysis and the network meta-analysis, without increasing the risk of adverse events. These results are based on moderate- to low-quality evidence, meaning that future studies with lower risk of bias could change these results. Augmented supported employment may be slightly more effective compared to supported employment alone. However, this difference was small, based on the direct comparison analysis, and further decreased with the network meta-analysis meaning that this difference should be interpreted cautiously. More studies on maintaining competitive employment are needed to get a better understanding of whether the costs and efforts are worthwhile in the long term for both the individual and society.

Evidence synthesis for decision making 7: a reviewer's checklist.

PubMed

Ades, A E; Caldwell, Deborah M; Reken, Stefanie; Welton, Nicky J; Sutton, Alex J; Dias, Sofia

2013-07-01

This checklist is for the review of evidence syntheses for treatment efficacy used in decision making based on either efficacy or cost-effectiveness. It is intended to be used for pairwise meta-analysis, indirect comparisons, and network meta-analysis, without distinction. It does not generate a quality rating and is not prescriptive. Instead, it focuses on a series of questions aimed at revealing the assumptions that the authors of the synthesis are expecting readers to accept, the adequacy of the arguments authors advance in support of their position, and the need for further analyses or sensitivity analyses. The checklist is intended primarily for those who review evidence syntheses, including indirect comparisons and network meta-analyses, in the context of decision making but will also be of value to those submitting syntheses for review, whether to decision-making bodies or journals. The checklist has 4 main headings: A) definition of the decision problem, B) methods of analysis and presentation of results, C) issues specific to network synthesis, and D) embedding the synthesis in a probabilistic cost-effectiveness model. The headings and implicit advice follow directly from the other tutorials in this series. A simple table is provided that could serve as a pro forma checklist.

Posterior Predictive Model Checking in Bayesian Networks

ERIC Educational Resources Information Center

Crawford, Aaron

2014-01-01

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

Statins in adult patients with HIV: Protocol for a systematic review and network meta-analysis.

PubMed

Roever, Leonardo; Resende, Elmiro Santos; Diniz, Angélica Lemos Debs; Penha-Silva, Nilson; O'Connell, João Lucas; Gomes, Paulo Fernando Silva; Zanetti, Hugo Ribeiro; Roerver-Borges, Anaisa Silva; Veloso, Fernando César; Fidale, Thiago Montes; Casella-Filho, Antonio; Dourado, Paulo Magno Martins; Chagas, Antonio Carlos Palandri; Ali-Hasan-Al-Saegh, Sadeq; Reis, Paulo Eduardo Ocke; Pinto, Rogério de Melo; Oliveira, Gustavo B F; Avezum, Álvaro; Neto, Mansueto; Durães, André; Silva, Rose Mary Ferreira Lisboa da; Grande, Antonio José; Denardi, Celise; Lopes, Renato Delascio; Nerlekar, Nitesh; Alizadeh, Shahab; Hernandez, Adrian V; Biondi-Zoccai, Giuseppe

2018-04-01

Patients with HIV have been found to suffer from lipid abnormalities, including elevated levels of total and LDL-cholesterol as well as triglyceride levels. Abnormal lipid levels are associated with an increased risk of developing cardiovascular diseases, which are significant causes of mortality among the general population. Therefore, the objective of the current study is to conduct a systematic review with network meta-analysis to compare the effects of statins classes on HIV patients. Randomized clinical trials (RCTs) and observational studies published in English up to 31 December 2017, and which include direct and/or indirect evidence, will be included. Studies will be retrieved by searching four electronic databases and cross-referencing. Dual selection and abstraction of data will occur. The primary outcome will all-cause mortality, new event of acute myocardial infarction, stroke (hemorrhagic and ischemic), hospitalization for acute coronary syndrome and urgent revascularization procedures and cardiovascular mortality. Secondary outcomes will be assessment of the differences in change of total cholesterol (TC), low-density lipoprotein (LDL-C), apolipoprotein B (ApoB), high density lipoprotein (HDL-C). Risk of bias will be assessed using the Cochrane Risk of Bias assessment instrument for RCTs and the Strengthening the Reporting of Observational Studies in Epidemiology instrument for observational studies. Network meta-analysis will be performed using multivariate random-effects meta-regression models. The surface under the cumulative ranking curve will be used to provide a hierarchy of statins that reduce cardiovascular mortality in HIV patients. A revised version of the Cochrane Risk of Bias tool (RoB 2.0) will be used to assess the risk of bias in eligible RCTs. Results will be synthesized and analyzed using network meta-analysis (NMA). Overall strength of the evidence and publication bias will be evaluated. Subgroup and sensitivity analysis will also be performed. Ethics approval was not required for this study because it was based on published studies. The results and findings of this study will be submitted and published in a scientific peer-reviewed journal. The evidence will determine which combination of interventions are most promising for current practice and further investigation. PROSPERO (CRD42017072996).

A Bayesian network to predict vulnerability to sea-level rise: data report

USGS Publications Warehouse

Gutierrez, Benjamin T.; Plant, Nathaniel G.; Thieler, E. Robert

2011-01-01

During the 21st century, sea-level rise is projected to have a wide range of effects on coastal environments, development, and infrastructure. Consequently, there has been an increased focus on developing modeling or other analytical approaches to evaluate potential impacts to inform coastal management. This report provides the data that were used to develop and evaluate the performance of a Bayesian network designed to predict long-term shoreline change due to sea-level rise. The data include local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline-change rate compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the U.S. Atlantic coast. In this project, the Bayesian network is used to define relationships among driving forces, geologic constraints, and coastal responses. Using this information, the Bayesian network is used to make probabilistic predictions of shoreline change in response to different future sea-level-rise scenarios.

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

PubMed

Constantinou, Anthony Costa; Yet, Barbaros; Fenton, Norman; Neil, Martin; Marsh, William

2016-01-01

Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science. Copyright © 2015 Elsevier B.V. All rights reserved.

Applying Bayesian belief networks in rapid response situations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gibson, William L; Deborah, Leishman, A.; Van Eeckhout, Edward

2008-01-01

The authors have developed an enhanced Bayesian analysis tool called the Integrated Knowledge Engine (IKE) for monitoring and surveillance. The enhancements are suited for Rapid Response Situations where decisions must be made based on uncertain and incomplete evidence from many diverse and heterogeneous sources. The enhancements extend the probabilistic results of the traditional Bayesian analysis by (1) better quantifying uncertainty arising from model parameter uncertainty and uncertain evidence, (2) optimizing the collection of evidence to reach conclusions more quickly, and (3) allowing the analyst to determine the influence of the remaining evidence that cannot be obtained in the time allowed.more » These extended features give the analyst and decision maker a better comprehension of the adequacy of the acquired evidence and hence the quality of the hurried decisions. They also describe two example systems where the above features are highlighted.« less

Network meta-analysis: application and practice using Stata

PubMed Central

2017-01-01

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions—similarity, transitivity, and consistency—should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system. PMID:29092392

Network meta-analysis: application and practice using Stata.

PubMed

Shim, Sungryul; Yoon, Byung-Ho; Shin, In-Soo; Bae, Jong-Myon

2017-01-01

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions-similarity, transitivity, and consistency-should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.

A Biologically Informed Framework for the Analysis of the PPAR Signaling Pathway using a Bayesian Network

EPA Science Inventory

The US EPA’s ToxCastTM program seeks to combine advances in high-throughput screening technology with methodologies from statistics and computer science to develop high-throughput decision support tools for assessing chemical hazard and risk. To develop new methods of analysis of...

Relative efficacy and safety of topical non-steroidal anti-inflammatory drugs for osteoarthritis: a systematic review and network meta-analysis of randomised controlled trials and observational studies.

PubMed

Zeng, Chao; Wei, Jie; Persson, Monica S M; Sarmanova, Aliya; Doherty, Michael; Xie, Dongxing; Wang, YiLun; Li, Xiaoxiao; Li, Jiatian; Long, Huizhong; Lei, Guanghua; Zhang, Weiya

2018-05-01

To compare the efficacy and safety of topical non-steroidal anti-inflammatory drugs (NSAIDs), including salicylate, for the treatment of osteoarthritis (OA). PubMed, Embase, Cochrane Library and Web of Science were searched from 1966 to January 2017. Randomised controlled trials (RCTs) comparing topical NSAIDs with placebo or each other in patients with OA and observational studies comparing topical NSAIDs with no treatment or each other irrespective of disease were included. Two investigators identified studies and independently extracted data. Bayesian network and conventional meta-analyses were conducted. The primary outcomes were pain relief for RCTs and risk of adverse effects (AEs) for observational studies. 43 studies, comprising 36 RCTs (7 900 patients with OA) and seven observational studies (218 074 participants), were included. Overall, topical NSAIDs were superior to placebo for relieving pain (standardised mean difference (SMD)=-0.30, 95% CI -0.40 to -0.20) and improving function (SMD=-0.35, 95% CI -0.45 to -0.24) in OA. Of all topical NSAIDs, diclofenac patches were most effective for OA pain (SMD=-0.81, 95% CI -1.12 to -0.52) and piroxicam was most effective for functional improvement (SMD=-1.04, 95% CI -1.60 to -0.48) compared with placebo. Although salicylate gel was associated with higher withdrawal rates due to AEs, the remaining topical NSAIDs were not associated with any increased local or systemic AEs. Topical NSAIDs were effective and safe for OA. Diclofenac patches may be the most effective topical NSAID for pain relief. No serious gastrointestinal and renal AEs were observed in trials or the general population. However, confirmation of the cardiovascular safety of topical NSAIDs still warrants further observational study. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Relative efficacy and safety of topical non-steroidal anti-inflammatory drugs for osteoarthritis: a systematic review and network meta-analysis of randomised controlled trials and observational studies

PubMed Central

Persson, Monica S M; Sarmanova, Aliya; Doherty, Michael; Xie, Dongxing; Wang, YiLun; Li, Xiaoxiao; Li, Jiatian; Long, Huizhong

2018-01-01

Objectives To compare the efficacy and safety of topical non-steroidal anti-inflammatory drugs (NSAIDs), including salicylate, for the treatment of osteoarthritis (OA). Methods PubMed, Embase, Cochrane Library and Web of Science were searched from 1966 to January 2017. Randomised controlled trials (RCTs) comparing topical NSAIDs with placebo or each other in patients with OA and observational studies comparing topical NSAIDs with no treatment or each other irrespective of disease were included. Two investigators identified studies and independently extracted data. Bayesian network and conventional meta-analyses were conducted. The primary outcomes were pain relief for RCTs and risk of adverse effects (AEs) for observational studies. Results 43 studies, comprising 36 RCTs (7 900 patients with OA) and seven observational studies (218 074 participants), were included. Overall, topical NSAIDs were superior to placebo for relieving pain (standardised mean difference (SMD)=−0.30, 95% CI −0.40 to –0.20) and improving function (SMD=−0.35, 95% CI −0.45 to –0.24) in OA. Of all topical NSAIDs, diclofenac patches were most effective for OA pain (SMD=−0.81, 95% CI −1.12 to –0.52) and piroxicam was most effective for functional improvement (SMD=−1.04, 95% CI −1.60 to –0.48) compared with placebo. Although salicylate gel was associated with higher withdrawal rates due to AEs, the remaining topical NSAIDs were not associated with any increased local or systemic AEs. Conclusions Topical NSAIDs were effective and safe for OA. Diclofenac patches may be the most effective topical NSAID for pain relief. No serious gastrointestinal and renal AEs were observed in trials or the general population. However, confirmation of the cardiovascular safety of topical NSAIDs still warrants further observational study. PMID:29436380

Network Meta-Analysis Using R: A Review of Currently Available Automated Packages

PubMed Central

Neupane, Binod; Richer, Danielle; Bonner, Ashley Joel; Kibret, Taddele; Beyene, Joseph

2014-01-01

Network meta-analysis (NMA) – a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously – has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA. PMID:25541687

Network meta-analysis using R: a review of currently available automated packages.

PubMed

Neupane, Binod; Richer, Danielle; Bonner, Ashley Joel; Kibret, Taddele; Beyene, Joseph

2014-01-01

Network meta-analysis (NMA)--a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously--has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.

Application of bayesian networks to real-time flood risk estimation

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Blasco, G.

2003-04-01

This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models

Comparative tolerability of treatments for acute migraine: A network meta-analysis.

PubMed

Thorlund, Kristian; Toor, Kabirraaj; Wu, Ping; Chan, Keith; Druyts, Eric; Ramos, Elodie; Bhambri, Rahul; Donnet, Anne; Stark, Richard; Goadsby, Peter J

2017-09-01

Introduction Migraine headache is a neurological disorder whose attacks are associated with nausea, vomiting, photophobia and phonophobia. Treatments for migraine aim to either prevent attacks before they have started or relieve attacks (abort) after onset of symptoms and range from complementary therapies to pharmacological interventions. A number of treatment-related adverse events such as somnolence, fatigue, and chest discomfort have previously been reported in association with triptans. The comparative tolerability of available agents for the abortive treatment of migraine attacks has not yet been systematically reviewed and quantified. Methods We performed a systematic literature review and Bayesian network meta-analysis for comparative tolerability of treatments for migraine. The literature search targeted all randomized controlled trials evaluating oral abortive treatments for acute migraine over a range of available doses in adults. The primary outcomes of interest were any adverse event, treatment-related adverse events, and serious adverse events. Secondary outcomes were fatigue, dizziness, chest discomfort, somnolence, nausea, and vomiting. Results Our search yielded 141 trials covering 15 distinct treatments. Of the triptans, sumatriptan, eletriptan, rizatriptan, zolmitriptan, and the combination treatment of sumatriptan and naproxen were associated with a statistically significant increase in odds of any adverse event or a treatment-related adverse event occurring compared with placebo. Of the non-triptans, only acetaminophen was associated with a statistically significant increase in odds of an adverse event occurring when compared with placebo. Overall, triptans were not associated with increased odds of serious adverse events occurring and the same was the case for non-triptans. For the secondary outcomes, with the exception of vomiting, all triptans except for almotriptan and frovatriptan were significantly associated with increased risk for all outcomes. Almotriptan was significantly associated with an increased risk of vomiting, whereas all other triptans yielded non-significant lower odds compared with placebo. Generally, the non-triptans were not associated with decreased tolerability for the secondary outcomes. Discussion In summary, triptans were associated with higher odds of any adverse event or a treatment-related adverse event occurring when compared to placebo and non-triptans. Non-significant results for non-triptans indicate that these treatments are comparable with one another and placebo regarding tolerability outcomes.

Systematic review and network meta-analysis of the efficacy and safety of tumour necrosis factor inhibitor-methotrexate combination therapy versus triple therapy in rheumatoid arthritis.

PubMed

Fleischmann, Roy; Tongbram, Vanita; van Vollenhoven, Ronald; Tang, Derek H; Chung, James; Collier, David; Urs, Shilpa; Ndirangu, Kerigo; Wells, George; Pope, Janet

2017-01-01

Clinical trials have not consistently demonstrated differences between tumour necrosis factor inhibitor (TNFi) plus methotrexate and triple therapy (methotrexate plus hydroxychloroquine plus sulfasalazine) in rheumatoid arthritis (RA). The study objective was to estimate the efficacy, radiographic benefits, safety and patient-reported outcomes of TNFi-methotrexate versus triple therapy in patients with RA. A systematic review and network meta-analysis (NMA) of randomised controlled trials of TNFi-methotrexate or triple therapy as one of the treatment arms in patients with an inadequate response to or who were naive to methotrexate was conducted. American College of Rheumatology 70% response criteria (ACR70) at 6 months was the prespecified primary endpoint to evaluate depth of response. Data from direct and indirect comparisons between TNFi-methotrexate and triple therapy were pooled and quantitatively analysed using fixed-effects and random-effects Bayesian models. We analysed 33 studies in patients with inadequate response to methotrexate and 19 in patients naive to methotrexate. In inadequate responders, triple therapy was associated with lower odds of achieving ACR70 at 6 months compared with TNFi-methotrexate (OR 0.35, 95% credible interval (CrI) 0.19 to 0.64). Most secondary endpoints tended to favour TNFi-methotrexate in terms of OR direction; however, no clear increased likelihood of achieving these endpoints was observed for either therapy. The odds of infection were lower with triple therapy than with TNFi-methotrexate (OR 0.08, 95% CrI 0.00 to 0.57). There were no differences observed between the two regimens in patients naive to methotrexate. In this NMA, triple therapy was associated with 65% lower odds of achieving ACR70 at 6 months compared with TNFi-methotrexate in patients with inadequate response to methotrexate. Although secondary endpoints numerically favoured TNFi-methotrexate, no clear differences were observed. The odds of infection were greater with TNFi-methotrexate. No differences were observed for patients naive to methotrexate. These results may help inform care of patients who fail methotrexate first-line therapy.

ACE-inhibitors versus angiotensin receptor blockers for prevention of events in cardiovascular patients without heart failure - A network meta-analysis.

PubMed

Ricci, Fabrizio; Di Castelnuovo, Augusto; Savarese, Gianluigi; Perrone Filardi, Pasquale; De Caterina, Raffaele

2016-08-15

Angiotensin receptor blockers (ARBs) are a valuable option to reduce cardiovascular (CV) mortality and morbidity in cardiac patients in whom ACE-inhibitors (ACE-Is) cannot be used. However, clinical outcome data from direct comparisons between ACE-Is and ARBs are scarce, and some data have recently suggested superiority of ACE-Is over ARBs. We performed a Bayesian network-meta-analysis, with data from both direct and indirect comparisons, from 27 randomized controlled trials (RCTs), including a total population of 125,330 patients, to assess the effects of ACE-Is and ARBs on the composite endpoint of CV death, myocardial infarction (MI) and stroke, and on all-cause death, new-onset heart failure (HF) and new-onset diabetes mellitus (DM) in high CV risk patients without HF. Using placebo as a common comparator, we found no significant differences between ACE-Is and ARBs in preventing the composite endpoint of CV death, MI and stroke (RR: 0.92; 95% CI 0.78-1.08). When components of the composite outcome were analysed separately, ACEi and ARBs were associated with a similar risk of CV death (RR: 0.92; 95% CI 0.73-1.10), MI (RR: 0.91; 95% CI 0.78-1.07) and stroke (RR: 0.97; 95% CI 0.79-1.19), as well as a similar incident risk of all-cause death (RR: 0.94; 95% CI 0.85-1.05), new-onset HF (RR: 0.92; 95% CI 0.77-1.15) and new-onset DM (RR: 99; 95% CI 0.81-1.21). With the limitations of indirect comparisons, we found that in patients at high CV risk without HF, ARBs were similar to ACE-Is in preventing the composite endpoint of CV death, MI and stroke. Compared with ARBs, we found no evidence of statistical superiority for ACE-Is, as a class, in preventing incident risk of all-cause death, CV death, MI, stroke, new-onset DM and new-onset HF. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Systematic review and network meta-analysis of the efficacy and safety of tumour necrosis factor inhibitor–methotrexate combination therapy versus triple therapy in rheumatoid arthritis

PubMed Central

Fleischmann, Roy; Tongbram, Vanita; van Vollenhoven, Ronald; Tang, Derek H; Chung, James; Collier, David; Urs, Shilpa; Ndirangu, Kerigo; Wells, George; Pope, Janet

2017-01-01

Objective Clinical trials have not consistently demonstrated differences between tumour necrosis factor inhibitor (TNFi) plus methotrexate and triple therapy (methotrexate plus hydroxychloroquine plus sulfasalazine) in rheumatoid arthritis (RA). The study objective was to estimate the efficacy, radiographic benefits, safety and patient-reported outcomes of TNFi–methotrexate versus triple therapy in patients with RA. Methods A systematic review and network meta-analysis (NMA) of randomised controlled trials of TNFi–methotrexate or triple therapy as one of the treatment arms in patients with an inadequate response to or who were naive to methotrexate was conducted. American College of Rheumatology 70% response criteria (ACR70) at 6 months was the prespecified primary endpoint to evaluate depth of response. Data from direct and indirect comparisons between TNFi–methotrexate and triple therapy were pooled and quantitatively analysed using fixed-effects and random-effects Bayesian models. Results We analysed 33 studies in patients with inadequate response to methotrexate and 19 in patients naive to methotrexate. In inadequate responders, triple therapy was associated with lower odds of achieving ACR70 at 6 months compared with TNFi–methotrexate (OR 0.35, 95% credible interval (CrI) 0.19 to 0.64). Most secondary endpoints tended to favour TNFi–methotrexate in terms of OR direction; however, no clear increased likelihood of achieving these endpoints was observed for either therapy. The odds of infection were lower with triple therapy than with TNFi−methotrexate (OR 0.08, 95% CrI 0.00 to 0.57). There were no differences observed between the two regimens in patients naive to methotrexate. Conclusions In this NMA, triple therapy was associated with 65% lower odds of achieving ACR70 at 6 months compared with TNFi–methotrexate in patients with inadequate response to methotrexate. Although secondary endpoints numerically favoured TNFi–methotrexate, no clear differences were observed. The odds of infection were greater with TNFi–methotrexate. No differences were observed for patients naive to methotrexate. These results may help inform care of patients who fail methotrexate first-line therapy. PMID:28123782

The comparative risk of developing postoperative complications in patients with distal radius fractures following different treatment modalities

PubMed Central

Qiu, Wen-Jun; Li, Yi-Fan; Ji, Yun-Han; Xu, Wei; Zhu, Xiao-Dong; Tang, Xian-Zhong; Zhao, Huan-Li; Wang, Gui-Bin; Jia, Yue-Qing; Zhu, Shi-Cai; Zhang, Feng-Fang; Liu, Hong-Mei

2015-01-01

In this study, we performed a network meta-analysis to compare the outcomes of seven most common surgical procedures to fix DRF, including bridging external fixation, non-bridging external fixation, K-wire fixation, plaster fixation, dorsal plating, volar plating, and dorsal and volar plating. Published studies were retrieved through PubMed, Embase and Cochrane Library databases. The database search terms used were the following keywords and MeSH terms: DRF, bridging external fixation, non-bridging external fixation, K-wire fixation, plaster fixation, dorsal plating, volar plating, and dorsal and volar plating. The network meta-analysis was performed to rank the probabilities of postoperative complication risks for the seven surgical modalities in DRF patients. This network meta-analysis included data obtained from a total of 19 RCTs. Our results revealed that compared to DRF patients treated with bridging external fixation, marked differences in pin-track infection (PTI) rate were found in patients treated with plaster fixation, volar plating, and dorsal and volar plating. Cluster analysis showed that plaster fixation is associated with the lowest probability of postoperative complication in DRF patients. Plaster fixation is associated with the lowest risk for postoperative complications in DRF patients, when compared to six other common DRF surgical methods examined. PMID:26549312

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

An empirical Bayes approach to network recovery using external knowledge.

PubMed

Kpogbezan, Gino B; van der Vaart, Aad W; van Wieringen, Wessel N; Leday, Gwenaël G R; van de Wiel, Mark A

2017-09-01

Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Food and drug cues activate similar brain regions: a meta-analysis of functional MRI studies.

PubMed

Tang, D W; Fellows, L K; Small, D M; Dagher, A

2012-06-06

In healthy individuals, food cues can trigger hunger and feeding behavior. Likewise, smoking cues can trigger craving and relapse in smokers. Brain imaging studies report that structures involved in appetitive behaviors and reward, notably the insula, striatum, amygdala and orbital frontal cortex, tend to be activated by both visual food and smoking cues. Here, by carrying out a meta-analysis of human neuro-imaging studies, we investigate the neural network activated by: 1) food versus neutral cues (14 studies, 142 foci) 2) smoking versus neutral cues (15 studies, 176 foci) 3) smoking versus neutral cues when correlated with craving scores (7 studies, 108 foci). PubMed was used to identify cue-reactivity imaging studies that compared brain response to visual food or smoking cues to neutral cues. Fourteen articles were identified for the food meta-analysis and fifteen articles were identified for the smoking meta-analysis. Six articles were identified for the smoking cue correlated with craving analysis. Meta-analyses were carried out using activation likelihood estimation. Food cues were associated with increased blood oxygen level dependent (BOLD) response in the left amygdala, bilateral insula, bilateral orbital frontal cortex, and striatum. Smoking cues were associated with increased BOLD signal in the same areas, with the exception of the insula. However, the smoking meta-analysis of brain maps correlating cue-reactivity with subjective craving did identify the insula, suggesting that insula activation is only found when craving levels are high. The brain areas identified here are involved in learning, memory and motivation, and their cue-induced activity is an index of the incentive salience of the cues. Using meta-analytic techniques to combine a series of studies, we found that food and smoking cues activate comparable brain networks. There is significant overlap in brain regions responding to conditioned cues associated with natural and drug rewards. Copyright © 2012 Elsevier Inc. All rights reserved.

Gender differences in working memory networks: A BrainMap meta-analysis

PubMed Central

Hill, Ashley C.; Laird, Angela R.; Robinson, Jennifer L.

2014-01-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigation using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. PMID:25042764

Gender differences in working memory networks: a BrainMap meta-analysis.

PubMed

Hill, Ashley C; Laird, Angela R; Robinson, Jennifer L

2014-10-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigations using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. Copyright © 2014 Elsevier B.V. All rights reserved.

Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

DTIC Science & Technology

2014-10-02

intervals (Neil, Tailor, Marquez, Fenton , & Hear, 2007). This is cumbersome, error prone and usually inaccurate. Even though a universal framework...Science. Neil, M., Tailor, M., Marquez, D., Fenton , N., & Hear. (2007). Inference in Bayesian networks using dynamic discretisation. Statistics

A Bayesian network approach for causal inferences in pesticide risk assessment and management

EPA Science Inventory

Pesticide risk assessment and management must balance societal benefits and ecosystem protection, based on quantified risks and the strength of the causal linkages between uses of the pesticide and socioeconomic and ecological endpoints of concern. A Bayesian network (BN) is a gr...

Comparison of effectiveness and adverse effects of gefitinib, erlotinib and icotinib among patients with non-small cell lung cancer: A network meta-analysis.

PubMed

Liu, Yuanyuan; Zhang, Yu; Feng, Gangling; Niu, Qiang; Xu, Shangzhi; Yan, Yizhong; Li, Shugang; Jing, Mingxia

2017-11-01

The present network meta-analysis aimed to compare the effectiveness and adverse effects of gefitinib, erlotinib and icotinib in the treatment of patients with non-small cell lung cancer (NSCLC). Two reviewers searched the Cochrane, PubMed, Embase, ScienceDirect, China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals and Wanfang databases for relevant studies. Studies were then screened and evaluated, and data was extracted. End-points evaluated for NSCLC included complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), overall response rate (ORR), disease control rate (DCR), progression-free survival (PFS), median survival time (MST) and adverse effects, including rash, diarrhea, nausea and vomiting, fatigue and abnormal liver function. For the analysis of incorporated studies, RevMan, SPSS, R and Stata software were used. A total of 43 studies with 7,168 patients were included in the network meta-analysis. No significant differences were observed in CR, PR, SD, PD, ORR or DCR between gefitinib, erlotinib and icotinib by using network meta analysis. Compared with gefitinib, erlotinib resulted in a higher rate of nausea and vomiting [adjusted odds ratio (OR)=2.0; 95% credible interval, 1.1-3.7]. However, no significant differences were observed in the rates of rash, diarrhea, fatigue or abnormal liver function using network meta-analysis. Compared with erlotinib, gefitinib resulted in a lower SD rate [OR=0.86; 95% confidence interval (CI): 0.75-0.99; P=0.04], and lower rates of rash (OR=0.45; 95% CI, 0.36-0.55; P<0.00001), diarrhea (OR=0.75; 95% CI, 0.61-0.92; P=0.005), nausea and vomiting (OR=0.47; 95% CI, 0.27-0.84; P=0.01) and fatigue (OR=0.43; 95% CI, 0.24-0.76; P=0.004) through meta-analysis of two congruent drugs. However, gefitinib resulted in a higher rate of rash compared with icotinib (OR=1.57; 95% CI, 1.18-2.09; P=0.002). Otherwise, no significant differences were observed in CR, PR, PD, ORR, DCR and abnormal liver function between gefitinib, erlotinib and icotinib through meta-analysis of two congruent drugs. The PFS rate for gefitinib, erlotinib and icotinib was 5.48, 5.15 and 5.81 months, respectively. The MST was 13.26, 13.52, 12.58 months for gefitinib, erlotinib and icotinib, respectively. Gefitinib and icotinib resulted in significantly higher PFS rates compared with erlotinib (P<0.05). Erlotinib resulted in a significantly longer MST compared with gefitinib and icotinib (P<0.05). In conclusion, gefitinib, erlotinib and icotinib had similar effectiveness for the treatment of patients with advanced NSCLC. However, gefitinib resulted in a lower frequency of fatigue, and nausea and vomiting, compared with the other two drugs. Icotinib resulted in a lower frequency of rash. Erlotinib resulted in a longer MST, but was also associated with a higher frequency of rash, and nausea and vomiting.

Comparison of effectiveness and adverse effects of gefitinib, erlotinib and icotinib among patients with non-small cell lung cancer: A network meta-analysis

PubMed Central

Liu, Yuanyuan; Zhang, Yu; Feng, Gangling; Niu, Qiang; Xu, Shangzhi; Yan, Yizhong; Li, Shugang; Jing, Mingxia

2017-01-01

The present network meta-analysis aimed to compare the effectiveness and adverse effects of gefitinib, erlotinib and icotinib in the treatment of patients with non-small cell lung cancer (NSCLC). Two reviewers searched the Cochrane, PubMed, Embase, ScienceDirect, China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals and Wanfang databases for relevant studies. Studies were then screened and evaluated, and data was extracted. End-points evaluated for NSCLC included complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), overall response rate (ORR), disease control rate (DCR), progression-free survival (PFS), median survival time (MST) and adverse effects, including rash, diarrhea, nausea and vomiting, fatigue and abnormal liver function. For the analysis of incorporated studies, RevMan, SPSS, R and Stata software were used. A total of 43 studies with 7,168 patients were included in the network meta-analysis. No significant differences were observed in CR, PR, SD, PD, ORR or DCR between gefitinib, erlotinib and icotinib by using network meta analysis. Compared with gefitinib, erlotinib resulted in a higher rate of nausea and vomiting [adjusted odds ratio (OR)=2.0; 95% credible interval, 1.1–3.7]. However, no significant differences were observed in the rates of rash, diarrhea, fatigue or abnormal liver function using network meta-analysis. Compared with erlotinib, gefitinib resulted in a lower SD rate [OR=0.86; 95% confidence interval (CI): 0.75–0.99; P=0.04], and lower rates of rash (OR=0.45; 95% CI, 0.36–0.55; P<0.00001), diarrhea (OR=0.75; 95% CI, 0.61–0.92; P=0.005), nausea and vomiting (OR=0.47; 95% CI, 0.27–0.84; P=0.01) and fatigue (OR=0.43; 95% CI, 0.24–0.76; P=0.004) through meta-analysis of two congruent drugs. However, gefitinib resulted in a higher rate of rash compared with icotinib (OR=1.57; 95% CI, 1.18–2.09; P=0.002). Otherwise, no significant differences were observed in CR, PR, PD, ORR, DCR and abnormal liver function between gefitinib, erlotinib and icotinib through meta-analysis of two congruent drugs. The PFS rate for gefitinib, erlotinib and icotinib was 5.48, 5.15 and 5.81 months, respectively. The MST was 13.26, 13.52, 12.58 months for gefitinib, erlotinib and icotinib, respectively. Gefitinib and icotinib resulted in significantly higher PFS rates compared with erlotinib (P<0.05). Erlotinib resulted in a significantly longer MST compared with gefitinib and icotinib (P<0.05). In conclusion, gefitinib, erlotinib and icotinib had similar effectiveness for the treatment of patients with advanced NSCLC. However, gefitinib resulted in a lower frequency of fatigue, and nausea and vomiting, compared with the other two drugs. Icotinib resulted in a lower frequency of rash. Erlotinib resulted in a longer MST, but was also associated with a higher frequency of rash, and nausea and vomiting. PMID:29104622

Disaster Response on September 11, 2001 Through the Lens of Statistical Network Analysis.

PubMed

Schweinberger, Michael; Petrescu-Prahova, Miruna; Vu, Duy Quang

2014-05-01

The rescue and relief operations triggered by the September 11, 2001 attacks on the World Trade Center in New York City demanded collaboration among hundreds of organisations. To shed light on the response to the September 11, 2001 attacks and help to plan and prepare the response to future disasters, we study the inter-organisational network that emerged in response to the attacks. Studying the inter-organisational network can help to shed light on (1) whether some organisations dominated the inter-organisational network and facilitated communication and coordination of the disaster response; (2) whether the dominating organisations were supposed to coordinate disaster response or emerged as coordinators in the wake of the disaster; and (3) the degree of network redundancy and sensitivity of the inter-organisational network to disturbances following the initial disaster. We introduce a Bayesian framework which can answer the substantive questions of interest while being as simple and parsimonious as possible. The framework allows organisations to have varying propensities to collaborate, while taking covariates into account, and allows to assess whether the inter-organisational network had network redundancy-in the form of transitivity-by using a test which may be regarded as a Bayesian score test. We discuss implications in terms of disaster management.

A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

NASA Technical Reports Server (NTRS)

Lee, S. Daniel

1990-01-01

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control.

Metformin for the prevention of hypertensive disorders of pregnancy in women with gestational diabetes and obesity: a systematic review and meta-analysis.

PubMed

Kalafat, Erkan; Sukur, Yavuz Emre; Abdi, Abdulkadir; Thilaganathan, Basky; Khalil, Asma

2018-05-10

Metformin has been reported to reduce the risk of preeclampsia. It is also known to influence soluble fms-like tyrosine kinase-1 (sFlt-1) levels, which correlate significantly with the gestation of onset and severity of preeclampsia. The main aim of this systematic review and meta-analysis was to determine whether metformin use is associated with the incidence of hypertensive disorders of pregnancy (HDP). MEDLINE (1947 - September 2017), Scopus (1970 - September 2017) and the Cochrane Library (since inception - September 2017) were searched for relevant citations in English language. Randomized controlled trials on metformin use, reporting the incidence of preeclampsia or pregnancy induced hypertension were included. Studies on populations with a high probability of metformin use prior to randomization (type II diabetes or polycystic ovary syndrome) were excluded. Random-effects models with Mantel-Haenszel were used for subgroup analyses. Moreover, a Bayesian random-effects meta-regression was used to synthesize the evidence. In total, 3337 citations matched the search criteria. After evaluating the abstracts and full text review, 15 studies were included in the review. Metformin use was associated with a reduced risk of pregnancy induced hypertension when compared to insulin (RR: 0.56, 95% CI: 0.37-0.85, I^ 2 =0, 1260 women) and a non-significantly reduced risk of preeclampsia (RR: 0.83, 95% CI: 0.60-1.14, I^ 2 =0%, 1724 women). When compared to placebo, metformin use was associated with a non-significant reduction of preeclampsia (RR: 0.74, 95% CI: 0.09-6.28, I^ 2 =86%, 840 women). Metformin use was also associated with a non-significant reduction of any HDP (RR: 0.71, 95% CI: 0.41-1.25, I^ 2 =0, 556 women) when compared to glyburide. When studies were combined with Bayesian random-effects meta-regression using treatment type as a covariate, the posterior probabilities of metformin having a beneficial effect for the prevention of preeclampsia, pregnancy induced hypertension and any HDP were 92.7%, 92.8% and 99.2%, respectively when compared to any other treatment or placebo. There is a high probability that metformin use is associated with a reduced HDP incidence when compared to other treatments and placebo. The small number of studies included in the analysis, the low quality of evidence and the clinical heterogeneity preclude the generalization of these results to broader populations. Given the clinical importance of this topic and the magnitude of effect observed in this meta-analysis, further prospective trials are urgently needed. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis.

PubMed

Fouragnan, Elsa; Retzler, Chris; Philiastides, Marios G

2018-03-25

Learning occurs when an outcome differs from expectations, generating a reward prediction error signal (RPE). The RPE signal has been hypothesized to simultaneously embody the valence of an outcome (better or worse than expected) and its surprise (how far from expectations). Nonetheless, growing evidence suggests that separate representations of the two RPE components exist in the human brain. Meta-analyses provide an opportunity to test this hypothesis and directly probe the extent to which the valence and surprise of the error signal are encoded in separate or overlapping networks. We carried out several meta-analyses on a large set of fMRI studies investigating the neural basis of RPE, locked at decision outcome. We identified two valence learning systems by pooling studies searching for differential neural activity in response to categorical positive-versus-negative outcomes. The first valence network (negative > positive) involved areas regulating alertness and switching behaviours such as the midcingulate cortex, the thalamus and the dorsolateral prefrontal cortex whereas the second valence network (positive > negative) encompassed regions of the human reward circuitry such as the ventral striatum and the ventromedial prefrontal cortex. We also found evidence of a largely distinct surprise-encoding network including the anterior cingulate cortex, anterior insula and dorsal striatum. Together with recent animal and electrophysiological evidence this meta-analysis points to a sequential and distributed encoding of different components of the RPE signal, with potentially distinct functional roles. © 2018 Wiley Periodicals, Inc.

Modelling neural correlates of working memory: A coordinate-based meta-analysis

PubMed Central

Rottschy, C.; Langner, R.; Dogan, I.; Reetz, K.; Laird, A.R.; Schulz, J.B.; Fox, P.T.; Eickhoff, S.B.

2011-01-01

Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca’s region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task-set and load effects. Nevertheless, a consistent but more restricted “core” network emerged from conjunctions across analyses of specific task designs and contrasts. This “core” network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focusing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations. PMID:22178808