Comparative phylogeography highlights the double-edged sword of climate change faced by arctic- and alpine-adapted mammals.

PubMed

Lanier, Hayley C; Gunderson, Aren M; Weksler, Marcelo; Fedorov, Vadim B; Olson, Link E

2015-01-01

Recent studies suggest that alpine and arctic organisms may have distinctly different phylogeographic histories from temperate or tropical taxa, with recent range contraction into interglacial refugia as opposed to post-glacial expansion out of refugia. We use a combination of phylogeographic inference, demographic reconstructions, and hierarchical Approximate Bayesian Computation to test for phylodemographic concordance among five species of alpine-adapted small mammals in eastern Beringia. These species (Collared Pikas, Hoary Marmots, Brown Lemmings, Arctic Ground Squirrels, and Singing Voles) vary in specificity to alpine and boreal-tundra habitat but share commonalities (e.g., cold tolerance and nunatak survival) that might result in concordant responses to Pleistocene glaciations. All five species contain a similar phylogeographic disjunction separating eastern and Beringian lineages, which we show to be the result of simultaneous divergence. Genetic diversity is similar within each haplogroup for each species, and there is no support for a post-Pleistocene population expansion in eastern lineages relative to those from Beringia. Bayesian skyline plots for four of the five species do not support Pleistocene population contraction. Brown Lemmings show evidence of late Quaternary demographic expansion without subsequent population decline. The Wrangell-St. Elias region of eastern Alaska appears to be an important zone of recent secondary contact for nearctic alpine mammals. Despite differences in natural history and ecology, similar phylogeographic histories are supported for all species, suggesting that these, and likely other, alpine- and arctic-adapted taxa are already experiencing population and/or range declines that are likely to synergistically accelerate in the face of rapid climate change. Climate change may therefore be acting as a double-edged sword that erodes genetic diversity within populations but promotes divergence and the generation of biodiversity.

Comparative Phylogeography Highlights the Double-Edged Sword of Climate Change Faced by Arctic- and Alpine-Adapted Mammals

PubMed Central

Lanier, Hayley C.; Gunderson, Aren M.; Weksler, Marcelo; Fedorov, Vadim B.; Olson, Link E.

2015-01-01

Recent studies suggest that alpine and arctic organisms may have distinctly different phylogeographic histories from temperate or tropical taxa, with recent range contraction into interglacial refugia as opposed to post-glacial expansion out of refugia. We use a combination of phylogeographic inference, demographic reconstructions, and hierarchical Approximate Bayesian Computation to test for phylodemographic concordance among five species of alpine-adapted small mammals in eastern Beringia. These species (Collared Pikas, Hoary Marmots, Brown Lemmings, Arctic Ground Squirrels, and Singing Voles) vary in specificity to alpine and boreal-tundra habitat but share commonalities (e.g., cold tolerance and nunatak survival) that might result in concordant responses to Pleistocene glaciations. All five species contain a similar phylogeographic disjunction separating eastern and Beringian lineages, which we show to be the result of simultaneous divergence. Genetic diversity is similar within each haplogroup for each species, and there is no support for a post-Pleistocene population expansion in eastern lineages relative to those from Beringia. Bayesian skyline plots for four of the five species do not support Pleistocene population contraction. Brown Lemmings show evidence of late Quaternary demographic expansion without subsequent population decline. The Wrangell-St. Elias region of eastern Alaska appears to be an important zone of recent secondary contact for nearctic alpine mammals. Despite differences in natural history and ecology, similar phylogeographic histories are supported for all species, suggesting that these, and likely other, alpine- and arctic-adapted taxa are already experiencing population and/or range declines that are likely to synergistically accelerate in the face of rapid climate change. Climate change may therefore be acting as a double-edged sword that erodes genetic diversity within populations but promotes divergence and the generation of biodiversity. PMID:25734275

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

ERIC Educational Resources Information Center

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A. G.

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are…

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

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.

Is probabilistic bias analysis approximately Bayesian?

PubMed Central

MacLehose, Richard F.; Gustafson, Paul

2011-01-01

Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. While the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well. PMID:22157311

Bayesian Mediation Analysis

ERIC Educational Resources Information Center

Yuan, Ying; MacKinnon, David P.

2009-01-01

In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

Specialist and generalist symbionts show counterintuitive levels of genetic diversity and discordant demographic histories along the Florida Reef Tract

NASA Astrophysics Data System (ADS)

Titus, Benjamin M.; Daly, Marymegan

2017-03-01

Specialist and generalist life histories are expected to result in contrasting levels of genetic diversity at the population level, and symbioses are expected to lead to patterns that reflect a shared biogeographic history and co-diversification. We test these assumptions using mtDNA sequencing and a comparative phylogeographic approach for six co-occurring crustacean species that are symbiotic with sea anemones on western Atlantic coral reefs, yet vary in their host specificities: four are host specialists and two are host generalists. We first conducted species discovery analyses to delimit cryptic lineages, followed by classic population genetic diversity analyses for each delimited taxon, and then reconstructed the demographic history for each taxon using traditional summary statistics, Bayesian skyline plots, and approximate Bayesian computation to test for signatures of recent and concerted population expansion. The genetic diversity values recovered here contravene the expectations of the specialist-generalist variation hypothesis and classic population genetics theory; all specialist lineages had greater genetic diversity than generalists. Demography suggests recent population expansions in all taxa, although Bayesian skyline plots and approximate Bayesian computation suggest the timing and magnitude of these events were idiosyncratic. These results do not meet the a priori expectation of concordance among symbiotic taxa and suggest that intrinsic aspects of species biology may contribute more to phylogeographic history than extrinsic forces that shape whole communities. The recovery of two cryptic specialist lineages adds an additional layer of biodiversity to this symbiosis and contributes to an emerging pattern of cryptic speciation in the specialist taxa. Our results underscore the differences in the evolutionary processes acting on marine systems from the terrestrial processes that often drive theory. Finally, we continue to highlight the Florida Reef Tract as an important biodiversity hotspot.

Comparison of 3 estimation methods of mycophenolic acid AUC based on a limited sampling strategy in renal transplant patients.

PubMed

Hulin, Anne; Blanchet, Benoît; Audard, Vincent; Barau, Caroline; Furlan, Valérie; Durrbach, Antoine; Taïeb, Fabrice; Lang, Philippe; Grimbert, Philippe; Tod, Michel

2009-04-01

A significant relationship between mycophenolic acid (MPA) area under the plasma concentration-time curve (AUC) and the risk for rejection has been reported. Based on 3 concentration measurements, 3 approaches have been proposed for the estimation of MPA AUC, involving either a multilinear regression approach model (MLRA) or a Bayesian estimation using either gamma absorption or zero-order absorption population models. The aim of the study was to compare the 3 approaches for the estimation of MPA AUC in 150 renal transplant patients treated with mycophenolate mofetil and tacrolimus. The population parameters were determined in 77 patients (learning study). The AUC estimation methods were compared in the learning population and in 73 patients from another center (validation study). In the latter study, the reference AUCs were estimated by the trapezoidal rule on 8 measurements. MPA concentrations were measured by liquid chromatography. The gamma absorption model gave the best fit. In the learning study, the AUCs estimated by both Bayesian methods were very similar, whereas the multilinear approach was highly correlated but yielded estimates about 20% lower than Bayesian methods. This resulted in dosing recommendations differing by 250 mg/12 h or more in 27% of cases. In the validation study, AUC estimates based on the Bayesian method with gamma absorption model and multilinear regression approach model were, respectively, 12% higher and 7% lower than the reference values. To conclude, the bicompartmental model with gamma absorption rate gave the best fit. The 3 AUC estimation methods are highly correlated but not concordant. For a given patient, the same estimation method should always be used.

The role of SISCOM in preoperative evaluation for patients with epilepsy surgery: A meta-analysis.

PubMed

Chen, Tong; Guo, Liang

2016-10-01

To assess the specific value of subtraction ictal and inter-ictal SPECT co-registered to MRI (SISCOM) in identifying the epileptogenic zone (EZ) and predicting postoperative outcomes in epileptic surgical patients. A meta-analysis of studies published from January 1995 to June 2015 was conducted through a comprehensive literature search, and 11 studies were included. R software was first used to calculate a pooled positive rate, concordant rate and positive predictive value (PPV) for good outcomes. Stata software was then used to explore the relationship between SISCOM localization and surgical outcomes, including a subgroup analysis for extra-temporal lobe epilepsy. The unweighted positive and concordant rates of SISCOM were 85.9% and 65.3%, respectively. In 142 MRI-negative patients, the SISCOM positive rate was 83.8%. The pooled PPV of 178 surgical patients with concordant SISCOM was 56%. In the meta-analysis of 275 surgical patients, the seizure-free odds ratio was 3.28-times higher in concordant than in non-concordant SISCOM patients [95%CI (1.90, 5.67)]. For extra-temporal lobe epilepsy, the seizure-free odds ratio was 2.44-times higher in concordant than in non-concordant SISCOM patients [95%CI (1.34, 4.43)]. Our data indicate that SISCOM has moderate sensitivity in localizing the epileptogenic zone and can provide complementary information when MRI is negative. Furthermore, SISCOM localization concordant with the gold standard demonstrates slightly higher predictive value for good surgical outcomes. Further research is required to explore the influence of SISCOM localization results in temporal lobe versus extra-temporal lobe epilepsy. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Prior approval: the growth of Bayesian methods in psychology.

PubMed

Andrews, Mark; Baguley, Thom

2013-02-01

Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

Bayesian Model Averaging for Propensity Score Analysis

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2013-01-01

The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…

Bayesian analyses of time-interval data for environmental radiation monitoring.

PubMed

Luo, Peng; Sharp, Julia L; DeVol, Timothy A

2013-01-01

Time-interval (time difference between two consecutive pulses) analysis based on the principles of Bayesian inference was investigated for online radiation monitoring. Using experimental and simulated data, Bayesian analysis of time-interval data [Bayesian (ti)] was compared with Bayesian and a conventional frequentist analysis of counts in a fixed count time [Bayesian (cnt) and single interval test (SIT), respectively]. The performances of the three methods were compared in terms of average run length (ARL) and detection probability for several simulated detection scenarios. Experimental data were acquired with a DGF-4C system in list mode. Simulated data were obtained using Monte Carlo techniques to obtain a random sampling of the Poisson distribution. All statistical algorithms were developed using the R Project for statistical computing. Bayesian analysis of time-interval information provided a similar detection probability as Bayesian analysis of count information, but the authors were able to make a decision with fewer pulses at relatively higher radiation levels. In addition, for the cases with very short presence of the source (< count time), time-interval information is more sensitive to detect a change than count information since the source data is averaged by the background data over the entire count time. The relationships of the source time, change points, and modifications to the Bayesian approach for increasing detection probability are presented.

First Evidence of Running Cosmic Vacuum: Challenging the Concordance Model

NASA Astrophysics Data System (ADS)

Solà, Joan; Gómez-Valent, Adrià; de Cruz Pérez, Javier

2017-02-01

Despite the fact that a rigid {{Λ }}-term is a fundamental building block of the concordance ΛCDM model, we show that a large class of cosmological scenarios with dynamical vacuum energy density {ρ }{{Λ }} together with a dynamical gravitational coupling G or a possible non-conservation of matter, are capable of seriously challenging the traditional phenomenological success of the ΛCDM. In this paper, we discuss these “running vacuum models” (RVMs), in which {ρ }{{Λ }}={ρ }{{Λ }}(H) consists of a nonvanishing constant term and a series of powers of the Hubble rate. Such generic structure is potentially linked to the quantum field theoretical description of the expanding universe. By performing an overall fit to the cosmological observables SN Ia+BAO+H(z)+LSS+BBN+CMB (in which the WMAP9, Planck 2013, and Planck 2015 data are taken into account), we find that the class of RVMs appears significantly more favored than the ΛCDM, namely, at an unprecedented level of ≳ 4.2σ . Furthermore, the Akaike and Bayesian information criteria confirm that the dynamical RVMs are strongly preferred compared to the conventional rigid {{Λ }}-picture of the cosmic evolution.

Genomic regions with a history of divergent selection affect fitness of hybrids between two butterfly species.

PubMed

Gompert, Zachariah; Lucas, Lauren K; Nice, Chris C; Fordyce, James A; Forister, Matthew L; Buerkle, C Alex

2012-07-01

Speciation is the process by which reproductively isolated lineages arise, and is one of the fundamental means by which the diversity of life increases. Whereas numerous studies have documented an association between ecological divergence and reproductive isolation, relatively little is known about the role of natural selection in genome divergence during the process of speciation. Here, we use genome-wide DNA sequences and Bayesian models to test the hypothesis that loci under divergent selection between two butterfly species (Lycaeides idas and L. melissa) also affect fitness in an admixed population. Locus-specific measures of genetic differentiation between L. idas and L. melissa and genomic introgression in hybrids varied across the genome. The most differentiated genetic regions were characterized by elevated L. idas ancestry in the admixed population, which occurs in L. idas-like habitat, consistent with the hypothesis that local adaptation contributes to speciation. Moreover, locus-specific measures of genetic differentiation (a metric of divergent selection) were positively associated with extreme genomic introgression (a metric of hybrid fitness). Interestingly, concordance of differentiation and introgression was only partial. We discuss multiple, complementary explanations for this partial concordance. © 2012 The Author(s).

A SAS Interface for Bayesian Analysis with WinBUGS

ERIC Educational Resources Information Center

Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki

2008-01-01

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

PubMed Central

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B; Neyer, Franz J; van Aken, Marcel AG

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided. PMID:24116396

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

Joint line tenderness and McMurray tests for the detection of meniscal lesions: what is their real diagnostic value?

PubMed

Galli, Marco; Ciriello, Vincenzo; Menghi, Amerigo; Aulisa, Angelo G; Rabini, Alessia; Marzetti, Emanuele

2013-06-01

To assess the interobserver concordance of the joint line tenderness (JLT) and McMurray tests, and to determine their diagnostic efficiency for the detection of meniscal lesions. Prospective observational study. Orthopedics outpatient clinic, university hospital. Patients (N=60) with suspected nonacute meniscal lesions who underwent knee arthroscopy. Not applicable. Patients were examined by 3 independent observers with graded levels of experience (>10y, 3y, and 4mo of practice). The interobserver concordance was assessed by Cohen-Fleiss κ statistics. Accuracy, negative and positive predictive values for prevalence 10% to 90%, positive (LR+) and negative (LR-) likelihood ratios, and the Bayesian posttest probability with a positive or negative result were also determined. The diagnostic value of the 2 tests combined was assessed by logistic regression. Arthroscopy was used as the reference test. No interobserver concordance was determined for the JLT. The McMurray test showed higher interobserver concordance, which improved when judgments by the less experienced examiner were discarded. The whole series studied by the "best" examiner (experienced orthopedist) provided the following values: (1) JLT: sensitivity, 62.9%; specificity, 50%; LR+, 1.26; LR-, .74; (2) McMurray: sensitivity, 34.3%; specificity, 86.4%; LR+, 2.52; LR-, .76. The combination of the 2 tests did not offer advantages over the McMurray alone. The JLT alone is of little clinical usefulness. A negative McMurray test does not modify the pretest probability of a meniscal lesion, while a positive result has a fair predictive value. Hence, in a patient with a suspected meniscal lesion, a positive McMurray test indicates that arthroscopy should be performed. In case of a negative result, further examinations, including imaging, are needed. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

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.

Using automatically extracted information from mammography reports for decision-support

PubMed Central

Bozkurt, Selen; Gimenez, Francisco; Burnside, Elizabeth S.; Gulkesen, Kemal H.; Rubin, Daniel L.

2016-01-01

Objective To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. Materials and methods We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect (“reference standard”) structured inputs to the DSS (“RS-DSS”) vs NLP-derived inputs (“NLP-DSS”) on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. Results The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004 ± 0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. Conclusion The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions. PMID:27388877

The Application of Bayesian Analysis to Issues in Developmental Research

ERIC Educational Resources Information Center

Walker, Lawrence J.; Gustafson, Paul; Frimer, Jeremy A.

2007-01-01

This article reviews the concepts and methods of Bayesian statistical analysis, which can offer innovative and powerful solutions to some challenging analytical problems that characterize developmental research. In this article, we demonstrate the utility of Bayesian analysis, explain its unique adeptness in some circumstances, address some…

A default Bayesian hypothesis test for mediation.

PubMed

Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

Concordance of Beta-papillomavirus across anogenital and oral anatomic sites of men: The HIM Study.

PubMed

Nunes, Emily M; López, Rossana V M; Sudenga, Staci L; Gheit, Tarik; Tommasino, Massimo; Baggio, Maria L; Ferreira, Silvaneide; Galan, Lenice; Silva, Roberto C; Lazcano-Ponce, Eduardo; Giuliano, Anna R; Villa, Luisa L; Sichero, Laura

2017-10-01

We evaluated the concordance between β-HPVs detected in external genital skin, anal canal, and oral cavity specimens collected simultaneously from 717 men that were participating in the multinational HIM Study. Viral genotyping was performed using the Luminex technology. Species- and type-specific concordance was measured using kappa statistics for agreement. Overall, concordance of β-HPVs across sites was low and mainly observed among paired genital/anal canal samples. When grouped by species, solely β-4 HPVs showed moderate concordance in genital/anal pairs (κ = 0.457), which could be attributed to the substantial concordance of HPV-92 in men from Brazil and Mexico (κ > 0.610). β-HPV type concordance was higher in Mexico, where HPV-19 was consistently concordant in all anatomic site combinations. Our analysis indicates that type-specific concordance across sites is limited to few viral types; however, these infections seem to occur more often than would be expected by chance, suggesting that although rare, there is agreement among sites. Copyright © 2017 Elsevier Inc. All rights reserved.

Investigating spousal concordance of diabetes through statistical analysis and data mining.

PubMed

Wang, Jong-Yi; Liu, Chiu-Shong; Lung, Chi-Hsuan; Yang, Ya-Tun; Lin, Ming-Hung

2017-01-01

Spousal clustering of diabetes merits attention. Whether old-age vulnerability or a shared family environment determines the concordance of diabetes is also uncertain. This study investigated the spousal concordance of diabetes and compared the risk of diabetes concordance between couples and noncouples by using nationally representative data. A total of 22,572 individuals identified from the 2002-2013 National Health Insurance Research Database of Taiwan constituted 5,643 couples and 5,643 noncouples through 1:1 dual propensity score matching (PSM). Factors associated with concordance in both spouses with diabetes were analyzed at the individual level. The risk of diabetes concordance between couples and noncouples was compared at the couple level. Logistic regression was the main statistical method. Statistical data were analyzed using SAS 9.4. C&RT and Apriori of data mining conducted in IBM SPSS Modeler 13 served as a supplement to statistics. High odds of the spousal concordance of diabetes were associated with old age, middle levels of urbanization, and high comorbidities (all P < 0.05). The dual PSM analysis revealed that the risk of diabetes concordance was significantly higher in couples (5.19%) than in noncouples (0.09%; OR = 61.743, P < 0.0001). A high concordance rate of diabetes in couples may indicate the influences of assortative mating and shared environment. Diabetes in a spouse implicates its risk in the partner. Family-based diabetes care that emphasizes the screening of couples at risk of diabetes by using the identified risk factors is suggested in prospective clinical practice interventions.

Investigating spousal concordance of diabetes through statistical analysis and data mining

PubMed Central

Liu, Chiu-Shong; Lung, Chi-Hsuan; Yang, Ya-Tun; Lin, Ming-Hung

2017-01-01

Objective Spousal clustering of diabetes merits attention. Whether old-age vulnerability or a shared family environment determines the concordance of diabetes is also uncertain. This study investigated the spousal concordance of diabetes and compared the risk of diabetes concordance between couples and noncouples by using nationally representative data. Methods A total of 22,572 individuals identified from the 2002–2013 National Health Insurance Research Database of Taiwan constituted 5,643 couples and 5,643 noncouples through 1:1 dual propensity score matching (PSM). Factors associated with concordance in both spouses with diabetes were analyzed at the individual level. The risk of diabetes concordance between couples and noncouples was compared at the couple level. Logistic regression was the main statistical method. Statistical data were analyzed using SAS 9.4. C&RT and Apriori of data mining conducted in IBM SPSS Modeler 13 served as a supplement to statistics. Results High odds of the spousal concordance of diabetes were associated with old age, middle levels of urbanization, and high comorbidities (all P < 0.05). The dual PSM analysis revealed that the risk of diabetes concordance was significantly higher in couples (5.19%) than in noncouples (0.09%; OR = 61.743, P < 0.0001). Conclusions A high concordance rate of diabetes in couples may indicate the influences of assortative mating and shared environment. Diabetes in a spouse implicates its risk in the partner. Family-based diabetes care that emphasizes the screening of couples at risk of diabetes by using the identified risk factors is suggested in prospective clinical practice interventions. PMID:28817654

Estimating the concordance probability in a survival analysis with a discrete number of risk groups.

PubMed

Heller, Glenn; Mo, Qianxing

2016-04-01

A clinical risk classification system is an important component of a treatment decision algorithm. A measure used to assess the strength of a risk classification system is discrimination, and when the outcome is survival time, the most commonly applied global measure of discrimination is the concordance probability. The concordance probability represents the pairwise probability of lower patient risk given longer survival time. The c-index and the concordance probability estimate have been used to estimate the concordance probability when patient-specific risk scores are continuous. In the current paper, the concordance probability estimate and an inverse probability censoring weighted c-index are modified to account for discrete risk scores. Simulations are generated to assess the finite sample properties of the concordance probability estimate and the weighted c-index. An application of these measures of discriminatory power to a metastatic prostate cancer risk classification system is examined.

Young Learners and Lexical Awareness: Children's Engagement with Wordlists and Concordances

ERIC Educational Resources Information Center

MacGregor, Alex

2014-01-01

Sinclair (1991) found that lexical analysis can be overcomplicated, yet Johns (1994) called for investigation into whether corpus analysis can motivate beginners and near-beginners. The findings of this research suggest that young EFL learners can enjoy using corpus analysis tools (wordlists and concordances) to identify, classify, and generalize…

Estimating synchronous demographic changes across populations using hABC and its application for a herpetological community from northeastern Brazil.

PubMed

Gehara, Marcelo; Garda, Adrian A; Werneck, Fernanda P; Oliveira, Eliana F; da Fonseca, Emanuel M; Camurugi, Felipe; Magalhães, Felipe de M; Lanna, Flávia M; Sites, Jack W; Marques, Ricardo; Silveira-Filho, Ricardo; São Pedro, Vinícius A; Colli, Guarino R; Costa, Gabriel C; Burbrink, Frank T

2017-09-01

Many studies propose that Quaternary climatic cycles contracted and/or expanded the ranges of species and biomes. Strong expansion-contraction dynamics of biomes presume concerted demographic changes of associated fauna. The analysis of temporal concordance of demographic changes can be used to test the influence of Quaternary climate on diversification processes. Hierarchical approximate Bayesian computation (hABC) is a powerful and flexible approach that models genetic data from multiple species, and can be used to estimate the temporal concordance of demographic processes. Using available single-locus data, we can now perform large-scale analyses, both in terms of number of species and geographic scope. Here, we first compared the power of four alternative hABC models for a collection of single-locus data. We found that the model incorporating an a priori hypothesis about the timing of simultaneous demographic change had the best performance. Second, we applied the hABC models to a data set of seven squamate and four amphibian species occurring in the Seasonally Dry Tropical Forests (Caatinga) in northeastern Brazil, which, according to paleoclimatic evidence, experienced an increase in aridity during the Pleistocene. If this increase was important for the diversification of associated xeric-adapted species, simultaneous population expansions should be evident at the community level. We found a strong signal of synchronous population expansion in the Late Pleistocene, supporting the increase of the Caatinga during this time. This expansion likely enhanced the formation of communities adapted to high aridity and seasonality and caused regional extirpation of taxa adapted to wet forest. © 2017 John Wiley & Sons Ltd.

Incorporating Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio in Place of Neutrophil Count and Platelet Count Improves Prognostic Accuracy of the International Metastatic Renal Cell Carcinoma Database Consortium Model

PubMed Central

Chrom, Pawel; Stec, Rafal; Bodnar, Lubomir; Szczylik, Cezary

2018-01-01

Purpose The study investigated whether a replacement of neutrophil count and platelet count by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model would improve its prognostic accuracy. Materials and Methods This retrospective analysis included consecutive patients with metastatic renal cell carcinoma treated with first-line tyrosine kinase inhibitors. The IMDC and modified-IMDC models were compared using: concordance index (CI), bias-corrected concordance index (BCCI), calibration plots, the Grønnesby and Borgan test, Bayesian Information Criterion (BIC), generalized R2, Integrated Discrimination Improvement (IDI), and continuous Net Reclassification Index (cNRI) for individual risk factors and the three risk groups. Results Three hundred and twenty-one patients were eligible for analyses. The modified-IMDC model with NLR value of 3.6 and PLR value of 157 was selected for comparison with the IMDC model. Both models were well calibrated. All other measures favoured the modified-IMDC model over the IMDC model (CI, 0.706 vs. 0.677; BCCI, 0.699 vs. 0.671; BIC, 2,176.2 vs. 2,190.7; generalized R2, 0.238 vs. 0.202; IDI, 0.044; cNRI, 0.279 for individual risk factors; and CI, 0.669 vs. 0.641; BCCI, 0.669 vs. 0.641; BIC, 2,183.2 vs. 2,198.1; generalized R2, 0.163 vs. 0.123; IDI, 0.045; cNRI, 0.165 for the three risk groups). Conclusion Incorporation of NLR and PLR in place of neutrophil count and platelet count improved prognostic accuracy of the IMDC model. These findings require external validation before introducing into clinical practice. PMID:28253564

Incorporating Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio in Place of Neutrophil Count and Platelet Count Improves Prognostic Accuracy of the International Metastatic Renal Cell Carcinoma Database Consortium Model.

PubMed

Chrom, Pawel; Stec, Rafal; Bodnar, Lubomir; Szczylik, Cezary

2018-01-01

The study investigated whether a replacement of neutrophil count and platelet count by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model would improve its prognostic accuracy. This retrospective analysis included consecutive patients with metastatic renal cell carcinoma treated with first-line tyrosine kinase inhibitors. The IMDC and modified-IMDC models were compared using: concordance index (CI), bias-corrected concordance index (BCCI), calibration plots, the Grønnesby and Borgan test, Bayesian Information Criterion (BIC), generalized R 2 , Integrated Discrimination Improvement (IDI), and continuous Net Reclassification Index (cNRI) for individual risk factors and the three risk groups. Three hundred and twenty-one patients were eligible for analyses. The modified-IMDC model with NLR value of 3.6 and PLR value of 157 was selected for comparison with the IMDC model. Both models were well calibrated. All other measures favoured the modified-IMDC model over the IMDC model (CI, 0.706 vs. 0.677; BCCI, 0.699 vs. 0.671; BIC, 2,176.2 vs. 2,190.7; generalized R 2 , 0.238 vs. 0.202; IDI, 0.044; cNRI, 0.279 for individual risk factors; and CI, 0.669 vs. 0.641; BCCI, 0.669 vs. 0.641; BIC, 2,183.2 vs. 2,198.1; generalized R 2 , 0.163 vs. 0.123; IDI, 0.045; cNRI, 0.165 for the three risk groups). Incorporation of NLR and PLR in place of neutrophil count and platelet count improved prognostic accuracy of the IMDC model. These findings require external validation before introducing into clinical practice.

Molecular and phenotypic diversity in Chionactis occipitalis (Western Shovel-nosed Snake), with emphasis on the status of C. o. klauberi (Tucson Shovel-nosed Snake).

USGS Publications Warehouse

Wood, D.A.; Meik, J.M.; Holycross, A.T.; Fisher, R.N.; Vandergast, A.G.

2008-01-01

Chionactis occipitalis (Western Shovel-nosed Snake) is a small colubrid snake inhabiting the arid regions of the Mojave, Sonoran, and Colorado deserts. Morphological assessments of taxonomy currently recognize four subspecies. However, these taxonomic proposals were largely based on weak morphological differentiation and inadequate geographic sampling. Our goal was to explore evolutionary relationships and boundaries among subspecies of C. occipitalis, with particular focus on individuals within the known range of C. o. klauberi (Tucson Shovel-nosed snake). Population sizes and range for C. o. klauberi have declined over the last 25 years due to habitat alteration and loss prompting a petition to list this subspecies as endangered. We examined the phylogeography, population structure, and subspecific taxonomy of C. occipitalis across its geographic range with genetic analysis of 1100 bases of mitochondrial DNA sequence and reanalysis of 14 morphological characters from 1543 museum specimens. We estimated the species gene phylogeny from 81 snakes using Bayesian inference and explored possible factors influencing genetic variation using landscape genetic analyses. Phylogenetic and population genetic analyses reveal genetic isolation and independent evolutionary trajectories for two primary clades. Our data indicate that diversification between these clades has developed as a result of both historical vicariance and environmental isolating mechanisms. Thus these two clades likely comprise 'evolutionary significant units' (ESUs). Neither molecular nor morphological data are concordant with the traditional C. occipitalis subspecies taxonomy. Mitochondrial sequences suggest specimens recognized as C. o. klauberi are embedded in a larger geographic clade whose range has expanded from western Arizona populations, and these data are concordant with clinal longitudinal variation in morphology. ?? 2007 Springer Science+Business Media B.V.

GHEP-ISFG collaborative simulated exercise for DVI/MPI: Lessons learned about large-scale profile database comparisons.

PubMed

Vullo, Carlos M; Romero, Magdalena; Catelli, Laura; Šakić, Mustafa; Saragoni, Victor G; Jimenez Pleguezuelos, María Jose; Romanini, Carola; Anjos Porto, Maria João; Puente Prieto, Jorge; Bofarull Castro, Alicia; Hernandez, Alexis; Farfán, María José; Prieto, Victoria; Alvarez, David; Penacino, Gustavo; Zabalza, Santiago; Hernández Bolaños, Alejandro; Miguel Manterola, Irati; Prieto, Lourdes; Parsons, Thomas

2016-03-01

The GHEP-ISFG Working Group has recognized the importance of assisting DNA laboratories to gain expertise in handling DVI or missing persons identification (MPI) projects which involve the need for large-scale genetic profile comparisons. Eleven laboratories participated in a DNA matching exercise to identify victims from a hypothetical conflict with 193 missing persons. The post mortem database was comprised of 87 skeletal remain profiles from a secondary mass grave displaying a minimal number of 58 individuals with evidence of commingling. The reference database was represented by 286 family reference profiles with diverse pedigrees. The goal of the exercise was to correctly discover re-associations and family matches. The results of direct matching for commingled remains re-associations were correct and fully concordant among all laboratories. However, the kinship analysis for missing persons identifications showed variable results among the participants. There was a group of laboratories with correct, concordant results but nearly half of the others showed discrepant results exhibiting likelihood ratio differences of several degrees of magnitude in some cases. Three main errors were detected: (a) some laboratories did not use the complete reference family genetic data to report the match with the remains, (b) the identity and/or non-identity hypotheses were sometimes wrongly expressed in the likelihood ratio calculations, and (c) many laboratories did not properly evaluate the prior odds for the event. The results suggest that large-scale profile comparisons for DVI or MPI is a challenge for forensic genetics laboratories and the statistical treatment of DNA matching and the Bayesian framework should be better standardized among laboratories. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Genders of patients and clinicians and their effect on shared decision making: a participant-level meta-analysis.

PubMed

Wyatt, Kirk D; Branda, Megan E; Inselman, Jonathan W; Ting, Henry H; Hess, Erik P; Montori, Victor M; LeBlanc, Annie

2014-09-02

Gender differences in communication styles between clinicians and patients have been postulated to impact patient care, but the extent to which the gender dyad structure impacts outcomes in shared decision making remains unclear. Participant-level meta-analysis of 775 clinical encounters within 7 randomized trials where decision aids, shared decision making tools, were used at the point of care. Outcomes analysed include decisional conflict scale scores, satisfaction with the clinical encounter, concordance between stated decision and action taken, and degree of patient engagement by the clinician using the OPTION scale. An estimated minimal important difference was used to determine if nonsignificant results could be explained by low power. We did not find a statistically significant interaction between clinician/patient gender mix and arm for decisional conflict, satisfaction with the clinical encounter or patient engagement. A borderline significant interaction (p = 0.05) was observed for one outcome: concordance between stated decision and action taken, where encounters with female clinician/male patient showed increased concordance in the decision aid arm compared to control (8% more concordant encounters). All other gender dyads showed decreased concordance with decision aid use (6% fewer concordant encounters for same-gender, 16% fewer concordant encounters for male clinician/female patient). In this participant-level meta-analysis of 7 randomized trials, decision aids used at the point of care demonstrated comparable efficacy across gender dyads. Purported barriers to shared decision making based on gender were not detected when tested for a minimum detected difference. ClinicalTrials.gov NCT00888537, NCT01077037, NCT01029288, NCT00388050, NCT00578981, NCT00949611, NCT00217061.

Bayesian structural equation modeling in sport and exercise psychology.

PubMed

Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus

2015-08-01

Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

Concordance correlation for model performance assessment: An example with reference evapotranspiration

USDA-ARS?s Scientific Manuscript database

Procedures for assessing model performance in agronomy are often arbitrary and not always helpful. An omnibus analysis statistic, concordance correlation, is widely known and used in many other sciences. An illustrative example is presented here. The analysis assumes the exact relationship “observat...

Bayesian Statistics for Biological Data: Pedigree Analysis

ERIC Educational Resources Information Center

Stanfield, William D.; Carlton, Matthew A.

2004-01-01

The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.

Ockham's razor and Bayesian analysis. [statistical theory for systems evaluation

NASA Technical Reports Server (NTRS)

Jefferys, William H.; Berger, James O.

1992-01-01

'Ockham's razor', the ad hoc principle enjoining the greatest possible simplicity in theoretical explanations, is presently shown to be justifiable as a consequence of Bayesian inference; Bayesian analysis can, moreover, clarify the nature of the 'simplest' hypothesis consistent with the given data. By choosing the prior probabilities of hypotheses, it becomes possible to quantify the scientific judgment that simpler hypotheses are more likely to be correct. Bayesian analysis also shows that a hypothesis with fewer adjustable parameters intrinsically possesses an enhanced posterior probability, due to the clarity of its predictions.

Retrospective study

PubMed Central

Lu, Chao; Lv, Xueyou; Lin, Yiming; Li, Dejian; Chen, Lihua; Ji, Feng; Li, Youming; Yu, Chaohui

2016-01-01

Abstract Conventional forceps biopsy (CFB) is the most popular way to screen for gastric epithelial neoplasia (GEN) and adenocarcinoma of gastric epithelium. The aim of this study was to compare the diagnostic accuracy between conventional forceps biopsy and endoscopic submucosal dissection (ESD). Four hundred forty-four patients who finally undertook ESD in our hospital were enrolled from Jan 1, 2009 to Sep 1, 2015. We retrospectively assessed the characteristics of pathological results of CFB and ESD. The concordance rate between CFB and ESD specimens was 68.92% (306/444). Men showed a lower concordance rate (63.61% vs 79.33%; P = 0.001) and concordance patients were younger (P = 0.048). In multivariate analysis, men significantly had a lower concordance rate (coefficient −0.730, P = 0.002) and a higher rate of pathological upgrade (coefficient −0.648, P = 0.015). Locations of CFB did not influence the concordance rate statistically. The concordance rate was relatively high in our hospital. According to our analysis, old men plus gastric fundus or antrum of CFB were strongly suggested to perform ESD if precancerous lesions were found. And young women with low-grade intraepithelial neoplasia could select regular follow-up. PMID:27472723

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

NASA Astrophysics Data System (ADS)

Sharma, Sanjib

2017-08-01

Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here.

Power in Bayesian Mediation Analysis for Small Sample Research

PubMed Central

Miočević, Milica; MacKinnon, David P.; Levy, Roy

2018-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results. PMID:29662296

Power in Bayesian Mediation Analysis for Small Sample Research.

PubMed

Miočević, Milica; MacKinnon, David P; Levy, Roy

2017-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs.

PubMed

Schmidt, Amand F; Klugkist, Irene; Klungel, Olaf H; Nielen, Mirjam; de Boer, Anthonius; Hoes, Arno W; Groenwold, Rolf H H

2015-04-01

Findings from nonrandomized studies on safety or efficacy of treatment in patient subgroups may trigger postlaunch randomized clinical trials (RCTs). In the analysis of such RCTs, results from nonrandomized studies are typically ignored. This study explores the trade-off between bias and power of Bayesian RCT analysis incorporating information from nonrandomized studies. A simulation study was conducted to compare frequentist with Bayesian analyses using noninformative and informative priors in their ability to detect interaction effects. In simulated subgroups, the effect of a hypothetical treatment differed between subgroups (odds ratio 1.00 vs. 2.33). Simulations varied in sample size, proportions of the subgroups, and specification of the priors. As expected, the results for the informative Bayesian analyses were more biased than those from the noninformative Bayesian analysis or frequentist analysis. However, because of a reduction in posterior variance, informative Bayesian analyses were generally more powerful to detect an effect. In scenarios where the informative priors were in the opposite direction of the RCT data, type 1 error rates could be 100% and power 0%. Bayesian methods incorporating data from nonrandomized studies can meaningfully increase power of interaction tests in postlaunch RCTs. Copyright © 2015 Elsevier Inc. All rights reserved.

IUCN classification zones concord with, but underestimate, the population genetic structure of the zebra shark Stegostoma fasciatum in the Indo-West Pacific.

PubMed

Dudgeon, C L; Broderick, D; Ovenden, J R

2009-01-01

The Indo-West Pacific (IWP), from South Africa in the western Indian Ocean to the western Pacific Ocean, contains some of the most biologically diverse marine habitats on earth, including the greatest biodiversity of chondrichthyan fishes. The region encompasses various densities of human habitation leading to contrasts in the levels of exploitation experienced by chondrichthyans, which are targeted for local consumption and export. The demersal chondrichthyan, the zebra shark, Stegostoma fasciatum, is endemic to the IWP and has two current regional International Union for the Conservation of Nature (IUCN) Red List classifications that reflect differing levels of exploitation: 'Least Concern' and 'Vulnerable'. In this study, we employed mitochondrial ND4 sequence data and 13 microsatellite loci to investigate the population genetic structure of 180 zebra sharks from 13 locations throughout the IWP to test the concordance of IUCN zones with demographic units that have conservation value. Mitochondrial and microsatellite data sets from samples collected throughout northern Australia and Southeast Asia concord with the regional IUCN classifications. However, we found evidence of genetic subdivision within these regions, including subdivision between locations connected by habitat suitable for migration. Furthermore, parametric F(ST) analyses and Bayesian clustering analyses indicated that the primary genetic break within the IWP is not represented by the IUCN classifications but rather is congruent with the Indonesian throughflow current. Our findings indicate that recruitment to areas of high exploitation from nearby healthy populations in zebra sharks is likely to be minimal, and that severe localized depletions are predicted to occur in zebra shark populations throughout the IWP region.

Moving beyond qualitative evaluations of Bayesian models of cognition.

PubMed

Hemmer, Pernille; Tauber, Sean; Steyvers, Mark

2015-06-01

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

A Preliminary Bayesian Analysis of Incomplete Longitudinal Data from a Small Sample: Methodological Advances in an International Comparative Study of Educational Inequality

ERIC Educational Resources Information Center

Hsieh, Chueh-An; Maier, Kimberly S.

2009-01-01

The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection.…

Informative Bayesian Type A uncertainty evaluation, especially applicable to a small number of observations

NASA Astrophysics Data System (ADS)

Cox, M.; Shirono, K.

2017-10-01

A criticism levelled at the Guide to the Expression of Uncertainty in Measurement (GUM) is that it is based on a mixture of frequentist and Bayesian thinking. In particular, the GUM’s Type A (statistical) uncertainty evaluations are frequentist, whereas the Type B evaluations, using state-of-knowledge distributions, are Bayesian. In contrast, making the GUM fully Bayesian implies, among other things, that a conventional objective Bayesian approach to Type A uncertainty evaluation for a number n of observations leads to the impractical consequence that n must be at least equal to 4, thus presenting a difficulty for many metrologists. This paper presents a Bayesian analysis of Type A uncertainty evaluation that applies for all n ≥slant 2 , as in the frequentist analysis in the current GUM. The analysis is based on assuming that the observations are drawn from a normal distribution (as in the conventional objective Bayesian analysis), but uses an informative prior based on lower and upper bounds for the standard deviation of the sampling distribution for the quantity under consideration. The main outcome of the analysis is a closed-form mathematical expression for the factor by which the standard deviation of the mean observation should be multiplied to calculate the required standard uncertainty. Metrological examples are used to illustrate the approach, which is straightforward to apply using a formula or look-up table.

Work happiness among teachers: a day reconstruction study on the role of self-concordance.

PubMed

Tadić, Maja; Bakker, Arnold B; Oerlemans, Wido G M

2013-12-01

Self-concordant work motivation arises from one's authentic choices, personal values, and interests. In the present study, we investigated whether self-concordant motivation may fluctuate from one work-related task to the next. On the basis of self-determination theory, we hypothesized that momentary self-concordance buffers the negative impact of momentary work demands on momentary happiness. We developed a modified version of the day reconstruction method to investigate self-concordance, work demands, and happiness during specific work-related tasks on a within-person and within-day level. In total, 132 teachers completed a daily diary on three consecutive work days as well as a background questionnaire. The daily diary resulted in 792 reported work activities and activity-related work demands, self-concordance, and happiness scores. Multilevel analysis showed that-for most work activities-state self-concordant motivation buffered the negative association of work demands with happiness. These findings add to the literature on motivation and well-being by showing that the levels of self-concordance and happiness experienced by employees vary significantly on a within-day level and show a predictable pattern. We discuss theoretical and practical implications of the findings to increase employees' well-being. © 2013.

Retrospective study: The diagnostic accuracy of conventional forceps biopsy of gastric epithelial compared to endoscopic submucosal dissection (STROBE compliant).

PubMed

Lu, Chao; Lv, Xueyou; Lin, Yiming; Li, Dejian; Chen, Lihua; Ji, Feng; Li, Youming; Yu, Chaohui

2016-07-01

Conventional forceps biopsy (CFB) is the most popular way to screen for gastric epithelial neoplasia (GEN) and adenocarcinoma of gastric epithelium. The aim of this study was to compare the diagnostic accuracy between conventional forceps biopsy and endoscopic submucosal dissection (ESD).Four hundred forty-four patients who finally undertook ESD in our hospital were enrolled from Jan 1, 2009 to Sep 1, 2015. We retrospectively assessed the characteristics of pathological results of CFB and ESD.The concordance rate between CFB and ESD specimens was 68.92% (306/444). Men showed a lower concordance rate (63.61% vs 79.33%; P = 0.001) and concordance patients were younger (P = 0.048). In multivariate analysis, men significantly had a lower concordance rate (coefficient -0.730, P = 0.002) and a higher rate of pathological upgrade (coefficient -0.648, P = 0.015). Locations of CFB did not influence the concordance rate statistically.The concordance rate was relatively high in our hospital. According to our analysis, old men plus gastric fundus or antrum of CFB were strongly suggested to perform ESD if precancerous lesions were found. And young women with low-grade intraepithelial neoplasia could select regular follow-up.

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 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.

RadNet Air Data From Concord, NH

EPA Pesticide Factsheets

This page presents radiation air monitoring and air filter analysis data for Concord, NH from EPA's RadNet system. RadNet is a nationwide network of monitoring stations that measure radiation in air, drinking water and precipitation.

Impact of Patient-Provider Race, Ethnicity, and Gender Concordance on Cancer Screening: Findings from Medical Expenditure Panel Survey.

PubMed

Malhotra, Jyoti; Rotter, David; Tsui, Jennifer; Llanos, Adana A M; Balasubramanian, Bijal A; Demissie, Kitaw

2017-12-01

Background: Racial and ethnic minorities experience lower rates of cancer screening compared with non-Hispanic whites (NHWs). Previous studies evaluating the role of patient-provider race, ethnicity, or gender concordance in cancer screening have been inconclusive. Methods: In a cross-sectional analysis using the Medical Expenditure Panel Survey (MEPS), data from 2003 to 2010 were assessed for associations between patient-provider race, ethnicity, and/or gender concordance and, screening (American Cancer Society guidelines) for breast, cervical, and colorectal cancer. Multivariable logistic analyses were conducted to examine associations of interest. Results: Of the 32,041 patient-provider pairs in our analysis, more than 60% of the patients were NHW, 15% were non-Hispanic black (NHB), and 15% were Hispanic. Overall, patients adherent to cancer screening were more likely to be non-Hispanic, better educated, married, wealthier, and privately insured. Patient-provider gender discordance was associated with lower rates of breast [OR, 0.83; 95% confidence interval (CI), 0.76-0.90], cervical (OR, 0.83; 95% CI, 0.76-0.91), and colorectal cancer (OR, 0.84; 95% CI, 0.79-0.90) screening in all patients. This association was also significant after adjusting for racial and/or ethnic concordance. Conversely, among NHWs and NHBs, patient-provider racial and/or ethnic concordance was not associated with screening. Among Hispanics, patient-provider ethnic discordant pairs had higher breast (58% vs. 52%) and colorectal cancer (45% vs. 39%) screening rates compared with concordant pairs. Conclusions: Patient-provider gender concordance positively affected cancer screening. Patient-provider ethnic concordance was inversely associated with receipt of cancer screening among Hispanics. This counter-intuitive finding requires further study. Impact: Our findings highlight the importance of gender concordance in improving cancer screening rates. Cancer Epidemiol Biomarkers Prev; 26(12); 1804-11. ©2017 AACR . ©2017 American Association for Cancer Research.

Attitudes toward concordance and self-efficacy in decision making: a cross-sectional study on pharmacist-patient consultations.

PubMed

Ng, Yew Keong; Shah, Noraida Mohamed; Loong, Ly Sia; Pee, Lay Ting; Hidzir, Sarina Anim M; Chong, Wei Wen

2018-01-01

This study investigated patients' and pharmacists' attitudes toward concordance in a pharmacist-patient consultation and how patients' attitudes toward concordance relate to their involvement and self-efficacy in decision making associated with medication use. A cross-sectional study was conducted among patients with chronic diseases and pharmacists from three public hospitals in Malaysia. The Revised United States Leeds Attitudes toward Concordance (RUS-LATCon) was used to measure attitudes toward concordance in both patients and pharmacists. Patients also rated their perceived level of involvement in decision making and completed the Decision Self-Efficacy scale. One-way analysis of variance (ANOVA) and independent t -test were used to determine significant differences between different subgroups on attitudes toward concordance, and multiple linear regression was performed to find the predictors of patients' self-efficacy in decision making. A total of 389 patients and 93 pharmacists participated in the study. Pharmacists and patients scored M=3.92 (SD=0.37) and M=3.84 (SD=0.46) on the RUS-LATCon scale, respectively. Seven items were found to be significantly different between pharmacists and patients on the subscale level. Patients who felt fully involved in decision making (M=3.94, SD=0.462) scored significantly higher on attitudes toward concordance than those who felt partially involved (M=3.82, SD=0.478) and not involved at all (M=3.68, SD=0.471; p <0.001). Patients had an average score of 76.7% (SD=14.73%) on the Decision Self-Efficacy scale. In multiple linear regression analysis, ethnicity, number of medications taken by patients, patients' perceived level of involvement, and attitudes toward concordance are significant predictors of patients' self-efficacy in decision making ( p <0.05). Patients who felt involved in their consultations had more positive attitudes toward concordance and higher confidence in making an informed decision. Further study is recommended on interventions involving pharmacists in supporting patients' involvement in medication-related decision making.

Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users

PubMed Central

Han, Hyemin; Park, Joonsuk

2018-01-01

Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research. PMID:29456498

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

The role of peripheral endemism in species diversification: evidence from the coral reef fish genus Anampses (Family: Labridae).

PubMed

Hodge, Jennifer R; Read, Charmaine I; van Herwerden, Lynne; Bellwood, David R

2012-02-01

We examined how peripherally isolated endemic species may have contributed to the biodiversity of the Indo-Australian Archipelago biodiversity hotspot by reconstructing the evolutionary history of the wrasse genus Anampses. We identified three alternate models of diversification: the vicariance-based 'successive division' model, and the dispersal-based 'successive colonisation' and 'peripheral budding' models. The genus was well suited for this study given its relatively high proportion (42%) of endemic species, its reasonably low diversity (12 species), which permitted complete taxon sampling, and its widespread tropical Indo-Pacific distribution. Monophyly of the genus was strongly supported by three phylogenetic analyses: maximum parsimony, maximum likelihood, and Bayesian inference based on mitochondrial CO1 and 12S rRNA and nuclear S7 sequences. Estimates of species divergence times from fossil-calibrated Bayesian inference suggest that Anampses arose in the mid-Eocene and subsequently diversified throughout the Miocene. Evolutionary relationships within the genus, combined with limited spatial and temporal concordance among endemics, offer support for all three alternate models of diversification. Our findings emphasise the importance of peripherally isolated locations in creating and maintaining endemic species and their contribution to the biodiversity of the Indo-Australian Archipelago. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2012-01-01

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for…

The phylodynamics of the rabies virus in the Russian Federation

PubMed Central

Lukashev, Alexander N.; Poleshchuk, Elena M.; Dedkov, Vladimir G.; Tkachev, Sergey E.; Sidorov, Gennadiy N.; Karganova, Galina G.; Galkina, Irina V.; Shchelkanov, Mikhail Yu.; Shipulin, German A.

2017-01-01

Near complete rabies virus N gene sequences (1,110 nt) were determined for 82 isolates obtained from different regions of Russia between 2008 and 2016. These sequences were analyzed together with 108 representative GenBank sequences from 1977–2016 using the Bayesian coalescent approach. The timing of the major evolutionary events was estimated. Most of the isolates represented the steppe rabies virus group C, which was found over a vast geographic region from Central Russia to Mongolia and split into three groups (C0-C2) with discrete geographic prevalence. A single strain of the steppe rabies virus lineage was isolated in the far eastern part of Russia (Primorsky Krai), likely as a result of a recent anthropogenic introduction. For the first time the polar rabies virus group A2, previously reported in Alaska, was described in the northern part of European Russia and at the Franz Josef Land. Phylogenetic analysis suggested that all currently circulating rabies virus groups in the Russian Federation were introduced within the few last centuries, with most of the groups spreading in the 20th century. The dating of evolutionary events was highly concordant with the historical epidemiological data. PMID:28225771

Evolution of plant growth and defense in a continental introduction.

PubMed

Agrawal, Anurag A; Hastings, Amy P; Bradburd, Gideon S; Woods, Ellen C; Züst, Tobias; Harvey, Jeffrey A; Bukovinszky, Tibor

2015-07-01

Substantial research has addressed adaptation of nonnative biota to novel environments, yet surprisingly little work has integrated population genetic structure and the mechanisms underlying phenotypic differentiation in ecologically important traits. We report on studies of the common milkweed Asclepias syriaca, which was introduced from North America to Europe over the past 400 years and which lacks most of its specialized herbivores in the introduced range. Using 10 populations from each continent grown in a common environment, we identified several growth and defense traits that have diverged, despite low neutral genetic differentiation between continents. We next developed a Bayesian modeling approach to account for relationships between molecular and phenotypic differences, confirming that continental trait differentiation was greater than expected from neutral genetic differentiation. We found evidence that growth-related traits adaptively diverged within and between continents. Inducible defenses triggered by monarch butterfly herbivory were substantially reduced in European populations, and this reduction in inducibility was concordant with altered phytohormonal dynamics, reduced plant growth, and a trade-off with constitutive investment. Freedom from the community of native and specialized herbivores may have favored constitutive over induced defense. Our replicated analysis of plant growth and defense, including phenotypically plastic traits, suggests adaptive evolution following a continental introduction.

Evaluation of concordance among three cardiac output measurement techniques in adult patients during cardiovascular surgery postoperative care.

PubMed

Muñoz, L; Velandia, A; Reyes, L E; Arevalo-Rodríguez, I; Mejía, C; Asprilla, D; Uribe, D V; Arevalo, J J

2017-12-01

The standard method for cardiac output measuring is thermodilution although it is an invasive technique. Transesophageal Echocardiography (TEE) offers a dynamic and functional alternative to thermodilution. Analyze concordance between two TEE methods and thermodilution for cardiac output assessment. Observational concordance study in cardiovascular surgery patients that required pulmonary artery catheter. TEE cardiac output measurement at both mitral annulus (MA) and left ventricle outflow tract (LVOT) were performed. Results were compared with thermodilution. Correlation was evaluated by Lin's concordance correlation coefficient and Bland-Altman analysis. Statistical analysis was undertaken in STATA 13.0. Twenty-five patients were enrolled. Fifty two percent of patients were male, median age and ejection fraction was 63 years and 35% respectively. Median thermodilution, LVOT and MA -measured cardiac output was 3.25 L/min, 3.46 L/min and 8.4 L/min respectively. Different values between thermodilution and MA measurements were found (Lin concordance=0.071; Confidence Interval 95%=-0.009 to 0.151; Spearman's correlation=0.22) as values between thermodilution and LVOT (Lin concordance=0.232; Confidence Interval 95%=-0.12 a 0.537; Spearman's correlation 0.28). Bland-Altman analysis showed greater difference between MA measurements and thermodilution (DM=-0.408; Bland-Altman Limits=-0.809 to -0.007), than the other echocardiographic findings (DM=0.007; Bland-Altman Limits=-0.441 to 0.428). Results from cardiac output measurement by doppler and 2D-TEE on both MA and LVOT do not correlate with those obtained by thermodilution. Copyright © 2017 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.

Prior elicitation and Bayesian analysis of the Steroids for Corneal Ulcers Trial.

PubMed

See, Craig W; Srinivasan, Muthiah; Saravanan, Somu; Oldenburg, Catherine E; Esterberg, Elizabeth J; Ray, Kathryn J; Glaser, Tanya S; Tu, Elmer Y; Zegans, Michael E; McLeod, Stephen D; Acharya, Nisha R; Lietman, Thomas M

2012-12-01

To elicit expert opinion on the use of adjunctive corticosteroid therapy in bacterial corneal ulcers. To perform a Bayesian analysis of the Steroids for Corneal Ulcers Trial (SCUT), using expert opinion as a prior probability. The SCUT was a placebo-controlled trial assessing visual outcomes in patients receiving topical corticosteroids or placebo as adjunctive therapy for bacterial keratitis. Questionnaires were conducted at scientific meetings in India and North America to gauge expert consensus on the perceived benefit of corticosteroids as adjunct treatment. Bayesian analysis, using the questionnaire data as a prior probability and the primary outcome of SCUT as a likelihood, was performed. For comparison, an additional Bayesian analysis was performed using the results of the SCUT pilot study as a prior distribution. Indian respondents believed there to be a 1.21 Snellen line improvement, and North American respondents believed there to be a 1.24 line improvement with corticosteroid therapy. The SCUT primary outcome found a non-significant 0.09 Snellen line benefit with corticosteroid treatment. The results of the Bayesian analysis estimated a slightly greater benefit than did the SCUT primary analysis (0.19 lines verses 0.09 lines). Indian and North American experts had similar expectations on the effectiveness of corticosteroids in bacterial corneal ulcers; that corticosteroids would markedly improve visual outcomes. Bayesian analysis produced results very similar to those produced by the SCUT primary analysis. The similarity in result is likely due to the large sample size of SCUT and helps validate the results of SCUT.

Careful with Those Priors: A Note on Bayesian Estimation in Two-Parameter Logistic Item Response Theory Models

ERIC Educational Resources Information Center

Marcoulides, Katerina M.

2018-01-01

This study examined the use of Bayesian analysis methods for the estimation of item parameters in a two-parameter logistic item response theory model. Using simulated data under various design conditions with both informative and non-informative priors, the parameter recovery of Bayesian analysis methods were examined. Overall results showed that…

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2014-01-01

Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

CytoBayesJ: software tools for Bayesian analysis of cytogenetic radiation dosimetry data.

PubMed

Ainsbury, Elizabeth A; Vinnikov, Volodymyr; Puig, Pedro; Maznyk, Nataliya; Rothkamm, Kai; Lloyd, David C

2013-08-30

A number of authors have suggested that a Bayesian approach may be most appropriate for analysis of cytogenetic radiation dosimetry data. In the Bayesian framework, probability of an event is described in terms of previous expectations and uncertainty. Previously existing, or prior, information is used in combination with experimental results to infer probabilities or the likelihood that a hypothesis is true. It has been shown that the Bayesian approach increases both the accuracy and quality assurance of radiation dose estimates. New software entitled CytoBayesJ has been developed with the aim of bringing Bayesian analysis to cytogenetic biodosimetry laboratory practice. CytoBayesJ takes a number of Bayesian or 'Bayesian like' methods that have been proposed in the literature and presents them to the user in the form of simple user-friendly tools, including testing for the most appropriate model for distribution of chromosome aberrations and calculations of posterior probability distributions. The individual tools are described in detail and relevant examples of the use of the methods and the corresponding CytoBayesJ software tools are given. In this way, the suitability of the Bayesian approach to biological radiation dosimetry is highlighted and its wider application encouraged by providing a user-friendly software interface and manual in English and Russian. Copyright © 2013 Elsevier B.V. All rights reserved.

Bayesian data analysis in observational comparative effectiveness research: rationale and examples.

PubMed

Olson, William H; Crivera, Concetta; Ma, Yi-Wen; Panish, Jessica; Mao, Lian; Lynch, Scott M

2013-11-01

Many comparative effectiveness research and patient-centered outcomes research studies will need to be observational for one or both of two reasons: first, randomized trials are expensive and time-consuming; and second, only observational studies can answer some research questions. It is generally recognized that there is a need to increase the scientific validity and efficiency of observational studies. Bayesian methods for the design and analysis of observational studies are scientifically valid and offer many advantages over frequentist methods, including, importantly, the ability to conduct comparative effectiveness research/patient-centered outcomes research more efficiently. Bayesian data analysis is being introduced into outcomes studies that we are conducting. Our purpose here is to describe our view of some of the advantages of Bayesian methods for observational studies and to illustrate both realized and potential advantages by describing studies we are conducting in which various Bayesian methods have been or could be implemented.

Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

PubMed

Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

2015-01-01

Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

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.

Bayesian linkage and segregation analysis: factoring the problem.

PubMed

Matthysse, S

2000-01-01

Complex segregation analysis and linkage methods are mathematical techniques for the genetic dissection of complex diseases. They are used to delineate complex modes of familial transmission and to localize putative disease susceptibility loci to specific chromosomal locations. The computational problem of Bayesian linkage and segregation analysis is one of integration in high-dimensional spaces. In this paper, three available techniques for Bayesian linkage and segregation analysis are discussed: Markov Chain Monte Carlo (MCMC), importance sampling, and exact calculation. The contribution of each to the overall integration will be explicitly discussed.

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

A Primer on Bayesian Analysis for Experimental Psychopathologists

PubMed Central

Krypotos, Angelos-Miltiadis; Blanken, Tessa F.; Arnaudova, Inna; Matzke, Dora; Beckers, Tom

2016-01-01

The principal goals of experimental psychopathology (EPP) research are to offer insights into the pathogenic mechanisms of mental disorders and to provide a stable ground for the development of clinical interventions. The main message of the present article is that those goals are better served by the adoption of Bayesian statistics than by the continued use of null-hypothesis significance testing (NHST). In the first part of the article we list the main disadvantages of NHST and explain why those disadvantages limit the conclusions that can be drawn from EPP research. Next, we highlight the advantages of Bayesian statistics. To illustrate, we then pit NHST and Bayesian analysis against each other using an experimental data set from our lab. Finally, we discuss some challenges when adopting Bayesian statistics. We hope that the present article will encourage experimental psychopathologists to embrace Bayesian statistics, which could strengthen the conclusions drawn from EPP research. PMID:28748068

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

PubMed

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

PubMed Central

Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated. PMID:28886019

Individual characteristics, area social participation, and primary non-concordance with medication: a multilevel analysis.

PubMed

Johnell, Kristina; Lindström, Martin; Sundquist, Jan; Eriksson, Charli; Merlo, Juan

2006-03-02

Non-concordance with medication remains a major public health problem that imposes a considerable financial burden on the health care system, and there is still a need for studies on correlates of non-concordance. Our first aim is to analyse whether any of the individual characteristics age, educational level, financial strain, self-rated health, social participation, and trust in the health care system are associated with primary non-concordance with medication. Our second aim is to investigate whether people living in the same area have similar probability of primary non-concordance with medication, that relates to area social participation. We analysed cross sectional data from 9,070 women and 6,795 men aged 18 to 79 years, living in 78 areas in central Sweden, who participated in the Life & Health year 2000 survey, with multilevel logistic regression (individuals at the first level and areas at the second level). Younger age, financial strain, low self-rated health, and low trust in the health care system were associated with primary non-concordance with medication. However, area social participation was not related to primary non-concordance, and the variation in primary non-concordance between the areas was small. Our results indicate that people in central Sweden with younger age, financial difficulties, low self-rated health, and low trust in the health care system may have a higher probability of primary non-concordance with medication. However, the area of residence--as defined by administrative boundaries--seems to play a minor role for primary non-concordance.

Individual characteristics, area social participation, and primary non-concordance with medication: a multilevel analysis

PubMed Central

Johnell, Kristina; Lindström, Martin; Sundquist, Jan; Eriksson, Charli; Merlo, Juan

2006-01-01

Background Non-concordance with medication remains a major public health problem that imposes a considerable financial burden on the health care system, and there is still a need for studies on correlates of non-concordance. Our first aim is to analyse whether any of the individual characteristics age, educational level, financial strain, self-rated health, social participation, and trust in the health care system are associated with primary non-concordance with medication. Our second aim is to investigate whether people living in the same area have similar probability of primary non-concordance with medication, that relates to area social participation. Methods We analysed cross sectional data from 9 070 women and 6 795 men aged 18 to 79 years, living in 78 areas in central Sweden, who participated in the Life & Health year 2000 survey, with multilevel logistic regression (individuals at the first level and areas at the second level). Results Younger age, financial strain, low self-rated health, and low trust in the health care system were associated with primary non-concordance with medication. However, area social participation was not related to primary non-concordance, and the variation in primary non-concordance between the areas was small. Conclusion Our results indicate that people in central Sweden with younger age, financial difficulties, low self-rated health, and low trust in the health care system may have a higher probability of primary non-concordance with medication. However, the area of residence – as defined by administrative boundaries – seems to play a minor role for primary non-concordance. PMID:16512907

Spousal concordance in attitudes toward violence and reported physical abuse in African couples.

PubMed

Alio, Amina P; Clayton, Heather B; Garba, Madeleine; Mbah, Alfred K; Daley, Ellen; Salihu, Hamisu M

2011-09-01

We examined the potential association between African couples' concordance on attitudes toward violence (ATV) and risk for intimate partner violence (IPV). Analyses included 13,837 couples from Demographic and Health Surveys conducted between 2003 and 2007, from six African countries. Concordance on ATV was defined as both spouses justifying physical abuse, and IPV was defined as incidence of a physically violent act against the wife. We constructed a concordance measure from the surveys to assess overall and country-level differences in couple's ATV concordance rates and assessed the association between concordance in ATV and IPV using hierarchical regression modeling that adjusted for multilevel influences on risk estimates. Negative concordance (perfect agreement in negative ATV) was used as referent category in all analyses. Overall, spousal ATV concordance was associated with higher likelihood for IPV (adjusted odds ratio [AOR] = 2.27, 95% confidence interval [CI] = [2.01, 2.56]). The level of wealth, educational attainment, rural/urban residence, presence of a cowife, religion, maternal age, and parity were characteristics that predicted the occurrence of IPV within couples. Spousal ATV concordance was significantly associated with violence in every African nation included in the analysis except Rwanda. African couples with high rates of ATV concor- dance experience higher risks for IPV, with some variation in magnitude of risk across countries. In African settings, ATV positive concordance could serve as a supplemental screening tool to detect spousal violence. Understanding ATV could potentially enhance our ability to formulate public health intervention to detect and prevent spousal abuse.

Patient-Clinician Ethnic Concordance and Communication in Mental Health Intake Visits

PubMed Central

Alegría, Margarita; Roter, Debra L.; Valentine, Anne; Chen, Chih-nan; Li, Xinliang; Lin, Julia; Rosen, Daniel; Lapatin, Sheri; Normand, Sharon-Lise; Larson, Susan; Shrout, Patrick E.

2013-01-01

Objective This study examines how communication patterns vary across racial and ethnic patient-clinician dyads in mental health intake sessions and its relation to continuance in treatment, defined as attending the next scheduled appointment. Methods Observational study of communication patterns among ethnically/racially concordant and discordant patient-clinician dyads. Primary analysis included 93 patients with 38 clinicians in race/ethnic concordant and discordant dyads. Communication was coded using the Roter Interaction Analysis System (RIAS) and the Working Alliance Inventory Observer (WAI-O) bond scale; continuance in care was derived from chart reviews. Results Latino concordant dyad patients were more verbally dominant (p<.05), engaged in more patient-centered communication (p<.05) and scored higher on the (WAI-O) bond scale (all p<.05) than other groups. Latino patients had higher continuance rates than other patients in models that adjusted for non-communication variables. When communication, global affect, and therapeutic process variables were adjusted for, differences were reversed and white dyad patients had higher continuance in care rates than other dyad patients. Conclusion Communication patterns seem to explain the role of ethnic concordance for continuance in care. Practice Implications Improve intercultural communication in cross cultural encounters appears significant for retaining minorities in care. PMID:23896127

[Bayesian statistics in medicine -- part II: main applications and inference].

PubMed

Montomoli, C; Nichelatti, M

2008-01-01

Bayesian statistics is not only used when one is dealing with 2-way tables, but it can be used for inferential purposes. Using the basic concepts presented in the first part, this paper aims to give a simple overview of Bayesian methods by introducing its foundation (Bayes' theorem) and then applying this rule to a very simple practical example; whenever possible, the elementary processes at the basis of analysis are compared to those of frequentist (classical) statistical analysis. The Bayesian reasoning is naturally connected to medical activity, since it appears to be quite similar to a diagnostic process.

Prior Elicitation and Bayesian Analysis of the Steroids for Corneal Ulcers Trial

PubMed Central

See, Craig W.; Srinivasan, Muthiah; Saravanan, Somu; Oldenburg, Catherine E.; Esterberg, Elizabeth J.; Ray, Kathryn J.; Glaser, Tanya S.; Tu, Elmer Y.; Zegans, Michael E.; McLeod, Stephen D.; Acharya, Nisha R.; Lietman, Thomas M.

2013-01-01

Purpose To elicit expert opinion on the use of adjunctive corticosteroid therapy in bacterial corneal ulcers. To perform a Bayesian analysis of the Steroids for Corneal Ulcers Trial (SCUT), using expert opinion as a prior probability. Methods The SCUT was a placebo-controlled trial assessing visual outcomes in patients receiving topical corticosteroids or placebo as adjunctive therapy for bacterial keratitis. Questionnaires were conducted at scientific meetings in India and North America to gauge expert consensus on the perceived benefit of corticosteroids as adjunct treatment. Bayesian analysis, using the questionnaire data as a prior probability and the primary outcome of SCUT as a likelihood, was performed. For comparison, an additional Bayesian analysis was performed using the results of the SCUT pilot study as a prior distribution. Results Indian respondents believed there to be a 1.21 Snellen line improvement, and North American respondents believed there to be a 1.24 line improvement with corticosteroid therapy. The SCUT primary outcome found a non-significant 0.09 Snellen line benefit with corticosteroid treatment. The results of the Bayesian analysis estimated a slightly greater benefit than did the SCUT primary analysis (0.19 lines verses 0.09 lines). Conclusion Indian and North American experts had similar expectations on the effectiveness of corticosteroids in bacterial corneal ulcers; that corticosteroids would markedly improve visual outcomes. Bayesian analysis produced results very similar to those produced by the SCUT primary analysis. The similarity in result is likely due to the large sample size of SCUT and helps validate the results of SCUT. PMID:23171211

Surgical site infections among high-risk patients in clean-contaminated head and neck reconstructive surgery: concordance with preoperative oral flora.

PubMed

Yang, Ching-Hsiang; Chew, Khong-Yik; Solomkin, Joseph S; Lin, Pao-Yuan; Chiang, Yuan-Cheng; Kuo, Yur-Ren

2013-12-01

Salivary contamination of surgical wounds in clean-contaminated head and neck surgery with free flap reconstruction remains a major cause of infection and leads to significant morbidity. This study investigates the correlation between intraoral flora and surgical site infections (SSIs) among high-risk head and neck cancer patients undergoing resection and free flap reconstruction. One hundred twenty-nine patients were identified as being at high risk for infective complications based on cancer stage, tumor size, comorbid factors, and extent of reconstruction. All patients had intraoral swab cultures before surgery. Patients with culture-confirmed SSI after surgery were chosen for analysis, using the κ index and its 95% confidence interval for concordance analysis. All patients received clindamycin and gentamicin for antibiotic prophylaxis for 5 days. Antibiotic susceptibility testing of all isolates was obtained and analyzed. Thirty-seven patients experienced SSI, or an infection rate of 28.3%, occurring at a mean of 9.3 postoperative days. The overall concordance between oral flora and SSI was fair to moderate (κ index of 0.25), but detailed analysis shows a higher concordance for known and opportunistic pathogens, such as Pseudomonas aeruginosa and Enterococcus faecalis, compared to typical oral commensals. Antibiotic susceptibility tests show rapid and significant increases in resistance to clindamycin, indicating a need for a more effective alternative. Predicting pathogens in SSI using preoperative oral swabs did not demonstrate a good concordance in general for patients undergoing clean-contaminated head and neck surgery, although concordance for certain pathogenic species seem to be higher than for typical intraoral commensals. The rapid development of resistance to clindamycin precludes its use as a prophylactic agent.

Familial intracranial aneurysms: is anatomic vulnerability heritable?

PubMed

Mackey, Jason; Brown, Robert D; Moomaw, Charles J; Hornung, Richard; Sauerbeck, Laura; Woo, Daniel; Foroud, Tatiana; Gandhi, Dheeraj; Kleindorfer, Dawn; Flaherty, Matthew L; Meissner, Irene; Anderson, Craig; Rouleau, Guy; Connolly, E Sander; Deka, Ranjan; Koller, Daniel L; Abruzzo, Todd; Huston, John; Broderick, Joseph P

2013-01-01

Previous studies have suggested that family members with intracranial aneurysms (IAs) often harbor IAs in similar anatomic locations. IA location is important because of its association with rupture. We tested the hypothesis that anatomic susceptibility to IA location exists using a family-based IA study. We identified all affected probands and first-degree relatives (FDRs) with a definite or probable phenotype in each family. We stratified each IA of the probands by major arterial territory and calculated each family's proband-FDR territory concordance and overall contribution to the concordance analysis. We then matched each family unit to an unrelated family unit selected randomly with replacement and performed 1001 simulations. The median concordance proportions, odds ratios (ORs), and P values from the 1001 logistic regression analyses were used to represent the final results of the analysis. There were 323 family units available for analysis, including 323 probands and 448 FDRs, with a total of 1176 IAs. IA territorial concordance was higher in the internal carotid artery (55.4% versus 45.6%; OR, 1.54 [1.04-2.27]; P=0.032), middle cerebral artery (45.8% versus 30.5%; OR, 1.99 [1.22-3.22]; P=0.006), and vertebrobasilar system (26.6% versus 11.3%; OR, 2.90 [1.05-8.24], P=0.04) distributions in the true family compared with the comparison family. Concordance was also higher when any location was considered (53.0% versus 40.7%; OR, 1.82 [1.34-2.46]; P<0.001). In a highly enriched sample with familial predisposition to IA development, we found that IA territorial concordance was higher when probands were compared with their own affected FDRs than with comparison FDRs, which suggests that anatomic vulnerability to IA formation exists. Future studies of IA genetics should consider stratifying cases by IA location.

A Gibbs sampler for Bayesian analysis of site-occupancy data

USGS Publications Warehouse

Dorazio, Robert M.; Rodriguez, Daniel Taylor

2012-01-01

1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.

RECONSTRUCTING EXPOSURE SCENARIOS USING DOSE BIOMARKERS - AN APPLICATION OF BAYESIAN UNCERTAINTY ANALYSIS

EPA Science Inventory

We use Bayesian uncertainty analysis to explore how to estimate pollutant exposures from biomarker concentrations. The growing number of national databases with exposure data makes such an analysis possible. They contain datasets of pharmacokinetic biomarkers for many polluta...

Attitudes toward concordance and self-efficacy in decision making: a cross-sectional study on pharmacist–patient consultations

PubMed Central

Ng, Yew Keong; Shah, Noraida Mohamed; Loong, Ly Sia; Pee, Lay Ting; Hidzir, Sarina Anim M; Chong, Wei Wen

2018-01-01

Purpose This study investigated patients’ and pharmacists’ attitudes toward concordance in a pharmacist–patient consultation and how patients’ attitudes toward concordance relate to their involvement and self-efficacy in decision making associated with medication use. Subjects and methods A cross-sectional study was conducted among patients with chronic diseases and pharmacists from three public hospitals in Malaysia. The Revised United States Leeds Attitudes toward Concordance (RUS-LATCon) was used to measure attitudes toward concordance in both patients and pharmacists. Patients also rated their perceived level of involvement in decision making and completed the Decision Self-Efficacy scale. One-way analysis of variance (ANOVA) and independent t-test were used to determine significant differences between different subgroups on attitudes toward concordance, and multiple linear regression was performed to find the predictors of patients’ self-efficacy in decision making. Results A total of 389 patients and 93 pharmacists participated in the study. Pharmacists and patients scored M=3.92 (SD=0.37) and M=3.84 (SD=0.46) on the RUS-LATCon scale, respectively. Seven items were found to be significantly different between pharmacists and patients on the subscale level. Patients who felt fully involved in decision making (M=3.94, SD=0.462) scored significantly higher on attitudes toward concordance than those who felt partially involved (M=3.82, SD=0.478) and not involved at all (M=3.68, SD=0.471; p<0.001). Patients had an average score of 76.7% (SD=14.73%) on the Decision Self-Efficacy scale. In multiple linear regression analysis, ethnicity, number of medications taken by patients, patients’ perceived level of involvement, and attitudes toward concordance are significant predictors of patients’ self-efficacy in decision making (p<0.05). Conclusion Patients who felt involved in their consultations had more positive attitudes toward concordance and higher confidence in making an informed decision. Further study is recommended on interventions involving pharmacists in supporting patients’ involvement in medication-related decision making. PMID:29731609

Bayesian Factor Analysis as a Variable Selection Problem: Alternative Priors and Consequences

PubMed Central

Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric

2016-01-01

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, a Bayesian structural equation modeling (BSEM) approach (Muthén & Asparouhov, 2012) has been proposed as a way to explore the presence of cross-loadings in CFA models. We show that the issue of determining factor loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov’s approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike and slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set (Byrne, 2012; Pettegrew & Wolf, 1982) is used to demonstrate our approach. PMID:27314566

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.

Non-invasive preimplantation genetic screening using array comparative genomic hybridization on spent culture media: a proof-of-concept pilot study.

PubMed

Feichtinger, Michael; Vaccari, Enrico; Carli, Luca; Wallner, Elisabeth; Mädel, Ulrike; Figl, Katharina; Palini, Simone; Feichtinger, Wilfried

2017-06-01

The aim of this pilot study was to assess if array comparative genomic hybridization (aCGH), non-invasive preimplantation genetic screening (PGS) on blastocyst culture media is feasible. Therefore, aCGH analysis was carried out on 22 spent blastocyst culture media samples after polar body PGS because of advanced maternal age. All oocytes were fertilized by intracytoplasmic sperm injection and all embryos underwent assisted hatching. Concordance of polar body analysis and culture media genetic results was assessed. Thirteen out of 18 samples (72.2%) revealed general concordance of ploidy status (euploid or aneuploid). At least one chromosomal aberration was found concordant in 10 out of 15 embryos found to be aneuploid by both polar body and culture media analysis. Overall, 17 out of 35 (48.6%) single chromosomal aneuploidies were concordant between the culture media and polar body analysis. By analysing negative controls (oocytes with fertilization failure), notable maternal contamination was observed. Therefore, non-invasive PGS could serve as a second matrix after polar body or cleavage stage PGS; however, in euploid results, maternal contamination needs to be considered and results interpreted with caution. Copyright © 2017 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Diagnostic accuracy of a bayesian latent group analysis for the detection of malingering-related poor effort.

PubMed

Ortega, Alonso; Labrenz, Stephan; Markowitsch, Hans J; Piefke, Martina

2013-01-01

In the last decade, different statistical techniques have been introduced to improve assessment of malingering-related poor effort. In this context, we have recently shown preliminary evidence that a Bayesian latent group model may help to optimize classification accuracy using a simulation research design. In the present study, we conducted two analyses. Firstly, we evaluated how accurately this Bayesian approach can distinguish between participants answering in an honest way (honest response group) and participants feigning cognitive impairment (experimental malingering group). Secondly, we tested the accuracy of our model in the differentiation between patients who had real cognitive deficits (cognitively impaired group) and participants who belonged to the experimental malingering group. All Bayesian analyses were conducted using the raw scores of a visual recognition forced-choice task (2AFC), the Test of Memory Malingering (TOMM, Trial 2), and the Word Memory Test (WMT, primary effort subtests). The first analysis showed 100% accuracy for the Bayesian model in distinguishing participants of both groups with all effort measures. The second analysis showed outstanding overall accuracy of the Bayesian model when estimates were obtained from the 2AFC and the TOMM raw scores. Diagnostic accuracy of the Bayesian model diminished when using the WMT total raw scores. Despite, overall diagnostic accuracy can still be considered excellent. The most plausible explanation for this decrement is the low performance in verbal recognition and fluency tasks of some patients of the cognitively impaired group. Additionally, the Bayesian model provides individual estimates, p(zi |D), of examinees' effort levels. In conclusion, both high classification accuracy levels and Bayesian individual estimates of effort may be very useful for clinicians when assessing for effort in medico-legal settings.

The impact of using informative priors in a Bayesian cost-effectiveness analysis: an application of endovascular versus open surgical repair for abdominal aortic aneurysms in high-risk patients.

PubMed

McCarron, C Elizabeth; Pullenayegum, Eleanor M; Thabane, Lehana; Goeree, Ron; Tarride, Jean-Eric

2013-04-01

Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis. Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs). The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly. The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.

Phenotypic concordance in familial inflammatory bowel disease (IBD). Results of a nationwide IBD Spanish database.

PubMed

Cabré, Eduard; Mañosa, Míriam; García-Sánchez, Valle; Gutiérrez, Ana; Ricart, Elena; Esteve, Maria; Guardiola, Jordi; Aguas, Mariam; Merino, Olga; Ponferrada, Angel; Gisbert, Javier P; Garcia-Planella, Esther; Ceña, Gloria; Cabriada, José L; Montoro, Miguel; Domènech, Eugeni

2014-07-01

Disease outcome has been found to be poorer in familial inflammatory bowel disease (IBD) than in sporadic forms, but assessment of phenotypic concordance in familial IBD provided controversial results. We assessed the concordance for disease type and phenotypic features in IBD families. Patients with familial IBD were identified from the IBD Spanish database ENEIDA. Families in whom at least two members were in the database were selected for concordance analysis (κ index). Concordance for type of IBD [Crohn's disease (CD) vs. ulcerative colitis (UC)], as well as for disease extent, localization and behaviour, perianal disease, extraintestinal manifestations, and indicators of severe disease (i.e., need for immunosuppressors, biological agents, and surgery) for those pairs concordant for IBD type, were analyzed. 798 out of 11,905 IBD patients (7%) in ENEIDA had familial history of IBD. Complete data of 107 families (231 patients and 144 consanguineous pairs) were available for concordance analyses. The youngest members of the pairs were diagnosed with IBD at a significantly younger age (p<0.001) than the oldest ones. Seventy-six percent of pairs matched up for the IBD type (κ=0.58; 95%CI: 0.42-0.73, moderate concordance). There was no relevant concordance for any of the phenotypic items assessed in both diseases. Familial IBD is associated with diagnostic anticipation in younger individuals. Familial history does not allow predicting any phenotypic feature other than IBD type. Copyright © 2013 European Crohn's and Colitis Organisation. Published by Elsevier B.V. All rights reserved.

Receipt of Guideline-Concordant Treatment in Elderly Prostate Cancer Patients

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

Chen, Ronald C., E-mail: Ronald_chen@med.unc.edu; Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

2014-02-01

Purpose: To examine the proportion of elderly prostate cancer patients receiving guideline-concordant treatment, using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. Methods and Materials: A total of 29,001 men diagnosed in 2004-2007 with localized prostate cancer, aged 66 to 79 years, were included. We characterized the proportion of men who received treatment concordant with the National Comprehensive Cancer Network guidelines, stratified by risk group and age. Logistic regression was used to examine covariates associated with receipt of guideline-concordant management. Results: Guideline concordance was 79%-89% for patients with low- or intermediate-risk disease. Among high-risk patients, 66.6% of those agedmore » 66-69 years received guideline-concordant management, compared with 51.9% of those aged 75-79 years. Discordance was mainly due to conservative management—no treatment or hormone therapy alone. Among the subgroup of patients aged ≤76 years with no measured comorbidity, findings were similar. On multivariable analysis, older age (75-79 vs 66-69 years, odds ratio 0.51, 95% confidence interval 0.50-0.57) was associated with a lower likelihood of guideline concordance for high-risk prostate cancer, but comorbidity was not. Conclusions: There is undertreatment of elderly but healthy patients with high-risk prostate cancer, the most aggressive form of this disease.« less

Single-Case Time Series with Bayesian Analysis: A Practitioner's Guide.

ERIC Educational Resources Information Center

Jones, W. Paul

2003-01-01

This article illustrates a simplified time series analysis for use by the counseling researcher practitioner in single-case baseline plus intervention studies with a Bayesian probability analysis to integrate findings from replications. The C statistic is recommended as a primary analysis tool with particular relevance in the context of actual…

Daniel Goodman’s empirical approach to Bayesian statistics

USGS Publications Warehouse

Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

2016-01-01

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

Robust Bayesian Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Yuan, Ke-Hai

2003-01-01

Bayesian factor analysis (BFA) assumes the normal distribution of the current sample conditional on the parameters. Practical data in social and behavioral sciences typically have significant skewness and kurtosis. If the normality assumption is not attainable, the posterior analysis will be inaccurate, although the BFA depends less on the current…

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…

Concordance of Gleason grading with three-dimensional ultrasound systematic biopsy and biopsy core pre-embedding.

PubMed

van der Aa, Anouk A M A; Mannaerts, Christophe K; van der Linden, Hans; Gayet, Maudy; Schrier, Bart Ph; Mischi, Massimo; Beerlage, Harrie P; Wijkstra, Hessel

2018-02-01

To determine the value of a three-dimensional (3D) greyscale transrectal ultrasound (TRUS)-guided prostate biopsy system and biopsy core pre-embedding method on concordance between Gleason scores of needle biopsies and radical prostatectomy (RP) specimens. Retrospective analysis of prostate biopsies and subsequent RP for PCa in the Jeroen Bosch Hospital, the Netherlands, from 2007 to 2016. Two cohorts were analysed: conventional 2D TRUS-guided biopsies and RP (2007-2013, n = 266) versus 3D TRUS-guided biopsies with pre-embedding (2013-2016, n = 129). The impact of 3D TRUS-guidance with pre-embedding on Gleason score (GS) concordance between biopsy and RP was evaluated using the κ-coefficient. Predictors of biopsy GS 6 upgrading were assessed using logistic regression models. Gleason concordance was comparable between the two cohorts with a κ = 0.44 for the 3D cohort, compared to κ = 0.42 for the 2D cohort. 3D TRUS-guidance with pre-embedding, did not significantly affect the risk of biopsy GS 6 upgrading in univariate and multivariate analysis. 3D TRUS-guidance with biopsy core pre-embedding did not improve Gleason concordance. Improved detection techniques are needed for recognition of low-grade disease upgrading.

Patient-clinician ethnic concordance and communication in mental health intake visits.

PubMed

Alegría, Margarita; Roter, Debra L; Valentine, Anne; Chen, Chih-nan; Li, Xinliang; Lin, Julia; Rosen, Daniel; Lapatin, Sheri; Normand, Sharon-Lise; Larson, Susan; Shrout, Patrick E

2013-11-01

This study examines how communication patterns vary across racial and ethnic patient-clinician dyads in mental health intake sessions and its relation to continuance in treatment, defined as attending the next scheduled appointment. Observational study of communication patterns among ethnically/racially concordant and discordant patient-clinician dyads. Primary analysis included 93 patients with 38 clinicians in race/ethnic concordant and discordant dyads. Communication was coded using the Roter Interaction Analysis System (RIAS) and the Working Alliance Inventory Observer (WAI-O) bond scale; continuance in care was derived from chart reviews. Latino concordant dyad patients were more verbally dominant (p<.05), engaged in more patient-centered communication (p<.05) and scored higher on the (WAI-O) bond scale (all p<.05) than other groups. Latino patients had higher continuance rates than other patients in models that adjusted for non-communication variables. When communication, global affect, and therapeutic process variables were adjusted for, differences were reversed and white dyad patients had higher continuance in care rates than other dyad patients. Communication patterns seem to explain the role of ethnic concordance for continuance in care. Improve intercultural communication in cross cultural encounters appears significant for retaining minorities in care. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Diagnostic concordance between mobile interfaces and conventional workstations for emergency imaging assessment.

PubMed

Venson, José Eduardo; Bevilacqua, Fernando; Berni, Jean; Onuki, Fabio; Maciel, Anderson

2018-05-01

Mobile devices and software are now available with sufficient computing power, speed and complexity to allow for real-time interpretation of radiology exams. In this paper, we perform a multivariable user study that investigates concordance of image-based diagnoses provided using mobile devices on the one hand and conventional workstations on the other hand. We performed a between-subjects task-analysis using CT, MRI and radiography datasets. Moreover, we investigated the adequacy of the screen size, image quality, usability and the availability of the tools necessary for the analysis. Radiologists, members of several teams, participated in the experiment under real work conditions. A total of 64 studies with 93 main diagnoses were analyzed. Our results showed that 56 cases were classified with complete concordance (87.69%), 5 cases with almost complete concordance (7.69%) and 1 case (1.56%) with partial concordance. Only 2 studies presented discordance between the reports (3.07%). The main reason to explain the cause of those disagreements was the lack of multiplanar reconstruction tool in the mobile viewer. Screen size and image quality had no direct impact on the mobile diagnosis process. We concluded that for images from emergency modalities, a mobile interface provides accurate interpretation and swift response, which could benefit patients' healthcare. Copyright © 2018 Elsevier B.V. All rights reserved.

Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors

PubMed Central

van de Schoot, Rens; Broere, Joris J.; Perryck, Koen H.; Zondervan-Zwijnenburg, Mariëlle; van Loey, Nancy E.

2015-01-01

Background The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions. By means of a simulation study and an empirical example concerning posttraumatic stress symptoms (PTSS) following mechanical ventilation in burn survivors, we demonstrate the advantages and potential pitfalls of using Bayesian estimation. Methods First, we show how to specify prior distributions and by means of a sensitivity analysis we demonstrate how to check the exact influence of the prior (mis-) specification. Thereafter, we show by means of a simulation the situations in which the Bayesian approach outperforms the default, maximum likelihood and approach. Finally, we re-analyze empirical data on burn survivors which provided preliminary evidence of an aversive influence of a period of mechanical ventilation on the course of PTSS following burns. Results Not suprisingly, maximum likelihood estimation showed insufficient coverage as well as power with very small samples. Only when Bayesian analysis, in conjunction with informative priors, was used power increased to acceptable levels. As expected, we showed that the smaller the sample size the more the results rely on the prior specification. Conclusion We show that two issues often encountered during analysis of small samples, power and biased parameters, can be solved by including prior information into Bayesian analysis. We argue that the use of informative priors should always be reported together with a sensitivity analysis. PMID:25765534

Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors.

PubMed

van de Schoot, Rens; Broere, Joris J; Perryck, Koen H; Zondervan-Zwijnenburg, Mariëlle; van Loey, Nancy E

2015-01-01

Background : The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions. By means of a simulation study and an empirical example concerning posttraumatic stress symptoms (PTSS) following mechanical ventilation in burn survivors, we demonstrate the advantages and potential pitfalls of using Bayesian estimation. Methods : First, we show how to specify prior distributions and by means of a sensitivity analysis we demonstrate how to check the exact influence of the prior (mis-) specification. Thereafter, we show by means of a simulation the situations in which the Bayesian approach outperforms the default, maximum likelihood and approach. Finally, we re-analyze empirical data on burn survivors which provided preliminary evidence of an aversive influence of a period of mechanical ventilation on the course of PTSS following burns. Results : Not suprisingly, maximum likelihood estimation showed insufficient coverage as well as power with very small samples. Only when Bayesian analysis, in conjunction with informative priors, was used power increased to acceptable levels. As expected, we showed that the smaller the sample size the more the results rely on the prior specification. Conclusion : We show that two issues often encountered during analysis of small samples, power and biased parameters, can be solved by including prior information into Bayesian analysis. We argue that the use of informative priors should always be reported together with a sensitivity analysis.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

The Importance of Proving the Null

ERIC Educational Resources Information Center

Gallistel, C. R.

2009-01-01

Null hypotheses are simple, precise, and theoretically important. Conventional statistical analysis cannot support them; Bayesian analysis can. The challenge in a Bayesian analysis is to formulate a suitably vague alternative, because the vaguer the alternative is (the more it spreads out the unit mass of prior probability), the more the null is…

Prevalence of tuberculosis infection in healthcare workers of the public hospital network in Medellín, Colombia: a Bayesian approach.

PubMed

Ochoa, J; León, A L; Ramírez, I C; Lopera, C M; Bernal, E; Arbeláez, M P

2017-04-01

A latent tuberculosis infection (LTBI) prevalence survey was conducted using tuberculin skin test (TST) and Quantiferon test (QFT) in 1218 healthcare workers (HCWs) in Medellín, Colombia. In order to improve the prevalence estimates, a latent class model was built using a Bayesian approach with informative priors on the sensitivity and specificity of the TST. The proportion of concordant results (TST+,QFT+) was 41% and the discordant results contributed 27%. The marginal estimate of the prevalence P(LTBI+) was 62·1% [95% credible interval (CrI) 53·0-68·2]. The probability of LTBI+ given positive results for both tests was 99·6% (95% CrI 98·1-99·9). Sensitivity was 88·5 for TST and 74·3 for QFT, and specificity was 87·8 for TST and 97·6 for QFT. A high LTBI prevalence was found in HCWs with time-accumulated exposure in hospitals that lack control plans. In a context of intermediate tuberculosis (TB) incidence it is recommended to use only one test (either QFT or TST) in prevalence surveys or as pre-employment tests. Results will be useful to help implement TB infection control plans in hospitals where HCWs may be repeatedly exposed to unnoticed TB patients, and to inform the design of TB control policies.

Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees.

PubMed

Yang, Ziheng; Zhu, Tianqi

2018-02-20

The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.

A comparative analysis of modeled and monitored ambient hazardous air pollutants in Texas: a novel approach using concordance correlation.

PubMed

Lupo, Philip J; Symanski, Elaine

2009-11-01

Often, in studies evaluating the health effects of hazardous air pollutants (HAPs), researchers rely on ambient air levels to estimate exposure. Two potential data sources are modeled estimates from the U.S. Environmental Protection Agency (EPA) Assessment System for Population Exposure Nationwide (ASPEN) and ambient air pollutant measurements from monitoring networks. The goal was to conduct comparisons of modeled and monitored estimates of HAP levels in the state of Texas using traditional approaches and a previously unexploited method, concordance correlation analysis, to better inform decisions regarding agreement. Census tract-level ASPEN estimates and monitoring data for all HAPs throughout Texas, available from the EPA Air Quality System, were obtained for 1990, 1996, and 1999. Monitoring sites were mapped to census tracts using U.S. Census data. Exclusions were applied to restrict the monitored data to measurements collected using a common sampling strategy with minimal missing values over time. Comparisons were made for 28 HAPs in 38 census tracts located primarily in urban areas throughout Texas. For each pollutant and by year of assessment, modeled and monitored air pollutant annual levels were compared using standard methods (i.e., ratios of model-to-monitor annual levels). Concordance correlation analysis was also used, which assesses linearity and agreement while providing a formal method of statistical inference. Forty-eight percent of the median model-to-monitor values fell between 0.5 and 2, whereas only 17% of concordance correlation coefficients were significant and greater than 0.5. On the basis of concordance correlation analysis, the findings indicate there is poorer agreement when compared with the previously applied ad hoc methods to assess comparability between modeled and monitored levels of ambient HAPs.

Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum

2006-01-01

A Bayesian approach is developed for analyzing nonlinear structural equation models with nonignorable missing data. The nonignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm is used to produce the joint Bayesian estimates of…

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

ERIC Educational Resources Information Center

Levy, Roy

2011-01-01

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…

Bayesian Posterior Odds Ratios: Statistical Tools for Collaborative Evaluations

ERIC Educational Resources Information Center

Hicks, Tyler; Rodríguez-Campos, Liliana; Choi, Jeong Hoon

2018-01-01

To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices…

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration.

PubMed

Bartlett, Jonathan W; Keogh, Ruth H

2018-06-01

Bayesian approaches for handling covariate measurement error are well established and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm. For others a contributory factor is the inability of standard statistical packages to perform such Bayesian analyses. In this paper, we first give an overview of the Bayesian approach to handling covariate measurement error, and contrast it with regression calibration, arguably the most commonly adopted approach. We then argue why the Bayesian approach has a number of statistical advantages compared to regression calibration and demonstrate that implementing the Bayesian approach is usually quite feasible for the analyst. Next, we describe the closely related maximum likelihood and multiple imputation approaches and explain why we believe the Bayesian approach to generally be preferable. We then empirically compare the frequentist properties of regression calibration and the Bayesian approach through simulation studies. The flexibility of the Bayesian approach to handle both measurement error and missing data is then illustrated through an analysis of data from the Third National Health and Nutrition Examination Survey.

Concordance of Parent- and Child-Reported Physical Abuse Following Child Protective Services Investigation.

PubMed

Kobulsky, Julia M; Kepple, Nancy Jo; Holmes, Megan R; Hussey, David L

2017-02-01

Knowledge about the concordance of parent- and child-reported child physical abuse is scarce, leaving researchers and practitioners with little guidance on the implications of selecting either informant. Drawing from a 2008-2009 sample of 11- to 17-year-olds ( N = 636) from Wave 1 of the second National Survey of Child and Adolescent Well-Being, this study first examined parent-child concordance in physical abuse reporting (Parent-Child Conflict Tactic Scale). Second, it applied multivariate regression analysis to relate parent-child agreement in physical abuse to parent-reported (Child Behavior Checklist) and child-reported (Youth Self Report) child behavioral problems. Results indicate low parent-child concordance of physical abuse (κ = .145). Coreporting of physical abuse was related to clinical-level parent-reported externalizing problems ([Formula: see text] = 64.57), whereas child-only reports of physical abuse were the only agreement category related to child-reported internalizing problems ( B = 4.17, p < .001). Attribution bias theory may further understanding of reporting concordance and its implications.

Enhancing the Modeling of PFOA Pharmacokinetics with Bayesian Analysis

EPA Science Inventory

The detail sufficient to describe the pharmacokinetics (PK) for perfluorooctanoic acid (PFOA) and the methods necessary to combine information from multiple data sets are both subjects of ongoing investigation. Bayesian analysis provides tools to accommodate these goals. We exa...

Concordant and discordant alcohol, tobacco, and marijuana use as predictors of marital dissolution

PubMed Central

Leonard, Kenneth E.; Smith, Philip H.; Homish, Gregory G.

2014-01-01

Objective This study examined concordant and discrepant alcohol, tobacco, and marijuana use among couples to determine whether they predicted marital separation/divorce over nine years. Method The study recruited 634 couples as they applied for their marriage license and assessed them at that time, and re-assessed them with mailed questionnaires at their 1st, 2nd, 4th, 7th, and 9th anniversaries. Approximately 60% of the men and women were European-American, and approximately 1/3 were African-American. The frequency of drinking to intoxication and binge drinking (more than 5 drinks in an occasion) were assessed, as were the use of cigarettes and marijuana. At each assessment, each member of the couple was asked about the occurrence of marital separations and divorce. Results Bivariate analyses indicated that tobacco and marijuana use, whether discrepant or concordant, were associated with marital disruptions. However, discrepant heavy drinking was associated with disruptions but concordant heavy drinking was not. Concordant and discordant marijuana use were not associated with divorce when analyses controlled for alcohol and tobacco use. Concordant and discordant tobacco use was not associated with divorce when analyses controlled for sociodemographic and personality factors. However, discrepant alcohol use was related to divorce after controlling for the other substances in one analysis and after controlling for the sociodemographic factors in a separate analysis. Conclusions Tobacco and marijuana use were related to divorce through their associations with other variables. However, results suggested that discrepant alcohol use may lead to marital disruptions and should be addressed with couples seeking marital treatment. PMID:24128287

Concordance of HPV-DNA in cervical dysplasia or genital warts in women and their monogamous long-term male partners.

PubMed

Rob, Filip; Tachezy, Ruth; Pichlík, Tomáš; Škapa, Petr; Rob, Lukáš; Hamšíková, Eva; Šmahelová, Jana; Hercogová, Jana

2017-09-01

Transmission of human papillomavirus (HPV) is a premise for development of cervical dysplasia and genital warts (GWs). This cross-sectional study assesses concordance of HPV types present in GWs or cervical dysplasia in women and genital infection of their monogamous male partners in conjunction with seroprevalence of HPV-6, -11, -16, and -18 antibodies. Blood was taken from both women and men, as well a smear of the urogenital area of men. HPV DNA detection in women was done in fixed paraffin embedded tissues under histological control. Of 143 couples who agreed to participate in the study, 68 met inclusion criteria. Type-specific concordance was observed in 32.5% (13/40) of couples in which women had genital warts and in 32.1% (9/28) of couples in which women had cervical dysplasia. In multivariate analysis only smoking in women was associated with concordance (P < 0.05). Prevalence of HPV-specific antibodies was high in male partners, but was not associated with presence of the same HPV type on their genitals. The same type-specific HPV antibodies were detected in 81.8% of men in couples with HPV-6 concordant genital warts, but only in 14.3% of men in couples with HPV-16 concordant cervical dysplasia (P < 0.01). These results suggest that type-specific HPV concordance in genital warts and cervical dysplasia lesions of women and genital infection of their male partners is common and similar. Higher seroconversion in couples with HPV-6 concordant genital warts compared with couples with HPV-16 concordant cervical dysplasia may be explained by viral load exposure. © 2017 Wiley Periodicals, Inc.

Exact and efficient simulation of concordant computation

NASA Astrophysics Data System (ADS)

Cable, Hugo; Browne, Daniel E.

2015-11-01

Concordant computation is a circuit-based model of quantum computation for mixed states, that assumes that all correlations within the register are discord-free (i.e. the correlations are essentially classical) at every step of the computation. The question of whether concordant computation always admits efficient simulation by a classical computer was first considered by Eastin in arXiv:quant-ph/1006.4402v1, where an answer in the affirmative was given for circuits consisting only of one- and two-qubit gates. Building on this work, we develop the theory of classical simulation of concordant computation. We present a new framework for understanding such computations, argue that a larger class of concordant computations admit efficient simulation, and provide alternative proofs for the main results of arXiv:quant-ph/1006.4402v1 with an emphasis on the exactness of simulation which is crucial for this model. We include detailed analysis of the arithmetic complexity for solving equations in the simulation, as well as extensions to larger gates and qudits. We explore the limitations of our approach, and discuss the challenges faced in developing efficient classical simulation algorithms for all concordant computations.

Bayesian statistics: estimating plant demographic parameters

Treesearch

James S. Clark; Michael Lavine

2001-01-01

There are times when external information should be brought tobear on an ecological analysis. experiments are never conducted in a knowledge-free context. The inference we draw from an observation may depend on everything else we know about the process. Bayesian analysis is a method that brings outside evidence into the analysis of experimental and observational data...

Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values

ERIC Educational Resources Information Center

Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel

2012-01-01

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…

Bayesian Structural Equation Modeling: A More Flexible Representation of Substantive Theory

ERIC Educational Resources Information Center

Muthen, Bengt; Asparouhov, Tihomir

2012-01-01

This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed…

BCM: toolkit for Bayesian analysis of Computational Models using samplers.

PubMed

Thijssen, Bram; Dijkstra, Tjeerd M H; Heskes, Tom; Wessels, Lodewyk F A

2016-10-21

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model. We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved. BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.

A tribal level phylogeny of Lake Tanganyika cichlid fishes based on a genomic multi-marker approach

PubMed Central

Meyer, Britta S.; Matschiner, Michael; Salzburger, Walter

2015-01-01

The species-flocks of cichlid fishes in the East African Great Lakes Victoria, Malawi and Tanganyika constitute the most diverse extant adaptive radiations in vertebrates. Lake Tanganyika, the oldest of the lakes, harbors the morphologically and genetically most diverse assemblage of cichlids and contains the highest number of endemic cichlid genera of all African lakes. Based on morphological grounds, the Tanganyikan cichlid species have been grouped into 12–16 distinct lineages, so-called tribes. While the monophyly of most of the tribes is well established, the phylogenetic relationships among the tribes remain largely elusive. Here, we present a new tribal level phylogenetic hypothesis for the cichlid fishes of Lake Tanganyika that is based on the so far largest set of nuclear markers and a total alignment length of close to 18 kb. Using next-generation amplicon sequencing with the 454 pyrosequencing technology, we compiled a dataset consisting of 42 nuclear loci in 45 East African cichlid species, which we subjected to maximum likelihood and Bayesian inference phylogenetic analyses. We analyzed the entire concatenated dataset and each marker individually, and performed a Bayesian concordance analysis and gene tree discordance tests. Overall, we find strong support for a position of the Oreochromini, Boulengerochromini, Bathybatini and Trematocarini outside of a clade combining the substrate spawning Lamprologini and the mouthbrooding tribes of the ‘H-lineage’, which are both strongly supported to be monophyletic. The Eretmodini are firmly placed within the ‘H-lineage’, as sister-group to the most species-rich tribe of cichlids, the Haplochromini. The phylogenetic relationships at the base of the ‘H-lineage’ received less support, which is likely due to high speciation rates in the early phase of the radiation. Discordance among gene trees and marker sets further suggests the occurrence of past hybridization and/or incomplete lineage sorting in the cichlid fishes of Lake Tanganyika. PMID:25433288

Gene trees, species trees, and morphology converge on a similar phylogeny of living gars (Actinopterygii: Holostei: Lepisosteidae), an ancient clade of ray-finned fishes.

PubMed

Wright, Jeremy J; David, Solomon R; Near, Thomas J

2012-06-01

Extant gars represent the remaining members of a formerly diverse assemblage of ancient ray-finned fishes and have been the subject of multiple phylogenetic analyses using morphological data. Here, we present the first hypothesis of phylogenetic relationships among living gar species based on molecular data, through the examination of gene tree heterogeneity and coalescent species tree analyses of a portion of one mitochondrial (COI) and seven nuclear (ENC1, myh6, plagl2, S7 ribosomal protein intron 1, sreb2, tbr1, and zic1) genes. Individual gene trees displayed varying degrees of resolution with regards to species-level relationships, and the gene trees inferred from COI and the S7 intron were the only two that were completely resolved. Coalescent species tree analyses of nuclear genes resulted in a well-resolved and strongly supported phylogenetic tree of living gar species, for which Bayesian posterior node support was further improved by the inclusion of the mitochondrial gene. Species-level relationships among gars inferred from our molecular data set were highly congruent with previously published morphological phylogenies, with the exception of the placement of two species, Lepisosteus osseus and L. platostomus. Re-examination of the character coding used by previous authors provided partial resolution of this topological discordance, resulting in broad concordance in the phylogenies inferred from individual genes, the coalescent species tree analysis, and morphology. The completely resolved phylogeny inferred from the molecular data set with strong Bayesian posterior support at all nodes provided insights into the potential for introgressive hybridization and patterns of allopatric speciation in the evolutionary history of living gars, as well as a solid foundation for future examinations of functional diversification and evolutionary stasis in a "living fossil" lineage. Copyright © 2012 Elsevier Inc. All rights reserved.

Molecular phylogenetics and diversification of trap-jaw ants in the genera Anochetus and Odontomachus (Hymenoptera: Formicidae).

PubMed

Larabee, Fredrick J; Fisher, Brian L; Schmidt, Chris A; Matos-Maraví, Pável; Janda, Milan; Suarez, Andrew V

2016-10-01

Ants in the genera Anochetus and Odontomachus belong to one of the largest clades in the subfamily Ponerinae, and are one of four lineages of ants possessing spring-loaded "trap-jaws." Here we present results from the first global species-level molecular phylogenetic analysis of these trap-jaw ants, reconstructed from one mitochondrial, one ribosomal RNA, and three nuclear protein-coding genes. Bayesian and likelihood analyses strongly support reciprocal monophyly for the genera Anochetus and Odontomachus. Additionally, we found strong support for seven trap-jaw ant clades (four in Anochetus and three in Odontomachus) mostly concordant with geographic distribution. Ambiguity remains concerning the closest living non-trap-jaw ant relative of the Anochetus+Odontomachus clade, but Bayes factor hypothesis testing strongly suggests that trap-jaw ants evolved from a short mandible ancestor. Ponerine trap-jaw ants originated in the early Eocene (52.5Mya) in either South America or Southeast Asia, where they have radiated rapidly in the last 30million years, and subsequently dispersed multiple times to Africa and Australia. These results will guide future taxonomic work on the group and act as a phylogenetic framework to study the macroevolution of extreme ant mouthpart specialization. Copyright © 2016 Elsevier Inc. All rights reserved.

A divergent Cardinium found in daddy long-legs (Arachnida: Opiliones).

PubMed

Chang, Jin; Masters, Amber; Avery, Amanda; Werren, John H

2010-11-01

Recent studies indicate that a newly described bacterial endosymbiont, Cardinium, is widespread in arthropods and induces different reproductive manipulations in hosts. In this study, we used a portion of the 16S rRNA gene of the Cardinium to screen 16 Opilionid species from the suborder Palptores. We found the incidence of Cardinium in these Opiliones was significantly higher than in other pooled arthropods (31.2% versus 7.2%, P=0.007). Phylogenetic analyses using maximum parsimony (MP) and Bayesian analysis revealed two distinct clades in Opiliones. One is a divergent monophyletic clade with strong support that has so far not been found in other arthropods, and a second one contains Cardinium both from Opiliones and other arthropods. There is not complete concordance of the Cardinium strains with host phylogeny, suggesting some horizontal movement of the bacteria among Opiliones. Although the divergence in the sequenced 16S rRNA region between the Cardinium infecting Opiliones and Cardinium from other arthropods is greater than among Cardinium found in other arthropods, all are monophyletic with respect to the outgroup bacteria (endosymbionts of Acanthamoeba). Based on high pairwise genetic distances, deep branch, and a distinct phylogenetic grouping, we conclude that some Opiliones harbor a newly discovered Cardinium clade. Copyright © 2010 Elsevier Inc. All rights reserved.

Bayesian Analysis of Longitudinal Data Using Growth Curve Models

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamagami, Fumiaki; Wang, Lijuan Lijuan; Nesselroade, John R.; Grimm, Kevin J.

2007-01-01

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data…

Introduction to Bayesian statistical approaches to compositional analyses of transgenic crops 1. Model validation and setting the stage.

PubMed

Harrison, Jay M; Breeze, Matthew L; Harrigan, George G

2011-08-01

Statistical comparisons of compositional data generated on genetically modified (GM) crops and their near-isogenic conventional (non-GM) counterparts typically rely on classical significance testing. This manuscript presents an introduction to Bayesian methods for compositional analysis along with recommendations for model validation. The approach is illustrated using protein and fat data from two herbicide tolerant GM soybeans (MON87708 and MON87708×MON89788) and a conventional comparator grown in the US in 2008 and 2009. Guidelines recommended by the US Food and Drug Administration (FDA) in conducting Bayesian analyses of clinical studies on medical devices were followed. This study is the first Bayesian approach to GM and non-GM compositional comparisons. The evaluation presented here supports a conclusion that a Bayesian approach to analyzing compositional data can provide meaningful and interpretable results. We further describe the importance of method validation and approaches to model checking if Bayesian approaches to compositional data analysis are to be considered viable by scientists involved in GM research and regulation. Copyright © 2011 Elsevier Inc. All rights reserved.

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Sleep-Wake Concordance in Couples Is Inversely Associated With Cardiovascular Disease Risk Markers.

PubMed

Gunn, Heather E; Buysse, Daniel J; Matthews, Karen A; Kline, Christopher E; Cribbet, Matthew R; Troxel, Wendy M

2017-01-01

To determine whether interdependence in couples' sleep (sleep-wake concordance i.e., whether couples are awake or asleep at the same time throughout the night) is associated with two markers of cardiovascular disease (CVD) risk, ambulatory blood pressure (BP) and systemic inflammation. This community-based study is a cross-sectional analysis of 46 adult couples, aged 18-45 years, without known sleep disorders. Percent sleep-wake concordance, the independent variable, was calculated for each individual using actigraphy. Ambulatory BP monitors measured BP across 48 h. Dependent variables included mean sleep systolic BP (SBP) and diastolic BP (DBP), mean wake SBP and DBP, sleep-wake SBP and DBP ratios, and C-reactive protein (CRP). Mixed models were used and were adjusted for age, sex, education, race, and body mass index. Higher sleep-wake concordance was associated with lower sleep SBP (b = -.35, SE = .01) and DBP (b = -.22, SE = .10) and lower wake SBP (b = -.26, SE = .12; all p values < .05). Results were moderated by sex; for women, high concordance was associated with lower BP. Men and women with higher sleep-wake concordance also had lower CRP values (b = -.15, SE = .03, p < .05). Sleep-wake concordance was not associated with wake DBP or sleep/wake BP ratios. Significant findings remained after controlling for individual sleep quality, duration, and wake after sleep onset. Sleep-wake concordance was associated with sleep BP, and this association was stronger for women. Higher sleep-wake concordance was associated with lower systemic inflammation for men and women. Sleep-wake concordance may be a novel mechanism by which marital relationships are associated with long-term CVD outcomes. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

A guide to Bayesian model selection for ecologists

USGS Publications Warehouse

Hooten, Mevin B.; Hobbs, N.T.

2015-01-01

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

BATSE gamma-ray burst line search. 2: Bayesian consistency methodology

NASA Technical Reports Server (NTRS)

Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.

1994-01-01

We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.

Application of Bayesian Approach in Cancer Clinical Trial

PubMed Central

Bhattacharjee, Atanu

2014-01-01

The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior information of the data. The inference is possible to establish through posterior estimates. However, some limitations are present in this method. The objective of this work was to explore the several merits and demerits of Bayesian approach in cancer research. The review of the technique will be helpful for the clinical researcher involved in the oncology to explore the limitation and power of Bayesian techniques. PMID:29147387

Disease-Concordant Twins Empower Genetic Association Studies.

PubMed

Tan, Qihua; Li, Weilong; Vandin, Fabio

2017-01-01

Genome-wide association studies with moderate sample sizes are underpowered, especially when testing SNP alleles with low allele counts, a situation that may lead to high frequency of false-positive results and lack of replication in independent studies. Related individuals, such as twin pairs concordant for a disease, should confer increased power in genetic association analysis because of their genetic relatedness. We conducted a computer simulation study to explore the power advantage of the disease-concordant twin design, which uses singletons from disease-concordant twin pairs as cases and ordinary healthy samples as controls. We examined the power gain of the twin-based design for various scenarios (i.e., cases from monozygotic and dizygotic twin pairs concordant for a disease) and compared the power with the ordinary case-control design with cases collected from the unrelated patient population. Simulation was done by assigning various allele frequencies and allelic relative risks for different mode of genetic inheritance. In general, for achieving a power estimate of 80%, the sample sizes needed for dizygotic and monozygotic twin cases were one half and one fourth of the sample size of an ordinary case-control design, with variations depending on genetic mode. Importantly, the enriched power for dizygotic twins also applies to disease-concordant sibling pairs, which largely extends the application of the concordant twin design. Overall, our simulation revealed a high value of disease-concordant twins in genetic association studies and encourages the use of genetically related individuals for highly efficiently identifying both common and rare genetic variants underlying human complex diseases without increasing laboratory cost. © 2016 John Wiley & Sons Ltd/University College London.

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.

Spatiotemporal Bayesian analysis of Lyme disease in New York state, 1990-2000.

PubMed

Chen, Haiyan; Stratton, Howard H; Caraco, Thomas B; White, Dennis J

2006-07-01

Mapping ordinarily increases our understanding of nontrivial spatial and temporal heterogeneities in disease rates. However, the large number of parameters required by the corresponding statistical models often complicates detailed analysis. This study investigates the feasibility of a fully Bayesian hierarchical regression approach to the problem and identifies how it outperforms two more popular methods: crude rate estimates (CRE) and empirical Bayes standardization (EBS). In particular, we apply a fully Bayesian approach to the spatiotemporal analysis of Lyme disease incidence in New York state for the period 1990-2000. These results are compared with those obtained by CRE and EBS in Chen et al. (2005). We show that the fully Bayesian regression model not only gives more reliable estimates of disease rates than the other two approaches but also allows for tractable models that can accommodate more numerous sources of variation and unknown parameters.

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Bayesian Factor Analysis When Only a Sample Covariance Matrix Is Available

ERIC Educational Resources Information Center

Hayashi, Kentaro; Arav, Marina

2006-01-01

In traditional factor analysis, the variance-covariance matrix or the correlation matrix has often been a form of inputting data. In contrast, in Bayesian factor analysis, the entire data set is typically required to compute the posterior estimates, such as Bayes factor loadings and Bayes unique variances. We propose a simple method for computing…

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.

Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities.

PubMed

Schmidt, Paul; Schmid, Volker J; Gaser, Christian; Buck, Dorothea; Bührlen, Susanne; Förschler, Annette; Mühlau, Mark

2013-01-01

Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data.

Genetic identification of Theobroma cacao L. trees with high Criollo ancestry in Soconusco, Chiapas, Mexico.

PubMed

Vázquez-Ovando, J A; Molina-Freaner, F; Nuñez-Farfán, J; Ovando-Medina, I; Salvador-Figueroa, M

2014-12-12

Criollo-type cacao trees are an important pool of genes with potential to be used in cacao breeding and selection programs. For that reason, we assessed the diversity and population structure of Criollo-type trees (108 cultivars with Criollo phenotypic characteristics and 10 Criollo references) using 12 simple sequence repeat (SSR) markers. Cultivars were selected from 7 demes in the Soconusco region of southern Mexico. SSRs amplified 74 alleles with an average of 3.6 alleles per population. The overall populations showed an average observed heterozygosity of 0.28, indicating heterozygote deficiency (average fixation index F = 0.50). However, moderate allelic diversity was found within populations (Shannon index for all populations I = 0.97). Bayesian method analysis determined 2 genetic clusters (K = 2) within individuals. In concordance, an assignment test grouped 37 multilocus genotypes (including 10 references) into a first cluster (Criollo), 54 into a second (presumably Amelonado), and 27 admixed individuals unassigned at the 90% threshold likely corresponding to the Trinitario genotype. This classification was supported by the principal coordinate analysis and analysis of molecular variance, which showed 12% of variation among populations (FST = 0.123, P < 0.0001). Sampled demes sites (1- 7) in the Soconusco region did not show any evidence of clustering by geographic location, and this was supported by the Mantel test (Rxy = 0.54, P = 0.120). Individuals with high Criollo lineage planted in Soconusco farms could be an important reservoir of genes for future breeding programs searching for fine, taste, flavor, and aroma cocoa.

Concordance analysis of paired cancer antigen (CA) 15-3 and 27.29 testing.

PubMed

Lin, David C; Genzen, Jonathan R

2018-01-01

Cancer antigens (CA) 15-3 and 27.29 are used in the clinical management of many breast cancer patients. Given that immunoassays for CA 15-3 and CA 27.29 target epitopes on the same glycoprotein-Mucin 1 (MUC1)-the present analysis was conducted to evaluate the potential concordance of tumor marker results when both tests were ordered by providers on the same specimens. A retrospective limited dataset of paired CA 15-3 (Roche Diagnostics) and CA 27.29 (Siemens Diagnostics) test results was obtained from a national clinical reference laboratory. Concordance according to reference interval (RI) status and percent (%) change between consecutive test results was analyzed. 37,652 paired results from 12,470 distinct patients were obtained. The correlation between CA 15-3 and CA 27.29 results was high (correlation coefficient: Pearson, 0.967), although across the dataset a significant difference between CA 15-3 and CA 27.29 results was observed (P < 0.05). RI concordance between CA 15-3 and CA 27.29 results was observed in 93.7% of pairs (35,280 of 37,652). Correlation was also observed in the % change of CA 15-3 and CA 27.29 results between consecutive specimens for individual patients. Using doubling or halving thresholds (i.e., 100% increase or 50% decrease), concordance in % change was observed between CA 15-3 and CA 27.29 in approximately 90% of cases. Individual patient results trended similarly across both markers over time. While generally concordant, CA 15-3 and CA 27.29 results should not be used interchangeably. The present report provides no evidence for added value in performing both tests routinely for individual patients.

Bayesian Exploratory Factor Analysis

PubMed Central

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi

2014-01-01

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517

Estimating phylogenetic relationships despite discordant gene trees across loci: the species tree of a diverse species group of feather mites (Acari: Proctophyllodidae).

PubMed

Knowles, Lacey L; Klimov, Pavel B

2011-11-01

With the increased availability of multilocus sequence data, the lack of concordance of gene trees estimated for independent loci has focused attention on both the biological processes producing the discord and the methodologies used to estimate phylogenetic relationships. What has emerged is a suite of new analytical tools for phylogenetic inference--species tree approaches. In contrast to traditional phylogenetic methods that are stymied by the idiosyncrasies of gene trees, approaches for estimating species trees explicitly take into account the cause of discord among loci and, in the process, provides a direct estimate of phylogenetic history (i.e. the history of species divergence, not divergence of specific loci). We illustrate the utility of species tree estimates with an analysis of a diverse group of feather mites, the pinnatus species group (genus Proctophyllodes). Discord among four sequenced nuclear loci is consistent with theoretical expectations, given the short time separating speciation events (as evident by short internodes relative to terminal branch lengths in the trees). Nevertheless, many of the relationships are well resolved in a Bayesian estimate of the species tree; the analysis also highlights ambiguous aspects of the phylogeny that require additional loci. The broad utility of species tree approaches is discussed, and specifically, their application to groups with high speciation rates--a history of diversification with particular prevalence in host/parasite systems where species interactions can drive rapid diversification.

2D Bayesian automated tilted-ring fitting of disc galaxies in large H I galaxy surveys: 2DBAT

NASA Astrophysics Data System (ADS)

Oh, Se-Heon; Staveley-Smith, Lister; Spekkens, Kristine; Kamphuis, Peter; Koribalski, Bärbel S.

2018-01-01

We present a novel algorithm based on a Bayesian method for 2D tilted-ring analysis of disc galaxy velocity fields. Compared to the conventional algorithms based on a chi-squared minimization procedure, this new Bayesian-based algorithm suffers less from local minima of the model parameters even with highly multimodal posterior distributions. Moreover, the Bayesian analysis, implemented via Markov Chain Monte Carlo sampling, only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature will be essential when performing kinematic analysis on the large number of resolved galaxies expected to be detected in neutral hydrogen (H I) surveys with the Square Kilometre Array and its pathfinders. The so-called 2D Bayesian Automated Tilted-ring fitter (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to derive rotation curves of galaxies. We explore 2DBAT performance on (a) artificial H I data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies, and (b) Australia Telescope Compact Array H I data from the Local Volume H I Survey. We find that 2DBAT works best for well-resolved galaxies with intermediate inclinations (20° < i < 70°), complementing 3D techniques better suited to modelling inclined galaxies.

Applying Bayesian Modeling and Receiver Operating Characteristic Methodologies for Test Utility Analysis

ERIC Educational Resources Information Center

Wang, Qiu; Diemer, Matthew A.; Maier, Kimberly S.

2013-01-01

This study integrated Bayesian hierarchical modeling and receiver operating characteristic analysis (BROCA) to evaluate how interest strength (IS) and interest differentiation (ID) predicted low–socioeconomic status (SES) youth's interest-major congruence (IMC). Using large-scale Kuder Career Search online-assessment data, this study fit three…

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...

Monte Carlo Algorithms for a Bayesian Analysis of the Cosmic Microwave Background

NASA Technical Reports Server (NTRS)

Jewell, Jeffrey B.; Eriksen, H. K.; ODwyer, I. J.; Wandelt, B. D.; Gorski, K.; Knox, L.; Chu, M.

2006-01-01

A viewgraph presentation on the review of Bayesian approach to Cosmic Microwave Background (CMB) analysis, numerical implementation with Gibbs sampling, a summary of application to WMAP I and work in progress with generalizations to polarization, foregrounds, asymmetric beams, and 1/f noise is given.

Bayesian analysis of rare events

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

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T

2016-12-20

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Emotion dysregulation and dyadic conflict in depressed and typical adolescents: Evaluating concordance across psychophysiological and observational measures

PubMed Central

Crowell, Sheila E.; Baucom, Brian R.; Yaptangco, Mona; Bride, Daniel; Hsiao, Ray; McCauley, Elizabeth; Beauchaine, Theodore P.

2014-01-01

Many depressed adolescents experience difficulty regulating their emotions. These emotion regulation difficulties appear to emerge in part from socialization processes within families and then generalize to other contexts. However, emotion dysregulation is typically assessed within the individual, rather than in the social relationships that shape and maintain dysregulation. In this study, we evaluated concordance of physiological and observational measures of emotion dysregulation during interpersonal conflict, using a multilevel actor-partner interdependence model (APIM). Participants were 75 mother-daughter dyads, including 50 depressed adolescents with or without a history of self-injury, and 25 typically developing controls. Behavior dysregulation was operationalized as observed aversiveness during a conflict discussion, and physiological dysregulation was indexed by respiratory sinus arrhythmia (RSA). Results revealed different patterns of concordance for control versus depressed participants. Controls evidenced a concordant partner (between-person) effect, and showed increased physiological regulation during minutes when their partner was more aversive. In contrast, clinical dyad members displayed a concordant actor (within-person) effect, becoming simultaneously physiologically and behaviorally dysregulated. Results inform current understanding of emotion dysregulation across multiple levels of analysis. PMID:24607894

Improved inference in Bayesian segmentation using Monte Carlo sampling: application to hippocampal subfield volumetry.

PubMed

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen

2013-10-01

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures. Copyright © 2013 Elsevier B.V. All rights reserved.

Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry

PubMed Central

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van

2013-01-01

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the technique also allows one to compute informative “error bars” on the volume estimates of individual structures. PMID:23773521

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.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

PubMed

Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Is strength training associated with mortality benefits? A 15year cohort study of US older adults.

PubMed

Kraschnewski, Jennifer L; Sciamanna, Christopher N; Poger, Jennifer M; Rovniak, Liza S; Lehman, Erik B; Cooper, Amanda B; Ballentine, Noel H; Ciccolo, Joseph T

2016-06-01

The relationship between strength training (ST) behavior and mortality remains understudied in large, national samples, although smaller studies have observed that greater amounts of muscle strength are associated with lower risks of death. We aimed to understand the association between meeting ST guidelines and future mortality in an older US adult population. Data were analyzed from the 1997-2001 National Health Interview Survey (NHIS) linked to death certificate data in the National Death Index. The main independent variable was guideline-concordant ST (i.e. twice each week) and dependent variable was all-cause mortality. Covariates identified in the literature and included in our analysis were demographics, past medical history, and other health behaviors (including other physical activity). Given our aim to understand outcomes in older adults, analyses were limited to adults age 65years and older. Multivariate analysis was conducted using multiple logistic regression analysis. During the study period, 9.6% of NHIS adults age 65 and older (N=30,162) reported doing guideline-concordant ST and 31.6% died. Older adults who reported guideline-concordant ST had 46% lower odds of all-cause mortality than those who did not (adjusted odds ratio: 0.64; 95% CI: 0.57, 0.70; p<0.001). The association between ST and death remained after adjustment for past medical history and health behaviors. Although a minority of older US adults met ST recommendations, guideline-concordant ST is significantly associated with decreased overall mortality. All-cause mortality may be significantly reduced through the identification of and engagement in guideline-concordant ST interventions by older adults. Copyright © 2016 Elsevier Inc. All rights reserved.

Next Steps in Bayesian Structural Equation Models: Comments on, Variations of, and Extensions to Muthen and Asparouhov (2012)

ERIC Educational Resources Information Center

Rindskopf, David

2012-01-01

Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By…

A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report.

ERIC Educational Resources Information Center

Glas, Cees A. W.; Meijer, Rob R.

A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov Chain Monte Carlo procedure can be used to generate samples of the posterior distribution…

Bayesian Latent Class Analysis Tutorial.

PubMed

Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

2018-01-01

This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

Validation, reliability, and specificity of CliniCom™ Psychiatric Assessment Software.

PubMed

Handal, Nelson; LePage, James; Dayley, Philip; Baldwin, Barbara; Roeser, Amellia; Kay, Joni; Theobald, Heather Ann; Nellamattathil, Michael; Drotar, Scott; Weir, Connor; Tindell, Neil; Tice, Kevin

2018-07-01

The purpose of this study was to determine the specificity and reproducibility of CliniCom™ Psychiatric Assessment Software to appropriately diagnose five prevalent mental health disorders. This online assessment tool incorporates proprietary algorithms for its propensity assessment. Unlike other questionnaires, which require a survey per specific mental disorder, CliniCom can simultaneously assess multiple mental disorders for an individual. CliniCom was concordant with other commonly used assessment tools in diagnosing five prevalent disorders including: Attention Deficit and Hyperactivity Disorder, Generalized Anxiety Disorder, Major Depressive Disorder, Obsessive Compulsive Disorder, and Social Phobia. The online tool was overall 78% concordant in diagnosing the same disorder during a test-retest analysis. When subjects exhibited two, three, or four disorders, the tool was less consistent in diagnosing the same set of disorders during the test-retest analysis (53% concordant). However, if evaluated as individual disorders within subjects, the more persistent disorders had a higher rate of concordance: MDD (83.3%), ADHD (81.0%), and OCD (68.4%). This study proposes CliniCom as an online assessment tool that demonstrates specificity in identifying specific psychiatric conditions and shows reproducibility over multiple administrations. Copyright © 2018 Elsevier B.V. All rights reserved.

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Genetic and teratological considerations in the analysis of concordant and discordant abnormalities in twins.

PubMed

Gericke, G S

1986-01-18

Results from monozygotic (MZ) and dizygotic (DZ) twin research are often used in an attempt to gain a clearer understanding of the 'nature v. nurture' dilemma. Discordance between MZ twins has been considered to be environmental, and greater concordance in MZ compared with DZ pairs to be genetic. Current genetic and teratological theories considerably complicate the interpretation of concordance and discordance of abnormalities. The high rate of discordant intra-uterine death recently demonstrated in twins may profoundly influence the value of epidemiological studies usually performed in later life. Furthermore, indirect zygosity estimations based on sex ratios in DZ twins may be flawed because it is now recognized that increasing numbers of conditions are genetically heterogeneous. Emphasis is laid on problems of interpretation of discordance and concordance for developmental abnormalities in twins, and some possible mechanisms for their induction are discussed. Basic genetic concepts relevant to the expression of abnormalities in twins are outlined.

Bayesian B-spline mapping for dynamic quantitative traits.

PubMed

Xing, Jun; Li, Jiahan; Yang, Runqing; Zhou, Xiaojing; Xu, Shizhong

2012-04-01

Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

Accuracy of gap analysis habitat models in predicting physical features for wildlife-habitat associations in the southwest U.S.

USGS Publications Warehouse

Boykin, K.G.; Thompson, B.C.; Propeck-Gray, S.

2010-01-01

Despite widespread and long-standing efforts to model wildlife-habitat associations using remotely sensed and other spatially explicit data, there are relatively few evaluations of the performance of variables included in predictive models relative to actual features on the landscape. As part of the National Gap Analysis Program, we specifically examined physical site features at randomly selected sample locations in the Southwestern U.S. to assess degree of concordance with predicted features used in modeling vertebrate habitat distribution. Our analysis considered hypotheses about relative accuracy with respect to 30 vertebrate species selected to represent the spectrum of habitat generalist to specialist and categorization of site by relative degree of conservation emphasis accorded to the site. Overall comparison of 19 variables observed at 382 sample sites indicated ???60% concordance for 12 variables. Directly measured or observed variables (slope, soil composition, rock outcrop) generally displayed high concordance, while variables that required judgments regarding descriptive categories (aspect, ecological system, landform) were less concordant. There were no differences detected in concordance among taxa groups, degree of specialization or generalization of selected taxa, or land conservation categorization of sample sites with respect to all sites. We found no support for the hypothesis that accuracy of habitat models is inversely related to degree of taxa specialization when model features for a habitat specialist could be more difficult to represent spatially. Likewise, we did not find support for the hypothesis that physical features will be predicted with higher accuracy on lands with greater dedication to biodiversity conservation than on other lands because of relative differences regarding available information. Accuracy generally was similar (>60%) to that observed for land cover mapping at the ecological system level. These patterns demonstrate resilience of gap analysis deductive model processes to the type of remotely sensed or interpreted data used in habitat feature predictions. ?? 2010 Elsevier B.V.

Bayesian inference for psychology. Part II: Example applications with JASP.

PubMed

Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

2018-02-01

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

PubMed

Yalch, Matthew M

2016-03-01

Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

The resolved star formation history of M51a through successive Bayesian marginalization

NASA Astrophysics Data System (ADS)

Martínez-García, Eric E.; Bruzual, Gustavo; Magris C., Gladis; González-Lópezlira, Rosa A.

2018-02-01

We have obtained the time and space-resolved star formation history (SFH) of M51a (NGC 5194) by fitting Galaxy Evolution Explorer (GALEX), Sloan Digital Sky Survey and near-infrared pixel-by-pixel photometry to a comprehensive library of stellar population synthesis models drawn from the Synthetic Spectral Atlas of Galaxies (SSAG). We fit for each space-resolved element (pixel) an independent model where the SFH is averaged in 137 age bins, each one 100 Myr wide. We used the Bayesian Successive Priors (BSP) algorithm to mitigate the bias in the present-day spatial mass distribution. We test BSP with different prior probability distribution functions (PDFs); this exercise suggests that the best prior PDF is the one concordant with the spatial distribution of the stellar mass as inferred from the near-infrared images. We also demonstrate that varying the implicit prior PDF of the SFH in SSAG does not affect the results. By summing the contributions to the global star formation rate of each pixel, at each age bin, we have assembled the resolved SFH of the whole galaxy. According to these results, the star formation rate of M51a was exponentially increasing for the first 10 Gyr after the big bang, and then turned into an exponentially decreasing function until the present day. Superimposed, we find a main burst of star formation at t ≈ 11.9 Gyr after the big bang.

Bayesian Network Meta-Analysis for Unordered Categorical Outcomes with Incomplete Data

ERIC Educational Resources Information Center

Schmid, Christopher H.; Trikalinos, Thomas A.; Olkin, Ingram

2014-01-01

We develop a Bayesian multinomial network meta-analysis model for unordered (nominal) categorical outcomes that allows for partially observed data in which exact event counts may not be known for each category. This model properly accounts for correlations of counts in mutually exclusive categories and enables proper comparison and ranking of…

A Comparison of Imputation Methods for Bayesian Factor Analysis Models

ERIC Educational Resources Information Center

Merkle, Edgar C.

2011-01-01

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

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…

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…

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data

PubMed Central

Tian, Ting; McLachlan, Geoffrey J.; Dieters, Mark J.; Basford, Kaye E.

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances. PMID:26689369

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.

PubMed

Tian, Ting; McLachlan, Geoffrey J; Dieters, Mark J; Basford, Kaye E

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances.

Approximate string matching algorithms for limited-vocabulary OCR output correction

NASA Astrophysics Data System (ADS)

Lasko, Thomas A.; Hauser, Susan E.

2000-12-01

Five methods for matching words mistranslated by optical character recognition to their most likely match in a reference dictionary were tested on data from the archives of the National Library of Medicine. The methods, including an adaptation of the cross correlation algorithm, the generic edit distance algorithm, the edit distance algorithm with a probabilistic substitution matrix, Bayesian analysis, and Bayesian analysis on an actively thinned reference dictionary were implemented and their accuracy rates compared. Of the five, the Bayesian algorithm produced the most correct matches (87%), and had the advantage of producing scores that have a useful and practical interpretation.

Bayesian conditional-independence modeling of the AIDS epidemic in England and Wales

NASA Astrophysics Data System (ADS)

Gilks, Walter R.; De Angelis, Daniela; Day, Nicholas E.

We describe the use of conditional-independence modeling, Bayesian inference and Markov chain Monte Carlo, to model and project the HIV-AIDS epidemic in homosexual/bisexual males in England and Wales. Complexity in this analysis arises through selectively missing data, indirectly observed underlying processes, and measurement error. Our emphasis is on presentation and discussion of the concepts, not on the technicalities of this analysis, which can be found elsewhere [D. De Angelis, W.R. Gilks, N.E. Day, Bayesian projection of the the acquired immune deficiency syndrome epidemic (with discussion), Applied Statistics, in press].

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Bayesian model reduction and empirical Bayes for group (DCM) studies

PubMed Central

Friston, Karl J.; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E.; van Wijk, Bernadette C.M.; Ziegler, Gabriel; Zeidman, Peter

2016-01-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level – e.g., dynamic causal models – and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. PMID:26569570

A Pragmatic Bayesian Perspective on Correlation Analysis. The exoplanetary gravity - stellar activity case

NASA Astrophysics Data System (ADS)

Figueira, P.; Faria, J. P.; Adibekyan, V. Zh.; Oshagh, M.; Santos, N. C.

2016-11-01

We apply the Bayesian framework to assess the presence of a correlation between two quantities. To do so, we estimate the probability distribution of the parameter of interest, ρ, characterizing the strength of the correlation. We provide an implementation of these ideas and concepts using python programming language and the pyMC module in a very short (˜ 130 lines of code, heavily commented) and user-friendly program. We used this tool to assess the presence and properties of the correlation between planetary surface gravity and stellar activity level as measured by the log(R^' }_{ {HK}}) indicator. The results of the Bayesian analysis are qualitatively similar to those obtained via p-value analysis, and support the presence of a correlation in the data. The results are more robust in their derivation and more informative, revealing interesting features such as asymmetric posterior distributions or markedly different credible intervals, and allowing for a deeper exploration. We encourage the reader interested in this kind of problem to apply our code to his/her own scientific problems. The full understanding of what the Bayesian framework is can only be gained through the insight that comes by handling priors, assessing the convergence of Monte Carlo runs, and a multitude of other practical problems. We hope to contribute so that Bayesian analysis becomes a tool in the toolkit of researchers, and they understand by experience its advantages and limitations.

Bayesian analysis of time-series data under case-crossover designs: posterior equivalence and inference.

PubMed

Li, Shi; Mukherjee, Bhramar; Batterman, Stuart; Ghosh, Malay

2013-12-01

Case-crossover designs are widely used to study short-term exposure effects on the risk of acute adverse health events. While the frequentist literature on this topic is vast, there is no Bayesian work in this general area. The contribution of this paper is twofold. First, the paper establishes Bayesian equivalence results that require characterization of the set of priors under which the posterior distributions of the risk ratio parameters based on a case-crossover and time-series analysis are identical. Second, the paper studies inferential issues under case-crossover designs in a Bayesian framework. Traditionally, a conditional logistic regression is used for inference on risk-ratio parameters in case-crossover studies. We consider instead a more general full likelihood-based approach which makes less restrictive assumptions on the risk functions. Formulation of a full likelihood leads to growth in the number of parameters proportional to the sample size. We propose a semi-parametric Bayesian approach using a Dirichlet process prior to handle the random nuisance parameters that appear in a full likelihood formulation. We carry out a simulation study to compare the Bayesian methods based on full and conditional likelihood with the standard frequentist approaches for case-crossover and time-series analysis. The proposed methods are illustrated through the Detroit Asthma Morbidity, Air Quality and Traffic study, which examines the association between acute asthma risk and ambient air pollutant concentrations. © 2013, The International Biometric Society.

The Bayesian approach to reporting GSR analysis results: some first-hand experiences

NASA Astrophysics Data System (ADS)

Charles, Sebastien; Nys, Bart

2010-06-01

The use of Bayesian principles in the reporting of forensic findings has been a matter of interest for some years. Recently, also the GSR community is gradually exploring the advantages of this method, or rather approach, for writing reports. Since last year, our GSR group is adapting reporting procedures to the use of Bayesian principles. The police and magistrates find the reports more directly accessible and useful in their part of the criminal investigation. In the lab we find that, through applying the Bayesian principles, unnecessary analyses can be eliminated and thus time can be freed on the instruments.

Bayesian Analysis of Multidimensional Item Response Theory Models: A Discussion and Illustration of Three Response Style Models

ERIC Educational Resources Information Center

Leventhal, Brian C.; Stone, Clement A.

2018-01-01

Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure,…

Symptoms of Depression and Challenging Behaviours in People with Intellectual Disability: A Bayesian Analysis. Brief Report

ERIC Educational Resources Information Center

Tsiouris, John; Mann, Rachel; Patti, Paul; Sturmey, Peter

2004-01-01

Clinicians need to know the likelihood of a condition given a positive or negative diagnostic test. In this study a Bayesian analysis of the Clinical Behavior Checklist for Persons with Intellectual Disabilities (CBCPID) to predict depression in people with intellectual disability was conducted. The CBCPID was administered to 92 adults with…

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research.

PubMed

Henderson, Nicholas C; Louis, Thomas A; Wang, Chenguang; Varadhan, Ravi

2016-01-01

Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed.

Enhancements of Bayesian Blocks; Application to Large Light Curve Databases

NASA Technical Reports Server (NTRS)

Scargle, Jeff

2015-01-01

Bayesian Blocks are optimal piecewise linear representations (step function fits) of light-curves. The simple algorithm implementing this idea, using dynamic programming, has been extended to include more data modes and fitness metrics, multivariate analysis, and data on the circle (Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations, Scargle, Norris, Jackson and Chiang 2013, ApJ, 764, 167), as well as new results on background subtraction and refinement of the procedure for precise timing of transient events in sparse data. Example demonstrations will include exploratory analysis of the Kepler light curve archive in a search for "star-tickling" signals from extraterrestrial civilizations. (The Cepheid Galactic Internet, Learned, Kudritzki, Pakvasa1, and Zee, 2008, arXiv: 0809.0339; Walkowicz et al., in progress).

Estimating size and scope economies in the Portuguese water sector using the Bayesian stochastic frontier analysis.

PubMed

Carvalho, Pedro; Marques, Rui Cunha

2016-02-15

This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. Copyright © 2015 Elsevier B.V. All rights reserved.

Factors influencing consistency of triage using the Australasian Triage Scale: implications for guideline development.

PubMed

Gerdtz, Marie F; Chu, Matthew; Collins, Marnie; Considine, Julie; Crellin, Dianne; Sands, Natisha; Stewart, Carmel; Pollock, Wendy E

2009-08-01

To examine the influence of the nurse, the type of patient presentation and the level of hospital service on consistency of triage using the Australasian Triage Scale. A secondary analysis of survey data was conducted. The main study was undertaken to measure the reliability of 237 scenarios for inclusion in a national training programme. Nurses were recruited from a quota sample of Australian ED according to peer group. Analysis was performed to determine concordance: the percentage of responses in the modal triage category. Analysis of variance (anova) and Pearson correlations were used to investigate associations between the explanatory variables and concordance. A total of 42/50 (84%) participants returned questionnaires, providing 9946 scenario responses for analysis. Significant differences in concordance were observed by variables describing the type of patient presentation and level of urgency. Mean scores for the comparison group (adult pain; 70.7%) were higher than the groups involving a mental health or pregnancy presentations (61.4%; P

Morbidities, concordance, and predictors of preterm premature rupture of membranes among pregnant women at the University of Nigeria Teaching Hospital (UNTH), Enugu, Nigeria.

PubMed

Okeke, T C; Enwereji, J O; Adiri, C O; Onwuka, C I; Iferikigwe, E S

2016-01-01

Preterm premature rupture of membranes (PPROM) is a challenging complication of pregnancies and an important cause of perinatal morbidity and mortality. Management of morbidities associated with PPROM is fraught with controversy. However, women should be informed of these complications. This article aimed to review the morbidities, concordance, and predictors of PPROM over a 10-year period. This was a retrospective review of morbidities, concordance, and predictors of PPROM among pregnant women at the University of Nigeria Teaching Hospital, Enugu, Nigeria between January 1, 1999, and December 31, 2008. The morbidities, concordance, and predictors of PPROM were expressed by regression analysis output for PPROM. Primigravidae had the highest occurrence of PPROM. Increasing parity does not significantly influence the incidence of PPROM. The concordance and predictors of PPROM are maternal age (P < 0.000), gestational age at PROM (P < 0.000), latency period (P < 0.000), and birth weight (P < 0.001). PPROM is a major complication of pregnancies and an important cause of perinatal morbidity and mortality. Management of these morbidities associated with PPROM poses a great challenge. However, women should be informed of these complications.

Emotion dysregulation and dyadic conflict in depressed and typical adolescents: evaluating concordance across psychophysiological and observational measures.

PubMed

Crowell, Sheila E; Baucom, Brian R; Yaptangco, Mona; Bride, Daniel; Hsiao, Ray; McCauley, Elizabeth; Beauchaine, Theodore P

2014-04-01

Many depressed adolescents experience difficulty in regulating their emotions. These emotion regulation difficulties appear to emerge in part from socialization processes within families and then generalize to other contexts. However, emotion dysregulation is typically assessed within the individual, rather than in the social relationships that shape and maintain dysregulation. In this study, we evaluated concordance of physiological and observational measures of emotion dysregulation during interpersonal conflict, using a multilevel actor-partner interdependence model (APIM). Participants were 75 mother-daughter dyads, including 50 depressed adolescents with or without a history of self-injury, and 25 typically developing controls. Behavior dysregulation was operationalized as observed aversiveness during a conflict discussion, and physiological dysregulation was indexed by respiratory sinus arrhythmia (RSA). Results revealed different patterns of concordance for control versus depressed participants. Controls evidenced a concordant partner (between-person) effect, and showed increased physiological regulation during minutes when their partner was more aversive. In contrast, clinical dyad members displayed a concordant actor (within-person) effect, becoming simultaneously physiologically and behaviorally dysregulated. Results inform current understanding of emotion dysregulation across multiple levels of analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

PubMed

Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

2017-12-01

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

[Concordance among analysts from Latin-American laboratories for rice grain appearance determination using a gallery of digital images].

PubMed

Avila, Manuel; Graterol, Eduardo; Alezones, Jesús; Criollo, Beisy; Castillo, Dámaso; Kuri, Victoria; Oviedo, Norman; Moquete, Cesar; Romero, Marbella; Hanley, Zaida; Taylor, Margie

2012-06-01

The appearance of rice grain is a key aspect in quality determination. Mainly, this analysis is performed by expert analysts through visual observation; however, due to the subjective nature of the analysis, the results may vary among analysts. In order to evaluate the concordance between analysts from Latin-American rice quality laboratories for rice grain appearance through digital images, an inter-laboratory test was performed with ten analysts and images of 90 grains captured with a high resolution scanner. Rice grains were classified in four categories including translucent, chalky, white belly, and damaged grain. Data was categorized using statistic parameters like mode and its frequency, the relative concordance, and the reproducibility parameter kappa. Additionally, a referential image gallery of typical grain for each category was constructed based on mode frequency. Results showed a Kappa value of 0.49, corresponding to a moderate reproducibility, attributable to subjectivity in the visual analysis of grain images. These results reveal the need for standardize the evaluation criteria among analysts to improve the confidence of the determination of rice grain appearance.

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Bayesian analysis of CCDM models

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

2017-09-01

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

Alveolar ridge preservation after tooth extraction: a Bayesian Network meta-analysis of grafting materials efficacy on prevention of bone height and width reduction.

PubMed

Iocca, Oreste; Farcomeni, Alessio; Pardiñas Lopez, Simon; Talib, Huzefa S

2017-01-01

To conduct a traditional meta-analysis and a Bayesian Network meta-analysis to synthesize the information coming from randomized controlled trials on different socket grafting materials and combine the resulting indirect evidence in order to make inferences on treatments that have not been compared directly. RCTs were identified for inclusion in the systematic review and subsequent statistical analysis. Bone height and width remodelling were selected as the chosen summary measures for comparison. First, a series of pairwise meta-analyses were performed and overall mean difference (MD) in mm with 95% CI was calculated between grafted versus non-grafted sockets. Then, a Bayesian Network meta-analysis was performed to draw indirect conclusions on which grafting materials can be considered most likely the best compared to the others. From the six included studies, seven comparisons were obtained. Traditional meta-analysis showed statistically significant results in favour of grafting the socket compared to no-graft both for height (MD 1.02, 95% CI 0.44-1.59, p value < 0.001) than for width (MD 1.52 95% CI 1.18-1.86, p value <0.000001) remodelling. Bayesian Network meta-analysis allowed to obtain a rank of intervention efficacy. On the basis of the results of the present analysis, socket grafting seems to be more favourable than unassisted socket healing. Moreover, Bayesian Network meta-analysis indicates that freeze-dried bone graft plus membrane is the most likely effective in the reduction of bone height remodelling. Autologous bone marrow resulted the most likely effective when width remodelling was considered. Studies with larger samples and less risk of bias should be conducted in the future in order to further strengthen the results of this analysis. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A Bayesian Analysis of a Randomized Clinical Trial Comparing Antimetabolite Therapies for Non-Infectious Uveitis.

PubMed

Browne, Erica N; Rathinam, Sivakumar R; Kanakath, Anuradha; Thundikandy, Radhika; Babu, Manohar; Lietman, Thomas M; Acharya, Nisha R

2017-02-01

To conduct a Bayesian analysis of a randomized clinical trial (RCT) for non-infectious uveitis using expert opinion as a subjective prior belief. A RCT was conducted to determine which antimetabolite, methotrexate or mycophenolate mofetil, is more effective as an initial corticosteroid-sparing agent for the treatment of intermediate, posterior, and pan-uveitis. Before the release of trial results, expert opinion on the relative effectiveness of these two medications was collected via online survey. Members of the American Uveitis Society executive committee were invited to provide an estimate for the relative decrease in efficacy with a 95% credible interval (CrI). A prior probability distribution was created from experts' estimates. A Bayesian analysis was performed using the constructed expert prior probability distribution and the trial's primary outcome. A total of 11 of the 12 invited uveitis specialists provided estimates. Eight of 11 experts (73%) believed mycophenolate mofetil is more effective. The group prior belief was that the odds of treatment success for patients taking mycophenolate mofetil were 1.4-fold the odds of those taking methotrexate (95% CrI 0.03-45.0). The odds of treatment success with mycophenolate mofetil compared to methotrexate was 0.4 from the RCT (95% confidence interval 0.1-1.2) and 0.7 (95% CrI 0.2-1.7) from the Bayesian analysis. A Bayesian analysis combining expert belief with the trial's result did not indicate preference for one drug. However, the wide credible interval leaves open the possibility of a substantial treatment effect. This suggests clinical equipoise necessary to allow a larger, more definitive RCT.

Whole genome duplication in coast redwood (Sequoia sempervirens) and its implications for explaining the rarity of polyploidy in conifers.

PubMed

Scott, Alison Dawn; Stenz, Noah W M; Ingvarsson, Pär K; Baum, David A

2016-07-01

Polyploidy is common and an important evolutionary factor in most land plant lineages, but it is rare in gymnosperms. Coast redwood (Sequoia sempervirens) is one of just two polyploid conifer species and the only hexaploid. Evidence from fossil guard cell size suggests that polyploidy in Sequoia dates to the Eocene. Numerous hypotheses about the mechanism of polyploidy and parental genome donors have been proposed, based primarily on morphological and cytological data, but it remains unclear how Sequoia became polyploid and why this lineage overcame an apparent gymnosperm barrier to whole-genome duplication (WGD). We sequenced transcriptomes and used phylogenetic inference, Bayesian concordance analysis and paralog age distributions to resolve relationships among gene copies in hexaploid coast redwood and close relatives. Our data show that hexaploidy in coast redwood is best explained by autopolyploidy or, if there was allopolyploidy, it happened within the Californian redwood clade. We found that duplicate genes have more similar sequences than expected, given the age of the inferred polyploidization. Conflict between molecular and fossil estimates of WGD can be explained if diploidization occurred very slowly following polyploidization. We extrapolate from this to suggest that the rarity of polyploidy in gymnosperms may be due to slow diploidization in this clade. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Phylogeny of Morella rubra and Its Relatives (Myricaceae) and Genetic Resources of Chinese Bayberry Using RAD Sequencing

PubMed Central

Liu, Luxian; Jin, Xinjie; Chen, Nan; Li, Xian; Li, Pan; Fu, Chengxin

2015-01-01

Phylogenetic relationships among Chinese species of Morella (Myricaceae) are unresolved. Here, we use restriction site-associated DNA sequencing (RAD-seq) to identify candidate loci that will help in determining phylogenetic relationships among Morella rubra, M. adenophora, M. nana and M. esculenta. Three methods for inferring phylogeny, maximum parsimony (MP), maximum likelihood (ML) and Bayesian concordance, were applied to data sets including as many as 4253 RAD loci with 8360 parsimony informative variable sites. All three methods significantly favored the topology of (((M. rubra, M. adenophora), M. nana), M. esculenta). Two species from North America (M. cerifera and M. pensylvanica) were placed as sister to the four Chinese species. According to BEAST analysis, we deduced speciation of M. rubra to be at about the Miocene-Pliocene boundary (5.28 Ma). Intraspecific divergence in M. rubra occurred in the late Pliocene (3.39 Ma). From pooled data, we assembled 29378, 21902 and 23552 de novo contigs with an average length of 229, 234 and 234 bp for M. rubra, M. nana and M. esculenta respectively. The contigs were used to investigate functional classification of RAD tags in a BLASTX search. Additionally, we identified 3808 unlinked SNP sites across the four populations of M. rubra and discovered genes associated with fruit ripening and senescence, fruit quality and disease/defense metabolism based on KEGG database. PMID:26431030

Bayesian Correlation Analysis for Sequence Count Data

PubMed Central

Lau, Nelson; Perkins, Theodore J.

2016-01-01

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities’ measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low—especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities’ signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset. PMID:27701449

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

Model-based Bayesian inference for ROC data analysis

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Bae, K. Ty

2013-03-01

This paper presents a study of model-based Bayesian inference to Receiver Operating Characteristics (ROC) data. The model is a simple version of general non-linear regression model. Different from Dorfman model, it uses a probit link function with a covariate variable having zero-one two values to express binormal distributions in a single formula. Model also includes a scale parameter. Bayesian inference is implemented by Markov Chain Monte Carlo (MCMC) method carried out by Bayesian analysis Using Gibbs Sampling (BUGS). Contrast to the classical statistical theory, Bayesian approach considers model parameters as random variables characterized by prior distributions. With substantial amount of simulated samples generated by sampling algorithm, posterior distributions of parameters as well as parameters themselves can be accurately estimated. MCMC-based BUGS adopts Adaptive Rejection Sampling (ARS) protocol which requires the probability density function (pdf) which samples are drawing from be log concave with respect to the targeted parameters. Our study corrects a common misconception and proves that pdf of this regression model is log concave with respect to its scale parameter. Therefore, ARS's requirement is satisfied and a Gaussian prior which is conjugate and possesses many analytic and computational advantages is assigned to the scale parameter. A cohort of 20 simulated data sets and 20 simulations from each data set are used in our study. Output analysis and convergence diagnostics for MCMC method are assessed by CODA package. Models and methods by using continuous Gaussian prior and discrete categorical prior are compared. Intensive simulations and performance measures are given to illustrate our practice in the framework of model-based Bayesian inference using MCMC method.

Systemic antibiotics in the treatment of aggressive periodontitis. A systematic review and a Bayesian Network meta-analysis.

PubMed

Rabelo, Cleverton Correa; Feres, Magda; Gonçalves, Cristiane; Figueiredo, Luciene C; Faveri, Marcelo; Tu, Yu-Kang; Chambrone, Leandro

2015-07-01

The aim of this study was to assess the effect of systemic antibiotic therapy on the treatment of aggressive periodontitis (AgP). This study was conducted and reported in accordance with the PRISMA statement. The MEDLINE, EMBASE and CENTRAL databases were searched up to June 2014 for randomized clinical trials comparing the treatment of subjects with AgP with either scaling and root planing (SRP) alone or associated with systemic antibiotics. Bayesian network meta-analysis was prepared using the Bayesian random-effects hierarchical models and the outcomes reported at 6-month post-treatment. Out of 350 papers identified, 14 studies were eligible. Greater gain in clinical attachment (CA) (mean difference [MD]: 1.08 mm; p < 0.0001) and reduction in probing depth (PD) (MD: 1.05 mm; p < 0.00001) were observed for SRP + metronidazole (Mtz), and for SRP + Mtz + amoxicillin (Amx) (MD: 0.45 mm, MD: 0.53 mm, respectively; p < 0.00001) than SRP alone/placebo. Bayesian network meta-analysis showed additional benefits in CA gain and PD reduction when SRP was associated with systemic antibiotics. SRP plus systemic antibiotics led to an additional clinical effect compared with SRP alone in the treatment of AgP. Of the antibiotic protocols available for inclusion into the Bayesian network meta-analysis, Mtz and Mtz/Amx provided to the most beneficial outcomes. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Bayesian model reduction and empirical Bayes for group (DCM) studies.

PubMed

Friston, Karl J; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E; van Wijk, Bernadette C M; Ziegler, Gabriel; Zeidman, Peter

2016-03-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Concordance Between Patient and Family Reports of Family Functioning in Bipolar I Disorder and Major Depressive Disorder

PubMed Central

Weinstock, Lauren M.; Wenze, Susan J.; Munroe, Mary K.; Miller, Ivan W.

2013-01-01

Despite the extensive literature on family functioning and mood disorders, less is known about concordance between patient- and family-reported family functioning. To address this question, adults with bipolar I disorder (BD; n = 92), major depressive disorder (MDD; n = 121), and their family members (ns = 135 and 201, respectively) were recruited from hospital sources. All patients and their family members completed the Family Assessment Device (Epstein et al., 1983). Intraclass correlation coefficients revealed that, in contrast to the moderate degree of concordance in the MDD sample, degree of concordance between patient- and family-reported family functioning was significantly weaker in BD. Subsequent analysis revealed that this discordance was driven by the reports of the child and young adolescent family members of the patients with BD. Results highlight the importance of collateral reports in the assessment of family functioning, especially among families of patients with BD, in research and treatment. PMID:23588224

Concordance between patient and family reports of family functioning in bipolar I disorder and major depressive disorder.

PubMed

Weinstock, Lauren M; Wenze, Susan J; Munroe, Mary K; Miller, Ivan W

2013-05-01

Despite the extensive literature on family functioning and mood disorders, less is known about concordance between patient- and family-reported family functioning. To address this question, adults with bipolar I disorder (BD; n = 92) or major depressive disorder (MDD; n = 121) and their family members (n = 135 and 201, respectively) were recruited from hospital sources. All patients and their family members completed the Family Assessment Device (Epstein, Baldwin, Bishop. J Marital Fam Ther. 9:171-180, 1983). Intraclass correlation coefficients revealed that, in contrast to the moderate degree of concordance in the MDD sample, degree of concordance between patient- and family-reported family functioning was significantly weaker in BD. Subsequent analysis revealed that this discordance was driven by the reports of the child and young adolescent family members of the patients with BD. Results highlight the importance of collateral reports in the assessment of family functioning, especially among families of patients with BD, in research and treatment.

Patient-centered communication, ratings of care, and concordance of patient and physician race.

PubMed

Cooper, Lisa A; Roter, Debra L; Johnson, Rachel L; Ford, Daniel E; Steinwachs, Donald M; Powe, Neil R

2003-12-02

African-American patients who visit physicians of the same race rate their medical visits as more satisfying and participatory than do those who see physicians of other races. Little research has investigated the communication process in race-concordant and race-discordant medical visits. To compare patient-physician communication in race-concordant and race-discordant visits and examine whether communication behaviors explain differences in patient ratings of satisfaction and participatory decision making. Cohort study with follow-up using previsit and postvisit surveys and audiotape analysis. 16 urban primary care practices. 252 adults (142 African-American patients and 110 white patients) receiving care from 31 physicians (of whom 18 were African-American and 13 were white). Audiotape measures of patient-centeredness, patient ratings of physicians' participatory decision-making styles, and overall satisfaction. Race-concordant visits were longer (2.15 minutes [95% CI, 0.60 to 3.71]) and had higher ratings of patient positive affect (0.55 point, [95% CI, 0.04 to 1.05]) compared with race-discordant visits. Patients in race-concordant visits were more satisfied and rated their physicians as more participatory (8.42 points [95% CI, 3.23 to 13.60]). Audiotape measures of patient-centered communication behaviors did not explain differences in participatory decision making or satisfaction between race-concordant and race-discordant visits. Race-concordant visits are longer and characterized by more patient positive affect. Previous studies link similar communication findings to continuity of care. The association between race concordance and higher patient ratings of care is independent of patient-centered communication, suggesting that other factors, such as patient and physician attitudes, may mediate the relationship. Until more evidence is available regarding the mechanisms of this relationship and the effectiveness of intercultural communication skills programs, increasing ethnic diversity among physicians may be the most direct strategy to improve health care experiences for members of ethnic minority groups.

Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board.

PubMed

Somashekhar, S P; Sepúlveda, M-J; Puglielli, S; Norden, A D; Shortliffe, E H; Rohit Kumar, C; Rauthan, A; Arun Kumar, N; Patil, P; Rhee, K; Ramya, Y

2018-02-01

Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer. Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in 2016. A blinded second review was carried out by the center's tumor board in 2016 for all cases in which there was not agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered concordant if the tumor board recommendations were designated 'recommended' or 'for consideration' by WFO. Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases. Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P ≤ 0.02; P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55-64 years. Receptor status was not found to affect concordance. Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not. This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment decision making, especially at centers where expert breast cancer resources are limited. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Examining the Association between Patient-Reported Symptoms of Attention and Memory Dysfunction with Objective Cognitive Performance: A Latent Regression Rasch Model Approach.

PubMed

Li, Yuelin; Root, James C; Atkinson, Thomas M; Ahles, Tim A

2016-06-01

Patient-reported cognition generally exhibits poor concordance with objectively assessed cognitive performance. In this article, we introduce latent regression Rasch modeling and provide a step-by-step tutorial for applying Rasch methods as an alternative to traditional correlation to better clarify the relationship of self-report and objective cognitive performance. An example analysis using these methods is also included. Introduction to latent regression Rasch modeling is provided together with a tutorial on implementing it using the JAGS programming language for the Bayesian posterior parameter estimates. In an example analysis, data from a longitudinal neurocognitive outcomes study of 132 breast cancer patients and 45 non-cancer matched controls that included self-report and objective performance measures pre- and post-treatment were analyzed using both conventional and latent regression Rasch model approaches. Consistent with previous research, conventional analysis and correlations between neurocognitive decline and self-reported problems were generally near zero. In contrast, application of latent regression Rasch modeling found statistically reliable associations between objective attention and processing speed measures with self-reported Attention and Memory scores. Latent regression Rasch modeling, together with correlation of specific self-reported cognitive domains with neurocognitive measures, helps to clarify the relationship of self-report with objective performance. While the majority of patients attribute their cognitive difficulties to memory decline, the Rash modeling suggests the importance of processing speed and initial learning. To encourage the use of this method, a step-by-step guide and programming language for implementation is provided. Implications of this method in cognitive outcomes research are discussed. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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.

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.

Embedding the results of focussed Bayesian fusion into a global context

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael

2014-05-01

Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

Phylogeny of sipunculan worms: A combined analysis of four gene regions and morphology.

PubMed

Schulze, Anja; Cutler, Edward B; Giribet, Gonzalo

2007-01-01

The intra-phyletic relationships of sipunculan worms were analyzed based on DNA sequence data from four gene regions and 58 morphological characters. Initially we analyzed the data under direct optimization using parsimony as optimality criterion. An implied alignment resulting from the direct optimization analysis was subsequently utilized to perform a Bayesian analysis with mixed models for the different data partitions. For this we applied a doublet model for the stem regions of the 18S rRNA. Both analyses support monophyly of Sipuncula and most of the same clades within the phylum. The analyses differ with respect to the relationships among the major groups but whereas the deep nodes in the direct optimization analysis generally show low jackknife support, they are supported by 100% posterior probability in the Bayesian analysis. Direct optimization has been useful for handling sequences of unequal length and generating conservative phylogenetic hypotheses whereas the Bayesian analysis under mixed models provided high resolution in the basal nodes of the tree.

Phylogeographic and population genetic structure of bighorn sheep ( Ovis canadensis ) in North American deserts.

PubMed

Buchalski, Michael R; Sacks, Benjamin N; Gille, Daphne A; Penedo, Maria Cecilia T; Ernest, Holly B; Morrison, Scott A; Boyce, Walter M

2016-06-09

Fossil data are ambiguous regarding the evolutionary origin of contemporary desert bighorn sheep ( Ovis canadensis subspecies). To address this uncertainty, we conducted phylogeographic and population genetic analyses on bighorn sheep subspecies found in southwestern North America. We analyzed 515 base pairs of mtDNA control region sequence and 39 microsatellites in 804 individuals from 58 locations. Phylogenetic analyses revealed 2 highly divergent clades concordant with Sierra Nevada ( O. c. sierrae ) and Rocky Mountain ( O. c. canadensis ) bighorn and showed that these 2 subspecies both diverged from desert bighorn prior to or during the Illinoian glaciation (~315-94 thousand years ago [kya]). Desert bighorn comprised several more recently diverged haplogroups concordant with the putative Nelson ( O. c. nelsoni ), Mexican ( O. c. mexicana ), and Peninsular ( O. c. cremnobates ) subspecies. Corresponding estimates of effective splitting times (~17-3 kya), and haplogroup ages (~85-72 kya) placed the most likely timeframe for divergence among desert bighorn subspecies somewhere within the last glacial maximum. Median-joining haplotype network and Bayesian skyline analyses both indicated that desert bighorn collectively comprised a historically large and haplotype-diverse population, which subsequently lost much of its diversity through demographic decline. Using microsatellite data, discriminant analysis of principle components (DAPC) and Bayesian clustering analyses both indicated genetic structure concordant with the geographic distribution of 3 desert subspecies. Likewise, microsatellite and mitochondrial-based F ST comparisons revealed significant fixation indices among the desert bighorn genetic clusters. We conclude these desert subspecies represent ancient lineages likely descended from separate Pleistocene refugial populations and should therefore be managed as distinct taxa to preserve maximal biodiversity. Los datos de fósiles sobre el origen evolutivo de las ovejas del desierto ( Ovis canadensis subespecies) contemporáneas son ambiguos. Para dilucidar esta incertidumbre, llevamos a cabo análisis filogeográficos y de genética de poblaciones entre cinco subespecies de ovejas del suroccidente de Norteamérica. Analizamos 515 pb de secuencia de la región control del ADN mitocondrial y 39 microsatélites en 804 ovejas de 58 localidades. Los análisis filogenéticos revelaron 2 clados altamente divergentes concordantes con ovejas de la Sierra Nevada ( O. c. sierrae ) y de las Montañas Rocosas ( O. c. canadensis ), y demostraron que estas dos subespecies divergieron antes o durante la glaciación de Illinois (315,000-94,000 años). Las ovejas del desierto formaron varios haplogrupos recientemente derivados concordantes con las subespecies de Nelson ( O. c. nelsoni ), México ( O. c. mexicana ) y peninsular ( O. c. cremnobates ). Las estimaciones correspondientes al tiempo de separación efectiva (17,000-3,000 años) y edades de haplogrupos (85,000-72,000 años) son los plazos más probables para las divergencias entre subespecies de ovejas del desierto dentro de la última glaciación máxima. Análisis de redes de haplotipos de unión de medias y análisis bayesianos de líneas de horizonte indicaron que las ovejas del desierto formaron una población históricamente grande y diversa en términos de haplotipos, que luego perdieron gran parte de su diversidad a través de un descenso demográfico. Utilizando datos de microsatélites los análisis DAPC y TESS indicaron agrupamiento genético concordante con la distribución geográfica actual de las tres subespecies. Asimismo, comparaciones de F ST con datos de microsatélites y mitocondriales revelaron índices de fijación significativos entre los grupos genéticos de ovejas del desierto. Concluimos que estas subespecies de ovejas del desierto representan linajes antiguos que probablemente descienden de poblaciones de distintos refugios del Pleistoceno, y que por lo tanto deben ser manejadas como taxones distintos para preservar su biodiversidad máxima.

Comparison of administrative/billing data to expected protocol-mandated chemotherapy exposure in children with acute myeloid leukemia: A report from the Children's Oncology Group.

PubMed

Miller, Tamara P; Troxel, Andrea B; Li, Yimei; Huang, Yuan-Shung; Alonzo, Todd A; Gerbing, Robert B; Hall, Matt; Torp, Kari; Fisher, Brian T; Bagatell, Rochelle; Seif, Alix E; Sung, Lillian; Gamis, Alan; Rubin, David; Luger, Selina; Aplenc, Richard

2015-07-01

Recently investigators have used analysis of administrative/billing datasets to answer clinical and pharmacoepidemiology questions in pediatric oncology. However, the accuracy of pharmacy data from administrative/billing datasets have not yet been evaluated. The primary objective of this study was to determine the concordance of Pediatric Health Information System (PHIS) administrative/billing chemotherapy data with Children's Oncology Group (COG) protocol-mandated chemotherapy and to assess the implications of this level of concordance for further PHIS research. Data from 384 pediatric patients (1,060 courses of chemotherapy) with acute myeloid leukemia treated on COG clinical trial AAML0531 were previously merged with PHIS data. PHIS chemotherapy administrative/billing data were reviewed for the first three courses of chemotherapy. Accuracy was assessed using three metrics: recognizability of chemotherapy pattern by course, chemotherapy administration pattern by individual medication, and concordance with the number of days of protocol-defined chemotherapy. The chemotherapy pattern was recognizable in 87.3% of courses when course-wide accuracy was assessed. Chemotherapy administration pattern varied by medication. Cytarabine had perfect concordance 70.9% of the time, daunorubicin had perfect concordance 77.4% of the time, and etoposide had perfect concordance 67.8% of the time. The accuracy of chemotherapy administrative/billing data supports the continued use of PHIS data for epidemiology studies as long as investigators perform data quality control checks and evaluate each specific medication prior to undertaking definitive analyses. © 2015 Wiley Periodicals, Inc.

Comparison between adrenal venous sampling and computed tomography in the diagnosis of primary aldosteronism and in the guidance of adrenalectomy

PubMed Central

Zhu, Limin; Zhang, Ying; Zhang, Hua; Zhou, Wenlong; Shen, Zhoujun; Zheng, Fangfang; Tang, Xiaofeng; Tao, Bo; Zhang, Jin; Lu, Xiaohong; Xu, Jianzhong; Chu, Shaoli; Zhu, Dingliang; Gao, Pingjin; Wang, Ji-Guang

2016-01-01

Abstract In our series of patients with primary aldosteronism, we compared diagnostic concordance and clinical outcomes after adrenalectomy between adrenal venous sampling (AVS) and computed tomography (CT) imaging. Our retrospective analysis included 886 patients with primary aldosteronism diagnosed in our hospital between 2005 and 2014. Of them, 269 patients with CT unilateral adrenal disease were included in the analysis on the diagnostic concordance and 126 patients with follow-up data in the analysis on clinical outcomes after adrenalectomy. Hypertension was considered cured if systolic/diastolic blood pressure (BP) was controlled (<140/90 mm Hg) without medication and improved if BP was controlled with a reduced number of antihypertensive drugs. In 269 patients with CT unilateral adrenal disease, the overall concordance rate between AVS and CT was 50.5% for lateralization on the same side. The concordance rate decreased with increasing age, with highest rate of 61% in patients aged <30 years (n = 16). In 126 patients with follow-up data after adrenalectomy, the AVS- (n = 96) and CT-guided patients (n = 30) had similar characteristics before adrenalectomy. After andrenalectomy, the AVS-guided patients had a significantly higher serum potassium concentration (4.3 ± 0.3 vs 4.0 ± 0.5 mmol/L, P = 0.04) and rate of cured and improved hypertension (98% vs 87%, P = 0.03). The AVS-guided patients (n = 50) had slightly higher cured rate than the CT-guided patients (n = 11) in those older than 50 years (26.0% vs 18.2%, P = 0.72). The age below which the cured rate in the CT-guided patients was 100% was 30 years. AVS guidance had moderate concordance with CT and slightly improved clinical outcomes after adrenalectomy. The age below which CT unilateralization achieved 100% cured rate was approximately 30 years. PMID:27684853

Species trees for the tree swallows (Genus Tachycineta): an alternative phylogenetic hypothesis to the mitochondrial gene tree.

PubMed

Dor, Roi; Carling, Matthew D; Lovette, Irby J; Sheldon, Frederick H; Winkler, David W

2012-10-01

The New World swallow genus Tachycineta comprises nine species that collectively have a wide geographic distribution and remarkable variation both within- and among-species in ecologically important traits. Existing phylogenetic hypotheses for Tachycineta are based on mitochondrial DNA sequences, thus they provide estimates of a single gene tree. In this study we sequenced multiple individuals from each species at 16 nuclear intron loci. We used gene concatenated approaches (Bayesian and maximum likelihood) as well as coalescent-based species tree inference to reconstruct phylogenetic relationships of the genus. We examined the concordance and conflict between the nuclear and mitochondrial trees and between concatenated and coalescent-based inferences. Our results provide an alternative phylogenetic hypothesis to the existing mitochondrial DNA estimate of phylogeny. This new hypothesis provides a more accurate framework in which to explore trait evolution and examine the evolution of the mitochondrial genome in this group. Copyright © 2012 Elsevier Inc. All rights reserved.

Species-Level Phylogeny and Polyploid Relationships in Hordeum (Poaceae) Inferred by Next-Generation Sequencing and In Silico Cloning of Multiple Nuclear Loci.

PubMed

Brassac, Jonathan; Blattner, Frank R

2015-09-01

Polyploidization is an important speciation mechanism in the barley genus Hordeum. To analyze evolutionary changes after allopolyploidization, knowledge of parental relationships is essential. One chloroplast and 12 nuclear single-copy loci were amplified by polymerase chain reaction (PCR) in all Hordeum plus six out-group species. Amplicons from each of 96 individuals were pooled, sheared, labeled with individual-specific barcodes and sequenced in a single run on a 454 platform. Reference sequences were obtained by cloning and Sanger sequencing of all loci for nine supplementary individuals. The 454 reads were assembled into contigs representing the 13 loci and, for polyploids, also homoeologues. Phylogenetic analyses were conducted for all loci separately and for a concatenated data matrix of all loci. For diploid taxa, a Bayesian concordance analysis and a coalescent-based dated species tree was inferred from all gene trees. Chloroplast matK was used to determine the maternal parent in allopolyploid taxa. The relative performance of different multilocus analyses in the presence of incomplete lineage sorting and hybridization was also assessed. The resulting multilocus phylogeny reveals for the first time species phylogeny and progenitor-derivative relationships of all di- and polyploid Hordeum taxa within a single analysis. Our study proves that it is possible to obtain a multilocus species-level phylogeny for di- and polyploid taxa by combining PCR with next-generation sequencing, without cloning and without creating a heavy load of sequence data. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

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

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

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.

A Bayesian test for Hardy–Weinberg equilibrium of biallelic X-chromosomal markers

PubMed Central

Puig, X; Ginebra, J; Graffelman, J

2017-01-01

The X chromosome is a relatively large chromosome, harboring a lot of genetic information. Much of the statistical analysis of X-chromosomal information is complicated by the fact that males only have one copy. Recently, frequentist statistical tests for Hardy–Weinberg equilibrium have been proposed specifically for dealing with markers on the X chromosome. Bayesian test procedures for Hardy–Weinberg equilibrium for the autosomes have been described, but Bayesian work on the X chromosome in this context is lacking. This paper gives the first Bayesian approach for testing Hardy–Weinberg equilibrium with biallelic markers at the X chromosome. Marginal and joint posterior distributions for the inbreeding coefficient in females and the male to female allele frequency ratio are computed, and used for statistical inference. The paper gives a detailed account of the proposed Bayesian test, and illustrates it with data from the 1000 Genomes project. In that implementation, a novel approach to tackle multiple testing from a Bayesian perspective through posterior predictive checks is used. PMID:28900292

Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models

NASA Astrophysics Data System (ADS)

Ardani, S.; Kaihatu, J. M.

2012-12-01

Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques, MCMC

New developments in the evolution and application of the WHO/IPCS framework on mode of action/species concordance analysis.

PubMed

Meek, M E; Boobis, A; Cote, I; Dellarco, V; Fotakis, G; Munn, S; Seed, J; Vickers, C

2014-01-01

The World Health Organization/International Programme on Chemical Safety mode of action/human relevance framework has been updated to reflect the experience acquired in its application and extend its utility to emerging areas in toxicity testing and non-testing methods. The underlying principles have not changed, but the framework's scope has been extended to enable integration of information at different levels of biological organization and reflect evolving experience in a much broader range of potential applications. Mode of action/species concordance analysis can also inform hypothesis-based data generation and research priorities in support of risk assessment. The modified framework is incorporated within a roadmap, with feedback loops encouraging continuous refinement of fit-for-purpose testing strategies and risk assessment. Important in this construct is consideration of dose-response relationships and species concordance analysis in weight of evidence. The modified Bradford Hill considerations have been updated and additionally articulated to reflect increasing experience in application for cases where the toxicological outcome of chemical exposure is known. The modified framework can be used as originally intended, where the toxicological effects of chemical exposure are known, or in hypothesizing effects resulting from chemical exposure, using information on putative key events in established modes of action from appropriate in vitro or in silico systems and other lines of evidence. This modified mode of action framework and accompanying roadmap and case examples are expected to contribute to improving transparency in explicitly addressing weight of evidence considerations in mode of action/species concordance analysis based on both conventional data sources and evolving methods. Copyright © 2013 John Wiley & Sons, Ltd. The World Health Organization retains copyright and all other rights in the manuscript of this article as submitted for publication.

Concordance Between FISH Analysis of Her-2/Neu Gene in Breast Duct Carcinoma and Corresponding Axillary Nodal Metastases: Egyptian National Cancer Institute Experience.

PubMed

Badawy, Omnia M; Hassan, Hannan; ELBakey, Heba A; Mosaad, Maha

2018-05-10

Breast cancer is a major health problem in Egypt. Her-2/Neu gene is routinely assessed for all breast cancer patients primarily by immunohistochemistry. At National Cancer Institute (NCI), Cairo University, Flourescence In Situ hybridization (FISH) analysis of Her-2/Neu gene is carried out for Her-2/Neu score 2 and for some cases of score 3 (particularly those assessed outside NCI). The test is performed essentially on the primary tumor. However, some situations require testing on corresponding lymph node metastases. There is a debate about the concordance between Her-2/Neu status in the primary tumor and synchronous lymph node metastases in various studies. The aim of this study was to test for the concordance between Her-2/Neu status in the primary breast tumor and corresponding axillary nodal metastases. This is a retrospective study in which FISH analysis of Her-2/Neu was carried out simultaneously on archived material of 50 cases previously diagnosed as invasive duct carcinoma and the corresponding nodal metastases from the Pathology Department, NCI. There was complete concordance between Her-2 status in the primary tumor and the corresponding axillary lymph node metastatic deposits in which Her-2 was amplified in 44% of the studied cohort of Egyptian patients. Her-2/Neu gene assessed by FISH analysis on synchronous lymph node metastases is strongly correlated with the primary tumor. Hence, it is justified to carry out the Her-2/Neu test on synchronous lymph nodes to decide on whether to carry out anti-Her-2/Neu target therapy. Further studies on other metastatic sites is recommended.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

Bayesian ensemble refinement by replica simulations and reweighting.

PubMed

Hummer, Gerhard; Köfinger, Jürgen

2015-12-28

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Bayesian ensemble refinement by replica simulations and reweighting

NASA Astrophysics Data System (ADS)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Interlaboratory comparison of autoradiographic DNA profiling measurements: precision and concordance.

PubMed

Duewer, D L; Lalonde, S A; Aubin, R A; Fourney, R M; Reeder, D J

1998-05-01

Knowledge of the expected uncertainty in restriction fragment length polymorphism (RFLP) measurements is required for confident exchange of such data among different laboratories. The total measurement uncertainty among all Technical Working Group for DNA Analysis Methods laboratories has previously been characterized and found to be acceptably small. Casework cell line control measurements provided by six Royal Canadian Mounted Police (RCMP) and 30 U.S. commercial, local, state, and Federal forensic laboratories enable quantitative determination of the within-laboratory precision and among-laboratory concordance components of measurement uncertainty typical of both sets of laboratories. Measurement precision is the same in the two countries for DNA fragments of size 1000 base pairs (bp) to 10,000 bp. However, the measurement concordance among the RCMP laboratories is clearly superior to that within the U.S. forensic community. This result is attributable to the use of a single analytical protocol in all RCMP laboratories. Concordance among U.S. laboratories cannot be improved through simple mathematical adjustments. Community-wide efforts focused on improved concordance may be the most efficient mechanism for further reduction of among-laboratory RFLP measurement uncertainty, should the resources required to fully evaluate potential cross-jurisdictional matches become burdensome as the number of RFLP profiles on record increases.

Making Sense of a Negative Clinical Trial Result: A Bayesian Analysis of a Clinical Trial of Lorazepam and Diazepam for Pediatric Status Epilepticus.

PubMed

Chamberlain, Daniel B; Chamberlain, James M

2017-01-01

We demonstrate the application of a Bayesian approach to a recent negative clinical trial result. A Bayesian analysis of such a trial can provide a more useful interpretation of results and can incorporate previous evidence. This was a secondary analysis of the efficacy and safety results of the Pediatric Seizure Study, a randomized clinical trial of lorazepam versus diazepam for pediatric status epilepticus. We included the published results from the only prospective pediatric study of status in a Bayesian hierarchic model, and we performed sensitivity analyses on the amount of pooling between studies. We evaluated 3 summary analyses for the results: superiority, noninferiority (margin <-10%), and practical equivalence (within ±10%). Consistent with the original study's classic analysis of study results, we did not demonstrate superiority of lorazepam over diazepam. There is a 95% probability that the true efficacy of lorazepam is in the range of 66% to 80%. For both the efficacy and safety outcomes, there was greater than 95% probability that lorazepam is noninferior to diazepam, and there was greater than 90% probability that the 2 medications are practically equivalent. The results were largely driven by the current study because of the sample sizes of our study (n=273) and the previous pediatric study (n=61). Because Bayesian analysis estimates the probability of one or more hypotheses, such an approach can provide more useful information about the meaning of the results of a negative trial outcome. In the case of pediatric status epilepticus, it is highly likely that lorazepam is noninferior and practically equivalent to diazepam. Copyright © 2016 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

A Bayesian Analysis of a Randomized Clinical Trial Comparing Antimetabolite Therapies for Non-Infectious Uveitis

PubMed Central

Browne, Erica N; Rathinam, Sivakumar R; Kanakath, Anuradha; Thundikandy, Radhika; Babu, Manohar; Lietman, Thomas M; Acharya, Nisha R

2017-01-01

Purpose To conduct a Bayesian analysis of a randomized clinical trial (RCT) for non-infectious uveitis using expert opinion as a subjective prior belief. Methods A RCT was conducted to determine which antimetabolite, methotrexate or mycophenolate mofetil, is more effective as an initial corticosteroid-sparing agent for the treatment of intermediate, posterior, and pan- uveitis. Before the release of trial results, expert opinion on the relative effectiveness of these two medications was collected via online survey. Members of the American Uveitis Society executive committee were invited to provide an estimate for the relative decrease in efficacy with a 95% credible interval (CrI). A prior probability distribution was created from experts’ estimates. A Bayesian analysis was performed using the constructed expert prior probability distribution and the trial’s primary outcome. Results 11 of 12 invited uveitis specialists provided estimates. Eight of 11 experts (73%) believed mycophenolate mofetil is more effective. The group prior belief was that the odds of treatment success for patients taking mycophenolate mofetil were 1.4-fold the odds of those taking methotrexate (95% CrI 0.03 – 45.0). The odds of treatment success with mycophenolate mofetil compared to methotrexate was 0.4 from the RCT (95% confidence interval 0.1–1.2) and 0.7 (95% CrI 0.2–1.7) from the Bayesian analysis. Conclusions A Bayesian analysis combining expert belief with the trial’s result did not indicate preference for one drug. However, the wide credible interval leaves open the possibility of a substantial treatment effect. This suggests clinical equipoise necessary to allow a larger, more definitive RCT. PMID:27982726

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA.

PubMed

Fong, Duncan K H; Kim, Sunghoon; Chen, Zhe; DeSarbo, Wayne S

2016-03-01

A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the estimation of individual level coefficients for the single-period multinomial probit model even when the available prior information is vague. We apply our new procedure to consumer purchase data and reanalyze a well-known scanner panel dataset that reveals new substantive insights. In addition, we delineate a number of advantageous features of our proposed procedure over several benchmark models. Finally, through a simulation analysis employing a fractional factorial design, we demonstrate that the results from our proposed model are quite robust with respect to differing factors across various conditions.

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

A fully Bayesian before-after analysis of permeable friction course (PFC) pavement wet weather safety.

PubMed

Buddhavarapu, Prasad; Smit, Andre F; Prozzi, Jorge A

2015-07-01

Permeable friction course (PFC), a porous hot-mix asphalt, is typically applied to improve wet weather safety on high-speed roadways in Texas. In order to warrant expensive PFC construction, a statistical evaluation of its safety benefits is essential. Generally, the literature on the effectiveness of porous mixes in reducing wet-weather crashes is limited and often inconclusive. In this study, the safety effectiveness of PFC was evaluated using a fully Bayesian before-after safety analysis. First, two groups of road segments overlaid with PFC and non-PFC material were identified across Texas; the non-PFC or reference road segments selected were similar to their PFC counterparts in terms of site specific features. Second, a negative binomial data generating process was assumed to model the underlying distribution of crash counts of PFC and reference road segments to perform Bayesian inference on the safety effectiveness. A data-augmentation based computationally efficient algorithm was employed for a fully Bayesian estimation. The statistical analysis shows that PFC is not effective in reducing wet weather crashes. It should be noted that the findings of this study are in agreement with the existing literature, although these studies were not based on a fully Bayesian statistical analysis. Our study suggests that the safety effectiveness of PFC road surfaces, or any other safety infrastructure, largely relies on its interrelationship with the road user. The results suggest that the safety infrastructure must be properly used to reap the benefits of the substantial investments. Copyright © 2015 Elsevier Ltd. All rights reserved.

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

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

Xu, Jin; Yu, Yaming; Van Dyk, David A.

2014-10-20

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use amore » principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.« less

Bayesian Techniques for Plasma Theory to Bridge the Gap Between Space and Lab Plasmas

NASA Astrophysics Data System (ADS)

Crabtree, Chris; Ganguli, Gurudas; Tejero, Erik

2017-10-01

We will show how Bayesian techniques provide a general data analysis methodology that is better suited to investigate phenomena that require a nonlinear theory for an explanation. We will provide short examples of how Bayesian techniques have been successfully used in the radiation belts to provide precise nonlinear spectral estimates of whistler mode chorus and how these techniques have been verified in laboratory plasmas. We will demonstrate how Bayesian techniques allow for the direct competition of different physical theories with data acting as the necessary arbitrator. This work is supported by the Naval Research Laboratory base program and by the National Aeronautics and Space Administration under Grant No. NNH15AZ90I.

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)

PubMed Central

2016-01-01

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitroMtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivoMtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app. PMID:27335215

Bayesian just-so stories in psychology and neuroscience.

PubMed

Bowers, Jeffrey S; Davis, Colin J

2012-05-01

According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak. This weakness relates to the many arbitrary ways that priors, likelihoods, and utility functions can be altered in order to account for the data that are obtained, making the models unfalsifiable. It further relates to the fact that Bayesian theories are rarely better at predicting data compared with alternative (and simpler) non-Bayesian theories. Second, we show that the empirical evidence for Bayesian theories in neuroscience is weaker still. There are impressive mathematical analyses showing how populations of neurons could compute in a Bayesian manner but little or no evidence that they do. Third, we challenge the general scientific approach that characterizes Bayesian theorizing in cognitive science. A common premise is that theories in psychology should largely be constrained by a rational analysis of what the mind ought to do. We question this claim and argue that many of the important constraints come from biological, evolutionary, and processing (algorithmic) considerations that have no adaptive relevance to the problem per se. In our view, these factors have contributed to the development of many Bayesian "just so" stories in psychology and neuroscience; that is, mathematical analyses of cognition that can be used to explain almost any behavior as optimal. 2012 APA, all rights reserved.

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

Myocardial blood flow quantification by Rb-82 cardiac PET/CT: A detailed reproducibility study between two semi-automatic analysis programs.

PubMed

Dunet, Vincent; Klein, Ran; Allenbach, Gilles; Renaud, Jennifer; deKemp, Robert A; Prior, John O

2016-06-01

Several analysis software packages for myocardial blood flow (MBF) quantification from cardiac PET studies exist, but they have not been compared using concordance analysis, which can characterize precision and bias separately. Reproducible measurements are needed for quantification to fully develop its clinical potential. Fifty-one patients underwent dynamic Rb-82 PET at rest and during adenosine stress. Data were processed with PMOD and FlowQuant (Lortie model). MBF and myocardial flow reserve (MFR) polar maps were quantified and analyzed using a 17-segment model. Comparisons used Pearson's correlation ρ (measuring precision), Bland and Altman limit-of-agreement and Lin's concordance correlation ρc = ρ·C b (C b measuring systematic bias). Lin's concordance and Pearson's correlation values were very similar, suggesting no systematic bias between software packages with an excellent precision ρ for MBF (ρ = 0.97, ρc = 0.96, C b = 0.99) and good precision for MFR (ρ = 0.83, ρc = 0.76, C b = 0.92). On a per-segment basis, no mean bias was observed on Bland-Altman plots, although PMOD provided slightly higher values than FlowQuant at higher MBF and MFR values (P < .0001). Concordance between software packages was excellent for MBF and MFR, despite higher values by PMOD at higher MBF values. Both software packages can be used interchangeably for quantification in daily practice of Rb-82 cardiac PET.

A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study.

PubMed

Kaplan, David; Chen, Jianshen

2012-07-01

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity score stratification, weighting, and optimal full matching. Three simulation studies and one case study are presented to elaborate the proposed two-step Bayesian propensity score approach. Results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist-based treatment effect. A slight advantage is shown for the Bayesian approach in small samples. Results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results. However, greater precision around the correct treatment effect parameter yields quite good results, with slight improvement seen with greater precision in the propensity score equation. A comparison of coverage rates for the conventional frequentist approach and proposed Bayesian approach is also provided. The case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non-informative.

MRMC analysis of agreement studies

NASA Astrophysics Data System (ADS)

Gallas, Brandon D.; Anam, Amrita; Chen, Weijie; Wunderlich, Adam; Zhang, Zhiwei

2016-03-01

The purpose of this work is to present and evaluate methods based on U-statistics to compare intra- or inter-reader agreement across different imaging modalities. We apply these methods to multi-reader multi-case (MRMC) studies. We measure reader-averaged agreement and estimate its variance accounting for the variability from readers and cases (an MRMC analysis). In our application, pathologists (readers) evaluate patient tissue mounted on glass slides (cases) in two ways. They evaluate the slides on a microscope (reference modality) and they evaluate digital scans of the slides on a computer display (new modality). In the current work, we consider concordance as the agreement measure, but many of the concepts outlined here apply to other agreement measures. Concordance is the probability that two readers rank two cases in the same order. Concordance can be estimated with a U-statistic and thus it has some nice properties: it is unbiased, asymptotically normal, and its variance is given by an explicit formula. Another property of a U-statistic is that it is symmetric in its inputs; it doesn't matter which reader is listed first or which case is listed first, the result is the same. Using this property and a few tricks while building the U-statistic kernel for concordance, we get a mathematically tractable problem and efficient software. Simulations show that our variance and covariance estimates are unbiased.

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models using Scale Mixtures of Normal Distributions

PubMed Central

Abanto-Valle, C. A.; Bandyopadhyay, D.; Lachos, V. H.; Enriquez, I.

2009-01-01

A Bayesian analysis of stochastic volatility (SV) models using the class of symmetric scale mixtures of normal (SMN) distributions is considered. In the face of non-normality, this provides an appealing robust alternative to the routine use of the normal distribution. Specific distributions examined include the normal, student-t, slash and the variance gamma distributions. Using a Bayesian paradigm, an efficient Markov chain Monte Carlo (MCMC) algorithm is introduced for parameter estimation. Moreover, the mixing parameters obtained as a by-product of the scale mixture representation can be used to identify outliers. The methods developed are applied to analyze daily stock returns data on S&P500 index. Bayesian model selection criteria as well as out-of- sample forecasting results reveal that the SV models based on heavy-tailed SMN distributions provide significant improvement in model fit as well as prediction to the S&P500 index data over the usual normal model. PMID:20730043

Bayesian Analysis of the Association between Family-Level Factors and Siblings' Dental Caries.

PubMed

Wen, A; Weyant, R J; McNeil, D W; Crout, R J; Neiswanger, K; Marazita, M L; Foxman, B

2017-07-01

We conducted a Bayesian analysis of the association between family-level socioeconomic status and smoking and the prevalence of dental caries among siblings (children from infant to 14 y) among children living in rural and urban Northern Appalachia using data from the Center for Oral Health Research in Appalachia (COHRA). The observed proportion of siblings sharing caries was significantly different from predicted assuming siblings' caries status was independent. Using a Bayesian hierarchical model, we found the inclusion of a household factor significantly improved the goodness of fit. Other findings showed an inverse association between parental education and siblings' caries and a positive association between households with smokers and siblings' caries. Our study strengthens existing evidence suggesting that increased parental education and decreased parental cigarette smoking are associated with reduced childhood caries in the household. Our results also demonstrate the value of a Bayesian approach, which allows us to include household as a random effect, thereby providing more accurate estimates than obtained using generalized linear mixed models.

Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

NASA Astrophysics Data System (ADS)

Reis, D. S.; Stedinger, J. R.; Martins, E. S.

2005-10-01

This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.

Bayesian Estimation of the True Prevalence and of the Diagnostic Test Sensitivity and Specificity of Enteropathogenic Yersinia in Finnish Pig Serum Samples.

PubMed

Vilar, M J; Ranta, J; Virtanen, S; Korkeala, H

2015-01-01

Bayesian analysis was used to estimate the pig's and herd's true prevalence of enteropathogenic Yersinia in serum samples collected from Finnish pig farms. The sensitivity and specificity of the diagnostic test were also estimated for the commercially available ELISA which is used for antibody detection against enteropathogenic Yersinia. The Bayesian analysis was performed in two steps; the first step estimated the prior true prevalence of enteropathogenic Yersinia with data obtained from a systematic review of the literature. In the second step, data of the apparent prevalence (cross-sectional study data), prior true prevalence (first step), and estimated sensitivity and specificity of the diagnostic methods were used for building the Bayesian model. The true prevalence of Yersinia in slaughter-age pigs was 67.5% (95% PI 63.2-70.9). The true prevalence of Yersinia in sows was 74.0% (95% PI 57.3-82.4). The estimates of sensitivity and specificity values of the ELISA were 79.5% and 96.9%.

A Bayesian Approach to a Multiple-Group Latent Class-Profile Analysis: The Timing of Drinking Onset and Subsequent Drinking Behaviors among U.S. Adolescents

ERIC Educational Resources Information Center

Chung, Hwan; Anthony, James C.

2013-01-01

This article presents a multiple-group latent class-profile analysis (LCPA) by taking a Bayesian approach in which a Markov chain Monte Carlo simulation is employed to achieve more robust estimates for latent growth patterns. This article describes and addresses a label-switching problem that involves the LCPA likelihood function, which has…

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

Bayesian Models for Astrophysical Data Using R, JAGS, Python, and Stan

NASA Astrophysics Data System (ADS)

Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.

2017-05-01

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

The CpG island methylator phenotype is concordant between primary colorectal carcinoma and matched distant metastases.

PubMed

Cohen, Stacey A; Yu, Ming; Baker, Kelsey; Redman, Mary; Wu, Chen; Heinzerling, Tai J; Wirtz, Ralph M; Charalambous, Elpida; Pentheroudakis, George; Kotoula, Vassiliki; Kalogeras, Konstantine T; Fountzilas, George; Grady, William M

2017-01-01

The CpG island methylator phenotype (CIMP) in stage III colon cancer (CRC) has been associated with improved survival after treatment with adjuvant irinotecan-based chemotherapy. In this analysis, we determine whether CIMP status in the primary CRC is concordant with the CIMP status of matched metastases in order to determine if assessment of CIMP status in the primary tumor can be used to predict CIMP status of metastatic disease, which is relevant for patient management as well as for understanding the biology of CIMP CRCs. We assessed the CIMP status of 70 pairs of primary CRC and matched metastases using a CRC-specific panel of five markers ( CACNA1G , IGF2 , NEUROG1 , RUNX3 , and SOCS1 ) where CIMP positive was defined as 3/5 positive markers at a percent methylated reference threshold of ≥10%. Concordance was compared using the Fisher's exact test and P  < 0.05 was considered significant. Sixty-nine of the pairs (98.6%) showed concordant CIMP status in the primary tumor and matched metastasis; five (7.0%) of the pairs were concordantly CIMP positive. Only one pair (1.4%) had divergent CIMP status, demonstrating CIMP positivity (4/5 markers positive) in the primary tumor, while the matched metastasis was CIMP negative (0 markers positive). CIMP status is generally concordant between primary CRCs and matched metastases. Thus, CIMP status in the primary tumor is maintained in matched metastases and can be used to inform CIMP-based therapy options for the metastases.

Epilepsy in twins: insights from unique historical data of William Lennox.

PubMed

Vadlamudi, L; Andermann, E; Lombroso, C T; Schachter, S C; Milne, R L; Hopper, J L; Andermann, F; Berkovic, S F

2004-04-13

To classify the Lennox twin pairs according to modern epilepsy classifications, use the classic twin model to identify which epilepsy syndromes have an inherited component, search for evidence of syndrome-specific genes, and compare concordances from Lennox's series with a contemporary Australian series. Following review of Lennox's original files describing twins with seizures from 1934 through 1958, the International League Against Epilepsy classifications of seizures and epileptic syndromes were applied to 169 pairs. Monozygous (MZ) and dizygous (DZ) pairs were subdivided into epilepsy syndromes and casewise concordances estimated. The authors excluded 26 pairs, with 71 MZ and 72 DZ pairs remaining. Seizure analysis demonstrated strong parallels between contemporary seizure classification and Lennox's terminology. Epilepsy syndrome diagnoses were made in 75%. The MZ and DZ casewise concordance estimates gave strong evidence for a major genetic influence in idiopathic generalized epilepsies (0.80 versus 0.00; n = 23). High MZ casewise concordances also supported a genetic etiology in symptomatic generalized epilepsies and febrile seizures. The pairs who were concordant for seizures usually had the same syndromic diagnoses in both twins (86% in MZ, 60% in DZ), suggesting syndrome-specific genes. Apart from partial epilepsies, the MZ casewise concordances were similar to those derived from Australian twin data. The authors were able to apply contemporary classifications to Lennox's twins. The data confirm genetic bases for common generalized epilepsies as well as febrile seizures and provide further support for syndrome-specific genes. Finally, comparable results to our Australian series were obtained, verifying the value of twin studies.

Toward an ecological analysis of Bayesian inferences: how task characteristics influence responses

PubMed Central

Hafenbrädl, Sebastian; Hoffrage, Ulrich

2015-01-01

In research on Bayesian inferences, the specific tasks, with their narratives and characteristics, are typically seen as exchangeable vehicles that merely transport the structure of the problem to research participants. In the present paper, we explore whether, and possibly how, task characteristics that are usually ignored influence participants’ responses in these tasks. We focus on both quantitative dimensions of the tasks, such as their base rates, hit rates, and false-alarm rates, as well as qualitative characteristics, such as whether the task involves a norm violation or not, whether the stakes are high or low, and whether the focus is on the individual case or on the numbers. Using a data set of 19 different tasks presented to 500 different participants who provided a total of 1,773 responses, we analyze these responses in two ways: first, on the level of the numerical estimates themselves, and second, on the level of various response strategies, Bayesian and non-Bayesian, that might have produced the estimates. We identified various contingencies, and most of the task characteristics had an influence on participants’ responses. Typically, this influence has been stronger when the numerical information in the tasks was presented in terms of probabilities or percentages, compared to natural frequencies – and this effect cannot be fully explained by a higher proportion of Bayesian responses when natural frequencies were used. One characteristic that did not seem to influence participants’ response strategy was the numerical value of the Bayesian solution itself. Our exploratory study is a first step toward an ecological analysis of Bayesian inferences, and highlights new avenues for future research. PMID:26300791

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.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

Applications of Bayesian Procrustes shape analysis to ensemble radar reflectivity nowcast verification

NASA Astrophysics Data System (ADS)

Fox, Neil I.; Micheas, Athanasios C.; Peng, Yuqiang

2016-07-01

This paper introduces the use of Bayesian full Procrustes shape analysis in object-oriented meteorological applications. In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts. This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble. The production of the ensemble mean avoids the problems of smoothing that result from simple pixel or cell averaging, while producing credible sets that retain information on ensemble spread. Also in this paper, the full Bayesian Procrustes scheme is used as an object verification tool for precipitation forecasts. This is an extension of a previously presented Procrustes shape analysis based verification approach into a full Bayesian format designed to handle the verification of precipitation forecasts that match objects from an ensemble of forecast fields to a single truth image. The methodology is tested on radar reflectivity nowcasts produced in the Warning Decision Support System - Integrated Information (WDSS-II) by varying parameters in the K-means cluster tracking scheme.

Bayesian analysis of non-homogeneous Markov chains: application to mental health data.

PubMed

Sung, Minje; Soyer, Refik; Nhan, Nguyen

2007-07-10

In this paper we present a formal treatment of non-homogeneous Markov chains by introducing a hierarchical Bayesian framework. Our work is motivated by the analysis of correlated categorical data which arise in assessment of psychiatric treatment programs. In our development, we introduce a Markovian structure to describe the non-homogeneity of transition patterns. In doing so, we introduce a logistic regression set-up for Markov chains and incorporate covariates in our model. We present a Bayesian model using Markov chain Monte Carlo methods and develop inference procedures to address issues encountered in the analyses of data from psychiatric treatment programs. Our model and inference procedures are implemented to some real data from a psychiatric treatment study. Copyright 2006 John Wiley & Sons, Ltd.

A FAST BAYESIAN METHOD FOR UPDATING AND FORECASTING HOURLY OZONE LEVELS

EPA Science Inventory

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows...

Uncertainty estimation of a complex water quality model: The influence of Box-Cox transformation on Bayesian approaches and comparison with a non-Bayesian method

NASA Astrophysics Data System (ADS)

Freni, Gabriele; Mannina, Giorgio

In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box-Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box-Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box-Cox transformation is not applied or non-calibrated parameter is used.

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.

A Development of Nonstationary Regional Frequency Analysis Model with Large-scale Climate Information: Its Application to Korean Watershed

NASA Astrophysics Data System (ADS)

Kim, Jin-Young; Kwon, Hyun-Han; Kim, Hung-Soo

2015-04-01

The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, this study aims to develop a hierarchical Bayesian model based nonstationary regional frequency analysis in that spatial patterns of the design rainfall with geographical information (e.g. latitude, longitude and altitude) are explicitly incorporated. This study assumes that the parameters of Gumbel (or GEV distribution) are a function of geographical characteristics within a general linear regression framework. Posterior distribution of the regression parameters are estimated by Bayesian Markov Chain Monte Carlo (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the distributions by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Finally, comprehensive discussion on design rainfall in the context of nonstationary will be presented. KEYWORDS: Regional frequency analysis, Nonstationary, Spatial information, Bayesian Acknowledgement This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Assessing the Relationship Between Sexual Concordance, Sexual Attractions, and Sexual Identity in Women.

PubMed

Suschinsky, Kelly D; Dawson, Samantha J; Chivers, Meredith L

2017-01-01

On average, there is a gender difference in sexual concordance, with men exhibiting greater agreement between genital and self-reported sexual arousal, relative to women. Much less is known about the substantial variation in women's sexual concordance; women's genital and self-reported sexual responses may correlate strongly and positively, not at all, or even strongly negatively. The within-gender variation in sexual concordance suggests that individual differences may be related to sexual concordance. We examined whether sexual concordance varies as a function of sexual orientation (based on self-reported sexual attractions and sexual identity labels) in a sample (N = 76) that included exclusively androphilic, predominantly androphilic, ambiphilic, and predominantly/exclusively gynephilic women. Participants viewed sexual and nonsexual stimuli that varied by actor gender while their vaginal vasocongestion and subjective sexual responses were measured. Women's sexual concordance varied as a function of their sexual attractions; women with any degree of gynephilia exhibited higher sexual concordance than exclusively androphilic women across a variety of sexual concordance measures, and these effects were demonstrated using correlation and multi-level modeling analyses. Only sexual concordance based on overall feelings of arousal varied by sexual identity, with heterosexual women exhibiting the lowest sexual concordance. Stimulus gender significantly influenced sexual concordance for most groups of women: Ambiphilic and predominantly/exclusively gynephilic women exhibited greater sexual concordance to female stimuli and exclusively androphilic women exhibited greater sexual concordance to male stimuli. These findings suggest that sexual orientation (particularly one's degree of gynephilia) may explain some of the within-gender variation seen in women's sexual concordance.

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.

Bayesian Modeling of Exposure and Airflow Using Two-Zone Models

PubMed Central

Zhang, Yufen; Banerjee, Sudipto; Yang, Rui; Lungu, Claudiu; Ramachandran, Gurumurthy

2009-01-01

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters. PMID:19403840

A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

DOE PAGES

Groth, Katrina M.; Smith, Curtis L.; Swiler, Laura P.

2014-04-05

In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existingmore » HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.« less

Patient factors associated with guideline-concordant treatment of anxiety and depression in primary care.

PubMed

Prins, Marijn A; Verhaak, Peter F M; Smolders, Mirrian; Laurant, Miranda G H; van der Meer, Klaas; Spreeuwenberg, Peter; van Marwijk, Harm W J; Penninx, Brenda W J H; Bensing, Jozien M

2010-07-01

To identify associations of patient characteristics (predisposing, enabling and need factors) with guideline-concordant care for anxiety and depression in primary care. Analysis of data from the Netherlands Study of Depression and Anxiety (NESDA). Seven hundred and twenty-one patients with a current anxiety or depressive disorder, recruited from 67 general practitioners (GPs), were included. Diagnoses according to the Diagnostic and Statistic Manual of Mental Disorders, fourth edition (DSM-IV) were made using a structured and widely validated assessment. Socio-demographic and enabling characteristics, severity of symptoms, disability, (under treatment for) chronic somatic conditions, perceived need for care, beliefs and evaluations of care were measured by questionnaires. Actual care data were derived from electronic medical records. Criteria for guideline-concordant care were based on general practice guidelines, issued by the Dutch College of General Practitioners. Two hundred and eighty-one (39%) patients received guideline-concordant care. High education level, accessibility of care, comorbidity of anxiety and depression, and severity and disability scores were positively associated with receiving guideline-concordant care in univariate analyses. In multivariate multi-level logistic regression models, significant associations with the clinical need factors disappeared. Positive evaluations of accessibility of care increased the chance (OR = 1.31; 95%-CI = 1.05-1.65; p = 0.02) of receiving guideline-concordant care, as well as perceiving any need for medication (OR = 2.99; 95%-CI = 1.84-4.85; p < 0.001), counseling (OR = 2.25; 95%-CI = 1.29-3.95; p = 0.005) or a referral (OR = 1.83; 95%-CI = 1.09-3.09; p = 0.02). A low educational level decreased the odds (OR = 0.33; 95%-CI = 0.11-0.98; p = 0.04) of receiving guideline-concordant care. This study shows that education level, accessibility of care and patients' perceived needs for care are more strongly associated with the delivery of guideline-concordant care for anxiety or depression than clinical need factors. Initiatives to improve GPs' communication skills around mental health issues, and to improve recognition of people suffering from anxiety disorders, could increase the number of patients receiving treatment for depression and anxiety in primary care.

Concordance of Results from Randomized and Observational Analyses within the Same Study: A Re-Analysis of the Women's Health Initiative Limited-Access Dataset.

PubMed

Bolland, Mark J; Grey, Andrew; Gamble, Greg D; Reid, Ian R

2015-01-01

Observational studies (OS) and randomized controlled trials (RCTs) often report discordant results. In the Women's Health Initiative Calcium and Vitamin D (WHI CaD) RCT, women were randomly assigned to CaD or placebo, but were permitted to use personal calcium and vitamin D supplements, creating a unique opportunity to compare results from randomized and observational analyses within the same study. WHI CaD was a 7-year RCT of 1g calcium/400IU vitamin D daily in 36,282 post-menopausal women. We assessed the effects of CaD on cardiovascular events, death, cancer and fracture in a randomized design- comparing CaD with placebo in 43% of women not using personal calcium or vitamin D supplements- and in a observational design- comparing women in the placebo group (44%) using personal calcium and vitamin D supplements with non-users. Incidence was assessed using Cox proportional hazards models, and results from the two study designs deemed concordant if the absolute difference in hazard ratios was ≤0.15. We also compared results from WHI CaD to those from the WHI Observational Study(WHI OS), which used similar methodology for analyses and recruited from the same population. In WHI CaD, for myocardial infarction and stroke, results of unadjusted and 6/8 covariate-controlled observational analyses (age-adjusted, multivariate-adjusted, propensity-adjusted, propensity-matched) were not concordant with the randomized design results. For death, hip and total fracture, colorectal and total cancer, unadjusted and covariate-controlled observational results were concordant with randomized results. For breast cancer, unadjusted and age-adjusted observational results were concordant with randomized results, but only 1/3 other covariate-controlled observational results were concordant with randomized results. Multivariate-adjusted results from WHI OS were concordant with randomized WHI CaD results for only 4/8 endpoints. Results of randomized analyses in WHI CaD were concordant with observational analyses for 5/8 endpoints in WHI CaD and 4/8 endpoints in WHI OS.

Concordance of Results from Randomized and Observational Analyses within the Same Study: A Re-Analysis of the Women’s Health Initiative Limited-Access Dataset

PubMed Central

Bolland, Mark J.; Grey, Andrew; Gamble, Greg D.; Reid, Ian R.

2015-01-01

Background Observational studies (OS) and randomized controlled trials (RCTs) often report discordant results. In the Women’s Health Initiative Calcium and Vitamin D (WHI CaD) RCT, women were randomly assigned to CaD or placebo, but were permitted to use personal calcium and vitamin D supplements, creating a unique opportunity to compare results from randomized and observational analyses within the same study. Methods WHI CaD was a 7-year RCT of 1g calcium/400IU vitamin D daily in 36,282 post-menopausal women. We assessed the effects of CaD on cardiovascular events, death, cancer and fracture in a randomized design- comparing CaD with placebo in 43% of women not using personal calcium or vitamin D supplements- and in a observational design- comparing women in the placebo group (44%) using personal calcium and vitamin D supplements with non-users. Incidence was assessed using Cox proportional hazards models, and results from the two study designs deemed concordant if the absolute difference in hazard ratios was ≤0.15. We also compared results from WHI CaD to those from the WHI Observational Study(WHI OS), which used similar methodology for analyses and recruited from the same population. Results In WHI CaD, for myocardial infarction and stroke, results of unadjusted and 6/8 covariate-controlled observational analyses (age-adjusted, multivariate-adjusted, propensity-adjusted, propensity-matched) were not concordant with the randomized design results. For death, hip and total fracture, colorectal and total cancer, unadjusted and covariate-controlled observational results were concordant with randomized results. For breast cancer, unadjusted and age-adjusted observational results were concordant with randomized results, but only 1/3 other covariate-controlled observational results were concordant with randomized results. Multivariate-adjusted results from WHI OS were concordant with randomized WHI CaD results for only 4/8 endpoints. Conclusions Results of randomized analyses in WHI CaD were concordant with observational analyses for 5/8 endpoints in WHI CaD and 4/8 endpoints in WHI OS. PMID:26440516

Fatigue tests on big structure assemblies of concorde aircraft

NASA Technical Reports Server (NTRS)

Nguyen, V. P.; Perrais, J. P.

1972-01-01

Fatigue tests on structural assemblies of the Concorde supersonic transport aircraft are reported. Two main sections of the aircraft were subjected to pressure, mechanical load, and thermal static tests. The types of fatigue tests conducted and the results obtained are discussed. It was concluded that on a supersonic aircraft whose structural weight is a significant part of the weight analysis, many fatigue and static strength development tests should be made and fatigue and thermal tests of the structures are absolutely necessary.

Recovery and concordance in a secure forensic psychiatry hospital - the self rated DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales.

PubMed

Davoren, Mary; Hennessy, Sarah; Conway, Catherine; Marrinan, Seamus; Gill, Pauline; Kennedy, Harry G

2015-03-28

Detention in a secure forensic psychiatric hospital may inhibit engagement and recovery. Having validated the clinician rated DUNDRUM-3 (programme completion) and DUNDRUM-4 (recovery) in a forensic hospital, we set out to draft and validate scales measuring the same programme completion and recovery items that patients could use to self-rate. Based on previous work, we hypothesised that self-rating scores might be predictors of objective progress including conditional discharge. We hypothesised also that the difference between patients' and clinicians' ratings of progress in treatment and other factors relevant to readiness for discharge (concordance) would diminish as patients neared discharge. We hypothesised also that this difference in matched scores would predict objective progress including conditional discharge. In a prospective naturalistic observational cohort study in a forensic hospital, we examined whether scores on the self-rated DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales or differences between clinician and patient ratings on the same scales (concordance) would predict moves between levels of therapeutic security and conditional discharge over the next twelve months. Both scales stratified along the recovery pathway of the hospital, but clinician ratings matched the level of therapeutic security more accurately than self ratings. The clinician rated scales predicted moves to less secure units and to more secure units and predicted conditional discharge but the self-rated scores did not. The difference between clinician and self-rated scores (concordance) predicted positive and negative moves and conditional discharge, but this was not always an independent predictor as shown by regression analysis. In regression analysis the DUNDRUM-3 predicted moves to less secure places though the HCR-20 C & R score dominated the model. Moves back to more secure places were predicted by lack of concordance on the DUNDRUM-4. Conditional discharge was predicted predominantly by the DUNDRUM-3. Patients accurately self-rate relative to other patients however their absolute ratings were consistently lower (better) than clinicians' ratings and were less accurate predictors of outcomes including conditional discharge. Quantifying concordance is a useful part of the recovery process and predicts outcomes but self-ratings are not accurate predictors.

Data Envelopment Analysis in the Presence of Measurement Error: Case Study from the National Database of Nursing Quality Indicators® (NDNQI®)

PubMed Central

Gajewski, Byron J.; Lee, Robert; Dunton, Nancy

2012-01-01

Data Envelopment Analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency (Hollingsworth, 2008), but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized (Gajewski, Lee, Bott, Piamjariyakul and Taunton, 2009; Ruggiero, 2004). We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators® (NDNQI®) to estimate nursing units’ efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible. PMID:23328796

Bayesian approach for counting experiment statistics applied to a neutrino point source analysis

NASA Astrophysics Data System (ADS)

Bose, D.; Brayeur, L.; Casier, M.; de Vries, K. D.; Golup, G.; van Eijndhoven, N.

2013-12-01

In this paper we present a model independent analysis method following Bayesian statistics to analyse data from a generic counting experiment and apply it to the search for neutrinos from point sources. We discuss a test statistic defined following a Bayesian framework that will be used in the search for a signal. In case no signal is found, we derive an upper limit without the introduction of approximations. The Bayesian approach allows us to obtain the full probability density function for both the background and the signal rate. As such, we have direct access to any signal upper limit. The upper limit derivation directly compares with a frequentist approach and is robust in the case of low-counting observations. Furthermore, it allows also to account for previous upper limits obtained by other analyses via the concept of prior information without the need of the ad hoc application of trial factors. To investigate the validity of the presented Bayesian approach, we have applied this method to the public IceCube 40-string configuration data for 10 nearby blazars and we have obtained a flux upper limit, which is in agreement with the upper limits determined via a frequentist approach. Furthermore, the upper limit obtained compares well with the previously published result of IceCube, using the same data set.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

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…

Multi-Scale Validation of a Nanodiamond Drug Delivery System and Multi-Scale Engineering Education

ERIC Educational Resources Information Center

Schwalbe, Michelle Kristin

2010-01-01

This dissertation has two primary concerns: (i) evaluating the uncertainty and prediction capabilities of a nanodiamond drug delivery model using Bayesian calibration and bias correction, and (ii) determining conceptual difficulties of multi-scale analysis from an engineering education perspective. A Bayesian uncertainty quantification scheme…

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

IMPLICATIONS OF USING ROBUST BAYESIAN ANALYSIS TO REPRESENT DIVERSE SOURCES OF UNCERTAINTY IN INTEGRATED ASSESSMENT

EPA Science Inventory

In our previous research, we showed that robust Bayesian methods can be used in environmental modeling to define a set of probability distributions for key parameters that captures the effects of expert disagreement, ambiguity, or ignorance. This entire set can then be update...

Pig Data and Bayesian Inference on Multinomial Probabilities

ERIC Educational Resources Information Center

Kern, John C.

2006-01-01

Bayesian inference on multinomial probabilities is conducted based on data collected from the game Pass the Pigs[R]. Prior information on these probabilities is readily available from the instruction manual, and is easily incorporated in a Dirichlet prior. Posterior analysis of the scoring probabilities quantifies the discrepancy between empirical…

Comparison between Measurements Obtained with three Different Perineometers

PubMed Central

Barbosa, Patrícia Brentegani; Franco, Maíra Menezes; de Oliveira Souza, Flaviane; Antônio, Flávia Ignácio; Montezuma, Thaís; Ferreira, Cristine Homsi Jorge

2009-01-01

OBJECTIVE: To analyze the results obtained in the evaluation of intra-vaginal pressure using three different brands of perineometers in nulliparous volunteers. MATERIALS AND METHODS: Twenty nulliparous women with no anatomical alterations and/or dysfunction of the pelvic floor were enrolled in our study. All the women had the ability to voluntarily contract their PFM (Pelvic Floor Muscles), as assessed by digital palpation. The intra-vaginal pressure was assessed using three different brands of perineometer (Neurodyn Evolution™, SensuPower™ and Peritron™). Each volunteer was evaluated on three alternate days by a single examiner using a single brand of perineometer on each day. In the assessment, the volunteers were required to pull (contract) their PFM in and up as strongly as possible 3 times and to sustain the contraction for 5 seconds, with an interval of 30 seconds between each pull. For the statistical analysis, a concordance correlation coefficient was used to compare the values that were obtained with each brand of perineometer. RESULTS: A moderate concordance (0.51) was found between the results from the Peritron™ and Neurodyn™ perineometers, a fair concordance (0.21) between the Peritron™ and SensuPower™ brands and a poor concordance (0.19) between the Neurodyn™ and SensuPower™ brands. CONCLUSION: The concordance of the measurements of the intra-vaginal pressure ranged from poor to moderate, suggesting that perineometers of different brands generate different results. PMID:19578656

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

PubMed

Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

2010-11-09

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library

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

Bates, Cameron Russell; Mckigney, Edward Allen

The use of Bayesian inference in data analysis has become the standard for large scienti c experiments [1, 2]. The Monte Carlo Codes Group(XCP-3) at Los Alamos has developed a simple set of algorithms currently implemented in C++ and Python to easily perform at-prior Markov Chain Monte Carlo Bayesian inference with pure Metropolis sampling. These implementations are designed to be user friendly and extensible for customization based on speci c application requirements. This document describes the algorithmic choices made and presents two use cases.

CRAFT (complete reduction to amplitude frequency table)--robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR.

PubMed

Krishnamurthy, Krish

2013-12-01

The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR-based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time-domain Bayesian approaches have been reported to be better than conventional frequency-domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time-efficient fashion - thus converting the time-domain FID to a frequency-amplitude table. CRAFT uses a two-step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.

Splicing analysis for exonic and intronic mismatch repair gene variants associated with Lynch syndrome confirms high concordance between minigene assays and patient RNA analyses

PubMed Central

van der Klift, Heleen M; Jansen, Anne M L; van der Steenstraten, Niki; Bik, Elsa C; Tops, Carli M J; Devilee, Peter; Wijnen, Juul T

2015-01-01

A subset of DNA variants causes genetic disease through aberrant splicing. Experimental splicing assays, either RT-PCR analyses of patient RNA or functional splicing reporter minigene assays, are required to evaluate the molecular nature of the splice defect. Here, we present minigene assays performed for 17 variants in the consensus splice site regions, 14 exonic variants outside these regions, and two deep intronic variants, all in the DNA mismatch-repair (MMR) genes MLH1, MSH2, MSH6, and PMS2, associated with Lynch syndrome. We also included two deep intronic variants in APC and PKD2. For one variant (MLH1 c.122A>G), our minigene assay and patient RNA analysis could not confirm the previously reported aberrant splicing. The aim of our study was to further investigate the concordance between minigene splicing assays and patient RNA analyses. For 30 variants results from patient RNA analyses were available, either performed by our laboratory or presented in literature. Some variants were deliberately included in this study because they resulted in multiple aberrant transcripts in patient RNA analysis, or caused a splice effect other than the prevalent exon skip. While both methods were completely concordant in the assessment of splice effects, four variants exhibited major differences in aberrant splice patterns. Based on the present and earlier studies, together showing an almost 100% concordance of minigene assays with patient RNA analyses, we discuss the weight given to minigene splicing assays in the current criteria proposed by InSiGHT for clinical classification of MMR variants. PMID:26247049

Bayesian coronal seismology

NASA Astrophysics Data System (ADS)

Arregui, Iñigo

2018-01-01

In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.

Bayesian evidence computation for model selection in non-linear geoacoustic inference problems.

PubMed

Dettmer, Jan; Dosso, Stan E; Osler, John C

2010-12-01

This paper applies a general Bayesian inference approach, based on Bayesian evidence computation, to geoacoustic inversion of interface-wave dispersion data. Quantitative model selection is carried out by computing the evidence (normalizing constants) for several model parameterizations using annealed importance sampling. The resulting posterior probability density estimate is compared to estimates obtained from Metropolis-Hastings sampling to ensure consistent results. The approach is applied to invert interface-wave dispersion data collected on the Scotian Shelf, off the east coast of Canada for the sediment shear-wave velocity profile. Results are consistent with previous work on these data but extend the analysis to a rigorous approach including model selection and uncertainty analysis. The results are also consistent with core samples and seismic reflection measurements carried out in the area.

A Bayesian Approach for Evaluation of Determinants of Health System Efficiency Using Stochastic Frontier Analysis and Beta Regression.

PubMed

Şenel, Talat; Cengiz, Mehmet Ali

2016-01-01

In today's world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems. This study investigates the most important determinants of healthcare efficiency for OECD countries using second stage approach for Bayesian Stochastic Frontier Analysis (BSFA). There are two steps in this study. First we measure 29 OECD countries' healthcare efficiency by BSFA using the data from the OECD Health Database. At second stage, we expose the multiple relationships between the healthcare efficiency and characteristics of healthcare systems across OECD countries using Bayesian beta regression.

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.

Applications of Bayesian spectrum representation in acoustics

NASA Astrophysics Data System (ADS)

Botts, Jonathan M.

This dissertation utilizes a Bayesian inference framework to enhance the solution of inverse problems where the forward model maps to acoustic spectra. A Bayesian solution to filter design inverts a acoustic spectra to pole-zero locations of a discrete-time filter model. Spatial sound field analysis with a spherical microphone array is a data analysis problem that requires inversion of spatio-temporal spectra to directions of arrival. As with many inverse problems, a probabilistic analysis results in richer solutions than can be achieved with ad-hoc methods. In the filter design problem, the Bayesian inversion results in globally optimal coefficient estimates as well as an estimate the most concise filter capable of representing the given spectrum, within a single framework. This approach is demonstrated on synthetic spectra, head-related transfer function spectra, and measured acoustic reflection spectra. The Bayesian model-based analysis of spatial room impulse responses is presented as an analogous problem with equally rich solution. The model selection mechanism provides an estimate of the number of arrivals, which is necessary to properly infer the directions of simultaneous arrivals. Although, spectrum inversion problems are fairly ubiquitous, the scope of this dissertation has been limited to these two and derivative problems. The Bayesian approach to filter design is demonstrated on an artificial spectrum to illustrate the model comparison mechanism and then on measured head-related transfer functions to show the potential range of application. Coupled with sampling methods, the Bayesian approach is shown to outperform least-squares filter design methods commonly used in commercial software, confirming the need for a global search of the parameter space. The resulting designs are shown to be comparable to those that result from global optimization methods, but the Bayesian approach has the added advantage of a filter length estimate within the same unified framework. The application to reflection data is useful for representing frequency-dependent impedance boundaries in finite difference acoustic simulations. Furthermore, since the filter transfer function is a parametric model, it can be modified to incorporate arbitrary frequency weighting and account for the band-limited nature of measured reflection spectra. Finally, the model is modified to compensate for dispersive error in the finite difference simulation, from the filter design process. Stemming from the filter boundary problem, the implementation of pressure sources in finite difference simulation is addressed in order to assure that schemes properly converge. A class of parameterized source functions is proposed and shown to offer straightforward control of residual error in the simulation. Guided by the notion that the solution to be approximated affects the approximation error, sources are designed which reduce residual dispersive error to the size of round-off errors. The early part of a room impulse response can be characterized by a series of isolated plane waves. Measured with an array of microphones, plane waves map to a directional response of the array or spatial intensity map. Probabilistic inversion of this response results in estimates of the number and directions of image source arrivals. The model-based inversion is shown to avoid ambiguities associated with peak-finding or inspection of the spatial intensity map. For this problem, determining the number of arrivals in a given frame is critical for properly inferring the state of the sound field. This analysis is effectively compression of the spatial room response, which is useful for analysis or encoding of the spatial sound field. Parametric, model-based formulations of these problems enhance the solution in all cases, and a Bayesian interpretation provides a principled approach to model comparison and parameter estimation. v

Bayesian Sensitivity Analysis of Statistical Models with Missing Data

PubMed Central

ZHU, HONGTU; IBRAHIM, JOSEPH G.; TANG, NIANSHENG

2013-01-01

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures. PMID:24753718

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

Quality of Antiepileptic Treatment Among Older Medicare Beneficiaries With Epilepsy: A Retrospective Claims Data Analysis.

PubMed

Pisu, Maria; Richman, Joshua; Piper, Kendra; Martin, Roy; Funkhouser, Ellen; Dai, Chen; Juarez, Lucia; Szaflarski, Jerzy P; Faught, Edward

2017-07-01

Enzyme-inducing antiepileptic drugs (EI-AEDs) are not recommended for older adults with epilepsy. Quality Indicator for Epilepsy Treatment 9 (QUIET-9) states that new patients should not receive EI-AEDs as first line of treatment. In light of reported racial/ethnic disparities in epilepsy care, we investigated EI-AED use and QUIET-9 concordance across major racial/ethnic groups of Medicare beneficiaries. Retrospective analyses of 2008-2010 Medicare claims for a 5% random sample of beneficiaries 67 years old and above in 2009 augmented for minority representation. Logistic regressions examined QUIET-9 concordance differences by race/ethnicity adjusting for individual, socioeconomic, and geography factors. Epilepsy prevalent (≥1 International Classification of Disease-version 9 code 345.x or ≥2 International Classification of Disease-version 9 code 780.3x, ≥1 AED), and new (same as prevalent+no seizure/epilepsy events nor AEDs in 365 d before index event) cases. Use of EI-AEDs and QUIET-9 concordance (no EI-AEDs for the first 2 AEDs). Cases were 21% white, 58% African American, 12% Hispanic, 6% Asian, 2% American Indian/Alaskan Native. About 65% of prevalent, 43.6% of new cases, used EI-AEDs. QUIET-9 concordance was found for 71% Asian, 65% white, 61% Hispanic, 57% African American, 55% American Indian/Alaskan new cases: racial/ethnic differences were not significant in adjusted model. Beneficiaries without neurology care, in deductible drug benefit phase, or in high poverty areas were less likely to have QUIET-9 concordant care. EI-AED use is high, and concordance with recommendations low, among all racial/ethnic groups of older adults with epilepsy. Potential socioeconomic disparities and drug coverage plans may affect treatment quality and opportunities to live well with epilepsy.

Guidelines-concordant empiric antimicrobial therapy and mortality in patients with severe community-acquired pneumonia requiring mechanical ventilation.

PubMed

Sakamoto, Yukiyo; Yamauchi, Yasuhiro; Yasunaga, Hideo; Takeshima, Hideyuki; Hasegawa, Wakae; Jo, Taisuke; Matsui, Hiroki; Fushimi, Kiyohide; Nagase, Takahide

2017-01-01

Community-acquired pneumonia (CAP) has high morbidity and mortality among adults. Several clinical guidelines recommend prompt administration of combined antimicrobial therapy. However, the association between guidelines concordance and mortality in patients with severe pneumonia remains unclear. The present study aimed to examine the impact of guidelines-concordant empiric antimicrobial therapy on 7-day mortality in patients with extremely severe pneumonia who required mechanical ventilation at admission, using a nationwide inpatient database in Japan. Data of CAP patients aged over 20 years who required mechanical ventilation at admission between April 2012 and March 2014 were retrospectively analyzed. Multivariable logistic regression analysis was performed to examine the association between guidelines-concordant empiric antimicrobial therapy and all-cause 7-day mortality, with adjustment for patient backgrounds and pneumonia severity. There were a total of 3719 eligible patients, 836 (22.5%) of whom received guidelines-concordant combination therapy. Overall, 7-day mortality was 29.5%. Higher 7-day mortality was associated with advanced age, confusion, lower systolic blood pressure, malignant tumor or immunocompromised state, and C-reactive protein ≥20mg/dl or infiltration occupying two-thirds of one lung on chest radiography. After adjustment for these variables, guidelines-concordant combined antimicrobial therapy was associated with significantly lower 7-day mortality (odds ratio: 0.78; 95% confidence interval: 0.65-0.95; P=0.013). Adherence to initial empiric treatment as recommended by the guidelines was associated with better short-term prognosis in patients with extremely severe pneumonia who required mechanical ventilation on hospital admission. Copyright © 2016 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Concordance and discordance between measured and perceived balance and the effect on gait speed and falls following stroke

PubMed Central

Liphart, Jodi; Gallichio, Joann; Tilson, Julie K; Pei, Qinglin; Wu, Samuel S; Duncan, Pamela W

2016-01-01

Objective To ascertain the existence of discordance between perceived and measured balance in persons with stroke and to examine the impact on walking speed and falls. Design A secondary analysis of a phase three, multicentered randomized controlled trial examining walking recovery following stroke. Subjects A total of 352 participants from the Locomotor Experience Applied Post-Stroke (LEAPS) trial. Methods Participants were categorized into four groups: two concordant and two discordant groups in relation to measured and perceived balance. Number and percentage of individuals with concordance and discordance were evaluated at two and 12 months. Walking speed and fall incidence between groups were examined. Main measures Perceived balance was measured by the Activity-specific Balance Confidence scale, measured balance was determined by the Berg Balance Scale and gait speed was measured by the 10-meter walk test. Results Discordance was present for 35.8% of participants at two months post-stroke with no statistically significant change in proportion at 12 months. Discordant participants with high perceived balance and low measured balance walked 0.09 m/s faster at two months than participants with concordant low perceived and measured balance (p < 0.05). Discordant participants with low perceived balance and high measured balance walked 0.15 m/s slower than those that were concordant with high perceived and measured balance (p ⩽ 0.0001) at 12 months. Concordant participants with high perceived and measured balance walked fastest and had fewer falls. Conclusions Discordance existed between perceived and measured balance in one-third of individuals at two and 12 months post-stroke. Perceived balance impacted gait speed but not fall incidence. PMID:25810426

The concordance between self-reported medication use and pharmacy records in pregnant women.

PubMed

Cheung, K; El Marroun, H; Elfrink, M E; Jaddoe, V W V; Visser, L E; Stricker, B H Ch

2017-09-01

Several studies have been conducted to assess determinants affecting the performance or accuracy of self-reports. These studies are often not focused on pregnant women, or medical records were used as a data source where it is unclear if medications have been dispensed. Therefore, our objective was to evaluate the concordance between self-reported medication data and pharmacy records among pregnant women and its determinants. We conducted a population-based cohort study within the Generation R study, in 2637 pregnant women. The concordance between self-reported medication data and pharmacy records was calculated for different therapeutic classes using Yule's Y. We evaluated a number of variables as determinant of discordance between both sources through univariate and multivariate logistic regression analysis. The concordance between self-reports and pharmacy records was moderate to good for medications used for chronic conditions, such as selective serotonin reuptake inhibitors or anti-asthmatic medications (0.88 and 0.68, respectively). Medications that are used occasionally, such as antibiotics, had a lower concordance (0.51). Women with a Turkish or other non-Western background were more likely to demonstrate discordance between pharmacy records and self-reported data compared with women with a Dutch background (Turkish: odds ratio, 1.63; 95% confidence interval, 1.16-2.29; other non-Western: odds ratio, 1.33; 95% confidence interval, 1.03-1.71). Further research is needed to assess how the cultural or ethnic differences may affect the concordance or discordance between both medication sources. The results of this study showed that the use of multiple sources is needed to have a good estimation of the medication use during pregnancy. Copyright © 2017 John Wiley & Sons, Ltd.

Circulating tumor DNA functions as an alternative for tissue to overcome tumor heterogeneity in advanced gastric cancer.

PubMed

Gao, Jing; Wang, Haixing; Zang, Wanchun; Li, Beifang; Rao, Guanhua; Li, Lei; Yu, Yang; Li, Zhongwu; Dong, Bin; Lu, Zhihao; Jiang, Zhi; Shen, Lin

2017-09-01

Overcoming tumor heterogeneity is a major challenge for personalized treatment of gastric cancer, especially for human epidermal growth factor receptor-2 targeted therapy. Analysis of circulating tumor DNA allows a more comprehensive analysis of tumor heterogeneity than traditional biopsies in lung cancer and breast cancer, but little is known in gastric cancer. We assessed mutation profiles of ctDNA and primary tumors from 30 patients with advanced gastric cancer, then performed a comprehensive analysis of tumor mutations by multiple biopsies from five patients, and finally analyzed the concordance of HER2 amplification in ctDNA and paired tumor tissues in 70 patients. By comparing with a single tumor sample, ctDNA displayed a low concordance of mutation profile, only approximately 50% (138/275) somatic mutations were found in paired tissue samples, however, when compared with multiple biopsies, most DNA mutations in ctDNA were also shown in paired tumor tissues. ctDNA had a high concordance (91.4%, Kappa index = 0.784, P < 0.001) of HER2 amplification with tumor tissues, suggesting it might be an alternative for tissue. It implied that ctDNA-based assessment could partially overcome the tumor heterogeneity, and might serve as a potential surrogate for HER2 analysis in gastric cancer. © 2017 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

A Bayesian Approach for Nonlinear Structural Equation Models with Dichotomous Variables Using Logit and Probit Links

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan; Cai, Jing-Heng

2010-01-01

Analysis of ordered binary and unordered binary data has received considerable attention in social and psychological research. This article introduces a Bayesian approach, which has several nice features in practical applications, for analyzing nonlinear structural equation models with dichotomous data. We demonstrate how to use the software…

Bayesian Statistics in Educational Research: A Look at the Current State of Affairs

ERIC Educational Resources Information Center

König, Christoph; van de Schoot, Rens

2018-01-01

The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline,…

Bayesian Finite Mixtures for Nonlinear Modeling of Educational Data.

ERIC Educational Resources Information Center

Tirri, Henry; And Others

A Bayesian approach for finding latent classes in data is discussed. The approach uses finite mixture models to describe the underlying structure in the data and demonstrate that the possibility of using full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated…

Bayesian Analysis of Structural Equation Models with Nonlinear Covariates and Latent Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2006-01-01

In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…

Evidence of major genes affecting stress response in rainbow trout using Bayesian methods of complex segregation analysis

USDA-ARS?s Scientific Manuscript database

As a first step towards the genetic mapping of quantitative trait loci (QTL) affecting stress response variation in rainbow trout, we performed complex segregation analyses (CSA) fitting mixed inheritance models of plasma cortisol using Bayesian methods in large full-sib families of rainbow trout. ...

A Hierarchical Bayesian Procedure for Two-Mode Cluster Analysis

ERIC Educational Resources Information Center

DeSarbo, Wayne S.; Fong, Duncan K. H.; Liechty, John; Saxton, M. Kim

2004-01-01

This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters.…

Exact Bayesian p-values for a test of independence in a 2 × 2 contingency table with missing data.

PubMed

Lin, Yan; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett; Lipshultz, Steven

2017-01-01

Altham (Altham PME. Exact Bayesian analysis of a 2 × 2 contingency table, and Fisher's "exact" significance test. J R Stat Soc B 1969; 31: 261-269) showed that a one-sided p-value from Fisher's exact test of independence in a 2 × 2 contingency table is equal to the posterior probability of negative association in the 2 × 2 contingency table under a Bayesian analysis using an improper prior. We derive an extension of Fisher's exact test p-value in the presence of missing data, assuming the missing data mechanism is ignorable (i.e., missing at random or completely at random). Further, we propose Bayesian p-values for a test of independence in a 2 × 2 contingency table with missing data using alternative priors; we also present results from a simulation study exploring the Type I error rate and power of the proposed exact test p-values. An example, using data on the association between blood pressure and a cardiac enzyme, is presented to illustrate the methods.

Bayesian randomized clinical trials: From fixed to adaptive design.

PubMed

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

2017-08-01

Randomized controlled studies are the gold standard for phase III clinical trials. Using α-spending functions to control the overall type I error rate, group sequential methods are well established and have been dominating phase III studies. Bayesian randomized design, on the other hand, can be viewed as a complement instead of competitive approach to the frequentist methods. For the fixed Bayesian design, the hypothesis testing can be cast in the posterior probability or Bayes factor framework, which has a direct link to the frequentist type I error rate. Bayesian group sequential design relies upon Bayesian decision-theoretic approaches based on backward induction, which is often computationally intensive. Compared with the frequentist approaches, Bayesian methods have several advantages. The posterior predictive probability serves as a useful and convenient tool for trial monitoring, and can be updated at any time as the data accrue during the trial. The Bayesian decision-theoretic framework possesses a direct link to the decision making in the practical setting, and can be modeled more realistically to reflect the actual cost-benefit analysis during the drug development process. Other merits include the possibility of hierarchical modeling and the use of informative priors, which would lead to a more comprehensive utilization of information from both historical and longitudinal data. From fixed to adaptive design, we focus on Bayesian randomized controlled clinical trials and make extensive comparisons with frequentist counterparts through numerical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of reliability of professor recommendation letters based on concordance with self-introduction letter.

PubMed

Kim, Sang Hyun

2013-12-01

The purpose of this study was to examine the concordance between a checklist's categories of professor recommendation letters and characteristics of the self-introduction letter. Checklists of professor recommendation letters were analyzed and classified into cognitive, social, and affective domains. Simple correlation was performed to determine whether the characteristics of the checklists were concordant with those of the self-introduction letter. The difference in ratings of the checklists by pass or fail grades was analyzed by independent sample t-test. Logistic regression analysis was performed to determine whether a pass or fail grade was influenced by ratings on the checklists. The Cronbach alpha value of the checklists was 0.854. Initiative, as an affective domain, in the professor's recommendation letter was highly ranked among the six checklist categories. Self-directed learning in the self-introduction letter was influenced by a pass or fail grade by logistic regression analysis (p<0.05). Successful applicants received higher ratings than those who failed in every checklist category, particularly in problem-solving ability, communication skills, initiative, and morality (p<0.05). There was a strong correlation between cognitive and affective characteristics in the professor recommendation letters and the sum of all characteristics in the self-introduction letter.

Exoplanet Biosignatures: Future Directions

PubMed Central

Bains, William; Cronin, Leroy; DasSarma, Shiladitya; Danielache, Sebastian; Domagal-Goldman, Shawn; Kacar, Betul; Kiang, Nancy Y.; Lenardic, Adrian; Reinhard, Christopher T.; Moore, William; Schwieterman, Edward W.; Shkolnik, Evgenya L.; Smith, Harrison B.

2018-01-01

Abstract We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets—Biosignatures—Life detection—Bayesian analysis. Astrobiology 18, 779–824. PMID:29938538

Population forecasts for Bangladesh, using a Bayesian methodology.

PubMed

Mahsin, Md; Hossain, Syed Shahadat

2012-12-01

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible.

Exoplanet Biosignatures: Future Directions.

PubMed

Walker, Sara I; Bains, William; Cronin, Leroy; DasSarma, Shiladitya; Danielache, Sebastian; Domagal-Goldman, Shawn; Kacar, Betul; Kiang, Nancy Y; Lenardic, Adrian; Reinhard, Christopher T; Moore, William; Schwieterman, Edward W; Shkolnik, Evgenya L; Smith, Harrison B

2018-06-01

We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets-Biosignatures-Life detection-Bayesian analysis. Astrobiology 18, 779-824.

Bayesian Estimation of Small Effects in Exercise and Sports Science.

PubMed

Mengersen, Kerrie L; Drovandi, Christopher C; Robert, Christian P; Pyne, David B; Gore, Christopher J

2016-01-01

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

33 CFR 165.1199 - Security Zones; Military Ocean Terminal Concord (MOTCO), Concord, California.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false Security Zones; Military Ocean... Coast Guard District § 165.1199 Security Zones; Military Ocean Terminal Concord (MOTCO), Concord..., extending from the surface to the sea floor, within 500 yards of the three Military Ocean Terminal Concord...

33 CFR 165.1199 - Security Zones; Military Ocean Terminal Concord (MOTCO), Concord, California.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Security Zones; Military Ocean... Coast Guard District § 165.1199 Security Zones; Military Ocean Terminal Concord (MOTCO), Concord..., extending from the surface to the sea floor, within 500 yards of the three Military Ocean Terminal Concord...

33 CFR 165.1199 - Security Zones; Military Ocean Terminal Concord (MOTCO), Concord, California.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false Security Zones; Military Ocean... Coast Guard District § 165.1199 Security Zones; Military Ocean Terminal Concord (MOTCO), Concord..., extending from the surface to the sea floor, within 500 yards of the three Military Ocean Terminal Concord...

33 CFR 165.1199 - Security Zones; Military Ocean Terminal Concord (MOTCO), Concord, California.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false Security Zones; Military Ocean... Coast Guard District § 165.1199 Security Zones; Military Ocean Terminal Concord (MOTCO), Concord..., extending from the surface to the sea floor, within 500 yards of the three Military Ocean Terminal Concord...

33 CFR 165.1199 - Security Zones; Military Ocean Terminal Concord (MOTCO), Concord, California.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false Security Zones; Military Ocean... Coast Guard District § 165.1199 Security Zones; Military Ocean Terminal Concord (MOTCO), Concord..., extending from the surface to the sea floor, within 500 yards of the three Military Ocean Terminal Concord...

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

Applications of Bayesian Statistics to Problems in Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Meegan, Charles A.

1997-01-01

This presentation will describe two applications of Bayesian statistics to Gamma Ray Bursts (GRBS). The first attempts to quantify the evidence for a cosmological versus galactic origin of GRBs using only the observations of the dipole and quadrupole moments of the angular distribution of bursts. The cosmological hypothesis predicts isotropy, while the galactic hypothesis is assumed to produce a uniform probability distribution over positive values for these moments. The observed isotropic distribution indicates that the Bayes factor for the cosmological hypothesis over the galactic hypothesis is about 300. Another application of Bayesian statistics is in the estimation of chance associations of optical counterparts with galaxies. The Bayesian approach is preferred to frequentist techniques here because the Bayesian approach easily accounts for galaxy mass distributions and because one can incorporate three disjoint hypotheses: (1) bursts come from galactic centers, (2) bursts come from galaxies in proportion to luminosity, and (3) bursts do not come from external galaxies. This technique was used in the analysis of the optical counterpart to GRB970228.

The evolutionary relationships and age of Homo naledi: An assessment using dated Bayesian phylogenetic methods.

PubMed

Dembo, Mana; Radovčić, Davorka; Garvin, Heather M; Laird, Myra F; Schroeder, Lauren; Scott, Jill E; Brophy, Juliet; Ackermann, Rebecca R; Musiba, Chares M; de Ruiter, Darryl J; Mooers, Arne Ø; Collard, Mark

2016-08-01

Homo naledi is a recently discovered species of fossil hominin from South Africa. A considerable amount is already known about H. naledi but some important questions remain unanswered. Here we report a study that addressed two of them: "Where does H. naledi fit in the hominin evolutionary tree?" and "How old is it?" We used a large supermatrix of craniodental characters for both early and late hominin species and Bayesian phylogenetic techniques to carry out three analyses. First, we performed a dated Bayesian analysis to generate estimates of the evolutionary relationships of fossil hominins including H. naledi. Then we employed Bayes factor tests to compare the strength of support for hypotheses about the relationships of H. naledi suggested by the best-estimate trees. Lastly, we carried out a resampling analysis to assess the accuracy of the age estimate for H. naledi yielded by the dated Bayesian analysis. The analyses strongly supported the hypothesis that H. naledi forms a clade with the other Homo species and Australopithecus sediba. The analyses were more ambiguous regarding the position of H. naledi within the (Homo, Au. sediba) clade. A number of hypotheses were rejected, but several others were not. Based on the available craniodental data, Homo antecessor, Asian Homo erectus, Homo habilis, Homo floresiensis, Homo sapiens, and Au. sediba could all be the sister taxon of H. naledi. According to the dated Bayesian analysis, the most likely age for H. naledi is 912 ka. This age estimate was supported by the resampling analysis. Our findings have a number of implications. Most notably, they support the assignment of the new specimens to Homo, cast doubt on the claim that H. naledi is simply a variant of H. erectus, and suggest H. naledi is younger than has been previously proposed. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

USGS Publications Warehouse

Link, William; Sauer, John R.

2016-01-01

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

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%.

Estimation of parameter uncertainty for an activated sludge model using Bayesian inference: a comparison with the frequentist method.

PubMed

Zonta, Zivko J; Flotats, Xavier; Magrí, Albert

2014-08-01

The procedure commonly used for the assessment of the parameters included in activated sludge models (ASMs) relies on the estimation of their optimal value within a confidence region (i.e. frequentist inference). Once optimal values are estimated, parameter uncertainty is computed through the covariance matrix. However, alternative approaches based on the consideration of the model parameters as probability distributions (i.e. Bayesian inference), may be of interest. The aim of this work is to apply (and compare) both Bayesian and frequentist inference methods when assessing uncertainty for an ASM-type model, which considers intracellular storage and biomass growth, simultaneously. Practical identifiability was addressed exclusively considering respirometric profiles based on the oxygen uptake rate and with the aid of probabilistic global sensitivity analysis. Parameter uncertainty was thus estimated according to both the Bayesian and frequentist inferential procedures. Results were compared in order to evidence the strengths and weaknesses of both approaches. Since it was demonstrated that Bayesian inference could be reduced to a frequentist approach under particular hypotheses, the former can be considered as a more generalist methodology. Hence, the use of Bayesian inference is encouraged for tackling inferential issues in ASM environments.

[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.

Assessment of DNA methylation profiling and copy number variation as indications of clonal relationship in ipsilateral and contralateral breast cancers to distinguish recurrent breast cancer from a second primary tumour.

PubMed

Huang, Katie T; Mikeska, Thomas; Li, Jason; Takano, Elena A; Millar, Ewan K A; Graham, Peter H; Boyle, Samantha E; Campbell, Ian G; Speed, Terence P; Dobrovic, Alexander; Fox, Stephen B

2015-10-09

Patients with breast cancer have an increased risk of developing subsequent breast cancers. It is important to distinguish whether these tumours are de novo or recurrences of the primary tumour in order to guide the appropriate therapy. Our aim was to investigate the use of DNA methylation profiling and array comparative genomic hybridization (aCGH) to determine whether the second tumour is clonally related to the first tumour. Methylation-sensitive high-resolution melting was used to screen promoter methylation in a panel of 13 genes reported as methylated in breast cancer (RASSF1A, TWIST1, APC, WIF1, MGMT, MAL, CDH13, RARβ, BRCA1, CDH1, CDKN2A, TP73, and GSTP1) in 29 tumour pairs (16 ipsilateral and 13 contralateral). Using the methylation profile of these genes, we employed a Bayesian and an empirical statistical approach to estimate clonal relationship. Copy number alterations were analysed using aCGH on the same set of tumour pairs. There is a higher probability of the second tumour being recurrent in ipsilateral tumours compared with contralateral tumours (38 % versus 8 %; p <0.05) based on the methylation profile. Using previously reported recurrence rates as Bayesian prior probabilities, we classified 69 % of ipsilateral and 15 % of contralateral tumours as recurrent. The inferred clonal relationship results of the tumour pairs were generally concordant between methylation profiling and aCGH. Our results show that DNA methylation profiling as well as aCGH have potential as diagnostic tools in improving the clinical decisions to differentiate recurrences from a second de novo tumour.

Bayesian probability estimates are not necessary to make choices satisfying Bayes' rule in elementary situations.

PubMed

Domurat, Artur; Kowalczuk, Olga; Idzikowska, Katarzyna; Borzymowska, Zuzanna; Nowak-Przygodzka, Marta

2015-01-01

This paper has two aims. First, we investigate how often people make choices conforming to Bayes' rule when natural sampling is applied. Second, we show that using Bayes' rule is not necessary to make choices satisfying Bayes' rule. Simpler methods, even fallacious heuristics, might prescribe correct choices reasonably often under specific circumstances. We considered elementary situations with binary sets of hypotheses and data. We adopted an ecological approach and prepared two-stage computer tasks resembling natural sampling. Probabilistic relations were inferred from a set of pictures, followed by a choice which was made to maximize the chance of a preferred outcome. Use of Bayes' rule was deduced indirectly from choices. Study 1 used a stratified sample of N = 60 participants equally distributed with regard to gender and type of education (humanities vs. pure sciences). Choices satisfying Bayes' rule were dominant. To investigate ways of making choices more directly, we replicated Study 1, adding a task with a verbal report. In Study 2 (N = 76) choices conforming to Bayes' rule dominated again. However, the verbal reports revealed use of a new, non-inverse rule, which always renders correct choices, but is easier than Bayes' rule to apply. It does not require inversion of conditions [transforming P(H) and P(D|H) into P(H|D)] when computing chances. Study 3 examined the efficiency of three fallacious heuristics (pre-Bayesian, representativeness, and evidence-only) in producing choices concordant with Bayes' rule. Computer-simulated scenarios revealed that the heuristics produced correct choices reasonably often under specific base rates and likelihood ratios. Summing up we conclude that natural sampling results in most choices conforming to Bayes' rule. However, people tend to replace Bayes' rule with simpler methods, and even use of fallacious heuristics may be satisfactorily efficient.

Bayesian probability estimates are not necessary to make choices satisfying Bayes’ rule in elementary situations

PubMed Central

Domurat, Artur; Kowalczuk, Olga; Idzikowska, Katarzyna; Borzymowska, Zuzanna; Nowak-Przygodzka, Marta

2015-01-01

This paper has two aims. First, we investigate how often people make choices conforming to Bayes’ rule when natural sampling is applied. Second, we show that using Bayes’ rule is not necessary to make choices satisfying Bayes’ rule. Simpler methods, even fallacious heuristics, might prescribe correct choices reasonably often under specific circumstances. We considered elementary situations with binary sets of hypotheses and data. We adopted an ecological approach and prepared two-stage computer tasks resembling natural sampling. Probabilistic relations were inferred from a set of pictures, followed by a choice which was made to maximize the chance of a preferred outcome. Use of Bayes’ rule was deduced indirectly from choices. Study 1 used a stratified sample of N = 60 participants equally distributed with regard to gender and type of education (humanities vs. pure sciences). Choices satisfying Bayes’ rule were dominant. To investigate ways of making choices more directly, we replicated Study 1, adding a task with a verbal report. In Study 2 (N = 76) choices conforming to Bayes’ rule dominated again. However, the verbal reports revealed use of a new, non-inverse rule, which always renders correct choices, but is easier than Bayes’ rule to apply. It does not require inversion of conditions [transforming P(H) and P(D|H) into P(H|D)] when computing chances. Study 3 examined the efficiency of three fallacious heuristics (pre-Bayesian, representativeness, and evidence-only) in producing choices concordant with Bayes’ rule. Computer-simulated scenarios revealed that the heuristics produced correct choices reasonably often under specific base rates and likelihood ratios. Summing up we conclude that natural sampling results in most choices conforming to Bayes’ rule. However, people tend to replace Bayes’ rule with simpler methods, and even use of fallacious heuristics may be satisfactorily efficient. PMID:26347676

Concordance between phylogeographical and biogeographical patterns in the Brazilian Cerrado: diversification of the endemic tree Dalbergia miscolobium (Fabaceae).

PubMed

Novaes, Renan Milagres Lage; Ribeiro, Renata Acácio; Lemos-Filho, José Pires; Lovato, Maria Bernadete

2013-01-01

Few studies have addressed the phylogeography of species of the Cerrado, the largest savanna biome of South America. Here we aimed to investigate the phylogeographical structure of Dalbergia miscolobium, a widespread tree from the Cerrado, and to verify its concordance with plant phylogeographical and biogeographical patterns so far described. A total of 287 individuals from 32 populations were analyzed by sequencing the trnL intron of the chloroplast DNA and the internal transcribed spacer of the nuclear ribosomal DNA. Analysis of population structure and tests of population expansion were performed and the time of divergence of haplotypes was estimated. Twelve and 27 haplotypes were identified in the cpDNA and nrDNA data, respectively. The star-like network configuration and the mismatch distributions indicated a recent spatial and demographic expansion of the species. Consistent with previous tree phylogeographical studies of Cerrado trees, the cpDNA also suggested a recent expansion towards the southern Cerrado. The diversity of D. miscolobium was widespread but high levels of genetic diversity were found in the Central Eastern and in the southern portion of Central Western Cerrado. The combined analysis of cpDNA and nrDNA supported a phylogeographic structure into seven groups. The phylogeographical pattern showed many concordances with biogeographical and phylogeographical studies in the Cerrado, mainly with the Cerrado phytogeographic provinces superimposed to our sampling area. The data reinforced the uniqueness of Northeastern and Southeastern Cerrados and the differentiation between Eastern and Western Central Cerrados. The recent diversification of the species (estimated between the Pliocene and the Pleistocene) and the 'genealogical concordances' suggest that a shared and persistent pattern of species diversification might have been present in the Cerrado over time. This is the first time that an extensive 'genealogical concordance' between phylogeographic and phytogeographic patterns is shown for the Cerrado biome.

Diffusion-weighted imaging of the prostate: should we use quantitative metrics to better characterize focal lesions originating in the peripheral zone?

PubMed

Pierre, Thibaut; Cornud, Francois; Colléter, Loïc; Beuvon, Frédéric; Foissac, Frantz; Delongchamps, Nicolas B; Legmann, Paul

2018-05-01

To compare inter-reader concordance and accuracy of qualitative diffusion-weighted (DW) PIRADSv2.0 score with those of quantitative DW-MRI for the diagnosis of peripheral zone prostate cancer. Two radiologists independently assigned a DW-MRI-PIRADS score to 92 PZ-foci, in 74 patients (64.3±5.6 years old; median PSA level: 8 ng/ml, normal DRE in 70 men). A standardised ADCmean and nine ADC-derived parameters were measured, including ADCratios with the whole-prostate (WP-ADCratio) or the mirror-PZ (mirror-ADCratio) as reference areas. Surgical histology and MRI-TRUS fusion-biopsy were the reference for tumours and benign foci, respectively. Inter-reader agreement was assessed by the Cohen-kappa-coefficient and the intraclass correlation coefficient (ICC). Univariate-multivariate regressions determined the most predictive factor for cancer. Fifty lesions were malignant. Inter-reader concordance was fair for qualitative assessment, but excellent for quantitative assessment for all quantitative variables. At univariate analysis, ADCmean, WP-ADCratio and WL-ADCmean performed equally, but significantly better than the mirror-ADCratio (p<0.001). At multivariate analysis, the only independent variable significantly associated with malignancy was the whole-prostate-ADCratio. At a cut-off value of 0.68, sensitivity was 94-90 % and specificity was 60-38 % for readers 1 and 2, respectively. The whole-prostate-ADCratio improved the qualitative inter-reader concordance and characterisation of focal PZ-lesions. • Inter-reader concordance of DW PI-RADSv2.0 score for PZ lesions was only fair. • Using a standardised ADCmean measurement and derived DW-quantitative parameters, concordance was excellent. • The whole-prostate ADCratio performed significantly better than the mirror-ADCratio for cancer detection. • At a cut-off of 0.68, sensitivity values of WP-ADCratio were 94-90 %. • The whole-prostate ADCratio may circumvent variations of ADC metrics across centres.

Concordance of preclinical and clinical pharmacology and toxicology of therapeutic monoclonal antibodies and fusion proteins: cell surface targets

PubMed Central

Bugelski, Peter J; Martin, Pauline L

2012-01-01

Monoclonal antibodies (mAbs) and fusion proteins directed towards cell surface targets make an important contribution to the treatment of disease. The purpose of this review was to correlate the clinical and preclinical data on the 15 currently approved mAbs and fusion proteins targeted to the cell surface. The principal sources used to gather data were: the peer reviewed Literature; European Medicines Agency ‘Scientific Discussions’; and the US Food and Drug Administration ‘Pharmacology/Toxicology Reviews’ and package inserts (United States Prescribing Information). Data on the 15 approved biopharmaceuticals were included: abatacept; abciximab; alefacept; alemtuzumab; basiliximab; cetuximab; daclizumab; efalizumab; ipilimumab; muromonab; natalizumab; panitumumab; rituximab; tocilizumab; and trastuzumab. For statistical analysis of concordance, data from these 15 were combined with data on the approved mAbs and fusion proteins directed towards soluble targets. Good concordance with human pharmacodynamics was found for mice receiving surrogates or non-human primates (NHPs) receiving the human pharmaceutical. In contrast, there was poor concordance for human pharmacodynamics in genetically deficient mice and for human adverse effects in all three test systems. No evidence that NHPs have superior predictive value was found. PMID:22168282

Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

PubMed

Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

2014-03-01

The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

Quantum state estimation when qubits are lost: a no-data-left-behind approach

DOE PAGES

Williams, Brian P.; Lougovski, Pavel

2017-04-06

We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean and maximum likelihood estimates for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the Bayesian mean estimate for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity.

Potential of SNP markers for the characterization of Brazilian cassava germplasm.

PubMed

de Oliveira, Eder Jorge; Ferreira, Cláudia Fortes; da Silva Santos, Vanderlei; de Jesus, Onildo Nunes; Oliveira, Gilmara Alvarenga Fachardo; da Silva, Maiane Suzarte

2014-06-01

High-throughput markers, such as SNPs, along with different methodologies were used to evaluate the applicability of the Bayesian approach and the multivariate analysis in structuring the genetic diversity in cassavas. The objective of the present work was to evaluate the diversity and genetic structure of the largest cassava germplasm bank in Brazil. Complementary methodological approaches such as discriminant analysis of principal components (DAPC), Bayesian analysis and molecular analysis of variance (AMOVA) were used to understand the structure and diversity of 1,280 accessions genotyped using 402 single nucleotide polymorphism markers. The genetic diversity (0.327) and the average observed heterozygosity (0.322) were high considering the bi-allelic markers. In terms of population, the presence of a complex genetic structure was observed indicating the formation of 30 clusters by DAPC and 34 clusters by Bayesian analysis. Both methodologies presented difficulties and controversies in terms of the allocation of some accessions to specific clusters. However, the clusters suggested by the DAPC analysis seemed to be more consistent for presenting higher probability of allocation of the accessions within the clusters. Prior information related to breeding patterns and geographic origins of the accessions were not sufficient for providing clear differentiation between the clusters according to the AMOVA analysis. In contrast, the F ST was maximized when considering the clusters suggested by the Bayesian and DAPC analyses. The high frequency of germplasm exchange between producers and the subsequent alteration of the name of the same material may be one of the causes of the low association between genetic diversity and geographic origin. The results of this study may benefit cassava germplasm conservation programs, and contribute to the maximization of genetic gains in breeding programs.

Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data.

PubMed

Ding, Aidong Adam; Hsieh, Jin-Jian; Wang, Weijing

2015-01-01

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.

Impact of the Supervisor Feedback Environment on Creative Performance: A Moderated Mediation Model.

PubMed

Zhang, Jian; Gong, Zhenxing; Zhang, Shuangyu; Zhao, Yujia

2017-01-01

Studies on the relationship between feedback and creative performance have only focused on the feedback-self and have underestimated the value of the feedback environment. Building on Self Determined Theory, the purpose of this article is to examine the relationship among feedback environment, creative personality, goal self-concordance and creative performance. Hierarchical regression analysis of a sample of 162 supervisor-employee dyads from nine industry firms. The results indicate that supervisor feedback environment is positively related to creative performance, the relationship between the supervisor feedback environment and creative performance is mediated by goal self-concordance perfectly and moderated by creative personality significantly. The mediation effort of goal self-concordance is significantly influenced by creative personality. The implication of improving employees' creative performance is further discussed. The present study advances several perspectives of previous studies, echoes recent suggestions that organizations interested in stimulating employee creativity might profitably focus on developing work contexts that support it.

How Accurate are Self-Reports? An Analysis of Self-Reported Healthcare Utilization and Absence When Compared to Administrative Data

PubMed Central

Short, Meghan E.; Pei, Xiaofei; Tabrizi, Maryam J.; Ozminkowski, Ronald J.; Gibson, Teresa B.; DeJoy, Dave M.; Wilson, Mark G.

2009-01-01

Objective To determine the accuracy of self-reported healthcare utilization and absence reported on health risk assessments (HRAs) against administrative claims and human resource records. Methods Self-reported values of healthcare utilization and absenteeism were analyzed for concordance to administrative claims values. Percent agreement, Pearson’s correlations, and multivariate logistic regression models examined the level of agreement and characteristics of participants with concordance. Results Self-report and administrative data showed greater concordance for monthly compared to yearly healthcare utilization metrics. Percent agreement ranged from 30 to 99% with annual doctor visits having the lowest percent agreement. Younger people, males, those with higher education, and healthier individuals more accurately reported their healthcare utilization and absenteeism. Conclusions Self-reported healthcare utilization and absenteeism may be used as a proxy when medical claims and administrative data are unavailable, particularly for shorter recall periods. PMID:19528832

Correlates of unmet need for contraception in Bangladesh: does couples' concordance in household decision making matter?

PubMed

Uddin, Jalal; Pulok, Mohammad Habibullah; Sabah, Md Nasim-Us

2016-07-01

A large body of literature has highlighted that women's household decision-making power is associated with better reproductive health outcomes, while most of the studies tend to measure such power from only women's point of view. Using both husband's and wife's matched responses to decision-making questions, this study examined the association between couples' concordant and discordant decision makings, and wife's unmet need for contraception in Bangladesh. This study used couple's data set (n=3336) from Bangladesh Demographic and Health Survey of 2007. Multivariate logistic regression was used to examine the likelihood of unmet need for contraception among married women of reproductive age. Study results suggested that couples who support the equalitarian power structure seemed to be more powerful in meeting the unmet demand for contraception. Logistic regression analysis revealed that compared to couple's concordant joint decision making, concordance in husband-only or other's involvement in decision making was associated with higher odds of unmet need for contraception. Wives exposed to family planning information discussed family planning more often with husbands, and those from richest households were less likely to have unmet need for contraception. Couple's concordant joint decision making, reflecting the concept of equalitarian power structure, appeared to be a significant analytic category. Policy makers in the field of family planning may promote community-based outreach programs and communication campaigns for family planning focusing on egalitarian gender roles in the household. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparison of Combined Endoscopic Ultrasonography and Endoscopic Secretin Testing With the Traditional Secretin Pancreatic Function Test in Patients With Suspected Chronic Pancreatitis: A Prospective Crossover Study.

PubMed

Kothari, Darshan; Ketwaroo, Gyanprakash; Sawhney, Mandeep S; Freedman, Steven D; Sheth, Sunil G

2017-07-01

We aimed to determine the feasibility and accuracy of a combined endoscopic ultrasonography (EUS) with a shortened pancreatic function testing (sEUS) for structural and functional assessment using a single instrument in patients with suspected chronic pancreatitis (CP). We completed a prospective crossover study, enrolling patients with suspected CP. Patients who underwent both traditional 1-hour secretin pancreatic function test (sPFT) and sEUS were included in the analysis. We compared study results for test concordance and for correlation of peak bicarbonate concentrations. Eleven (64.7%) of 17 patients had concordant sPFT and sEUS findings when the cutoff for peak bicarbonate was 80 mEq/L. Six patients had discordant findings with a negative sPFT and positive sEUS. This poor concordance suggests that sEUS is an unreliable functional test. Lowering the sEUS cutoff to 70 mEq/L resulted in improved concordance (64.7% vs 70.6%). Finally, there was no significant correlation between peak bicarbonate concentrations (r = 0.47; 95% confidence interval, -0.02 to 0.79) in these 2 functional tests. We demonstrate poor concordance between sPFT and sEUS suggesting that a combined shortened functional and structural test using a single instrument may not be a feasible test for diagnosis of suspected CP when a cutoff of 80 mEq/L is used.

Concordance in the Assessment of Effectiveness of Palliative Care between Patients and Palliative Care Nurses in Malaysia: A Study with the Palliative Care Outcome Scale

PubMed Central

Koh, Kwee Choy; Gupta, Esha Das; Poovaneswaran, Sangeetha; Then, Siaw Ling; Teo, Michelle Jia Jui; Gan, Teik Yiap; Thing, Joanne Hwei Yean

2017-01-01

Context: The Palliative Care Outcome Scale (POS) is an easy-to-use assessment tool to evaluate the effectiveness of palliative care. There is no published literature on the use of POS as an assessment tool in Malaysia. Aim: To define the concordance in the assessment of quality of life between patients with advanced cancers and their palliative care nurses using a Malay version of the POS. Settings and Design: This study was conducted in the palliative care unit of the Hospital Tuanku Ja'afar Seremban, Malaysia, from February 2014 to June 2014. Subjects and Methods: We adapted and validated the English version of the 3-day recall POS into Malay and used it to define the concordance in the assessment of quality of life between patients and palliative care nurses. Forty patients with advanced stage cancers and forty palliative care nurses completed the Malay POS questionnaire. Statistical Analysis Used: The kappa statistical test was used to assess the agreement between patients and their palliative care nurses. Results: Slight to fair concordance was found in all items, except for one item (family anxiety) where there was no agreement. Conclusions: The Malay version of the POS was well accepted and reliable as an assessment tool for evaluation of the effectiveness of palliative care in Malaysia. Slight to fair concordance was shown between the patients and their palliative care nurses, suggesting the needs for more training of the nurses. PMID:28216862

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

PubMed

Li, Qike; Schissler, A Grant; Gardeux, Vincent; Achour, Ikbel; Kenost, Colleen; Berghout, Joanne; Li, Haiquan; Zhang, Hao Helen; Lussier, Yves A

2017-05-24

Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

Hair Analysis and its Concordance with Self-report for Drug Users Presenting in Emergency Department*

PubMed Central

Sharma, Gaurav; Oden, Neal; VanVeldhuisen, Paul C.; Bogenschutz, Michael P.

2016-01-01

Background Secondary analysis using data from the National Drug Abuse Treatment Clinical Trials Network randomized trial (NCT # 01207791), in which 1,285 adult ED patients endorsing moderate to severe problems related to drug use were recruited from 6 US academic hospitals. Objective To investigate the utility of hair analysis in drug use disorder trials with infrequent visits, and its concordance with Timeline Follow Back (TLFB). Methods This study compared the self-reported drug use on the TLFB instrument with the biological measure of drug use from hair analysis for four major drug classes (Cannabis, Cocaine, Prescribed Opioids and Street Opioids). Both hair analysis and TLFB were conducted at 3, 6 and 12 month follow-up visit and each covered a 90-day recall period prior to the visit. Results The concordance between the hair sample results and the TLFB was high for cannabis and street opioids, but was low to moderate for cocaine and prescribed opioids. Under-reporting of drug use given the positive hair sample was always significantly lower for the drug the study participant noted as their primary drug of choice compared with other drugs the participant reported taking, irrespective of whether the drug of choice was cannabis, cocaine, street opioids and prescribed opioids. Over-reporting of drug use given the negative hair sample was always significantly higher for the drug of choice, except for cocaine. Conclusions This study extends the literature on hair analysis supporting its use as a secondary outcome measure in clinical trials. PMID:27522871

Hair analysis and its concordance with self-report for drug users presenting in emergency department.

PubMed

Sharma, Gaurav; Oden, Neal; VanVeldhuisen, Paul C; Bogenschutz, Michael P

2016-10-01

Secondary analysis using data from the National Drug Abuse Treatment Clinical Trials Network randomized trial (NCT # 01207791), in which 1285 adult ED patients endorsing moderate to severe problems related to drug use were recruited from 6 US academic hospitals. To investigate the utility of hair analysis in drug use disorder trials with infrequent visits, and its concordance with Timeline Follow Back (TLFB). This study compared the self-reported drug use on the TLFB instrument with the biological measure of drug use from hair analysis for four major drug classes (Cannabis, Cocaine, Prescribed Opioids and Street Opioids). Both hair analysis and TLFB were conducted at 3, 6 and 12 month follow-up visit and each covered a 90-day recall period prior to the visit. The concordance between the hair sample results and the TLFB was high for cannabis and street opioids, but was low to moderate for cocaine and prescribed opioids. Under-reporting of drug use given the positive hair sample was always significantly lower for the drug the study participant noted as their primary drug of choice compared with other drugs the participant reported taking, irrespective of whether the drug of choice was cannabis, cocaine, street opioids and prescribed opioids. Over-reporting of drug use given the negative hair sample was always significantly higher for the drug of choice, except for cocaine. This study extends the literature on hair analysis supporting its use as a secondary outcome measure in clinical trials. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Competing risk models in reliability systems, a weibull distribution model with bayesian analysis approach

NASA Astrophysics Data System (ADS)

Iskandar, Ismed; Satria Gondokaryono, Yudi

2016-02-01

In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range between the true value and the maximum likelihood estimated value lines.

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

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.

Bayesian estimation of dynamic matching function for U-V analysis in Japan

NASA Astrophysics Data System (ADS)

Kyo, Koki; Noda, Hideo; Kitagawa, Genshiro

2012-05-01

In this paper we propose a Bayesian method for analyzing unemployment dynamics. We derive a Beveridge curve for unemployment and vacancy (U-V) analysis from a Bayesian model based on a labor market matching function. In our framework, the efficiency of matching and the elasticities of new hiring with respect to unemployment and vacancy are regarded as time varying parameters. To construct a flexible model and obtain reasonable estimates in an underdetermined estimation problem, we treat the time varying parameters as random variables and introduce smoothness priors. The model is then described in a state space representation, enabling the parameter estimation to be carried out using Kalman filter and fixed interval smoothing. In such a representation, dynamic features of the cyclic unemployment rate and the structural-frictional unemployment rate can be accurately captured.

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…

Designing a Mobile Training System in Rural Areas with Bayesian Factor Models

ERIC Educational Resources Information Center

Omidi Najafabadi, Maryam; Mirdamadi, Seyed Mehdi; Payandeh Najafabadi, Amir Teimour

2014-01-01

The facts that the wireless technologies (1) are more convenient; and (2) need less skill than desktop computers, play a crucial role to decrease digital gap in rural areas. This study employed the Bayesian Confirmatory Factor Analysis (CFA) to design a mobile training system in rural areas of Iran. It categorized challenges, potential, and…

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

NASA Astrophysics Data System (ADS)

Sadegh, Mojtaba; Ragno, Elisa; AghaKouchak, Amir

2017-06-01

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.

Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

NASA Astrophysics Data System (ADS)

Wilting, Jens; Lehnertz, Klaus

2015-08-01

We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

Genetic variability among elite popcorn lines based on molecular and morphoagronomic characteristics.

PubMed

Dos Santos, J F; Mangolin, C A; Machado, M F P S; Scapim, C A; Giordani, W; Gonçalves, L S A

2017-06-29

Knowledge of genetic diversity among genotypes and relationships among elite lines is of great importance for the development of breeding programs. Therefore, the objective of this study was to evaluate genetic variability based on the morphoagronomic and molecular characterization of 18 elite popcorn (Zea mays var. everta) lines to be used by Universidade Estadual de Maringá breeding programs. We used 31 microsatellite primers (widely distributed in the genome), and 16 morphological descriptors (including the resistance to maize white spot, common rust, polysora rust of maize, cercospora and leaf blights). The molecular data revealed variability among the lines, which were divided into four groups that were partially concordant with unweighted pair group method with arithmetic mean (UPMGA) and Bayesian clusters. The lines G3, G4, G11, and G13 exhibited favorable morphological characters and low disease incidence rates. The four groups were confirmed using the Gower distance in the UPGMA cluster; however, there was no association with the dissimilarity patterns obtained using the molecular data. The absence of a correlation suggests that both characterizations (morphoagronomic and molecular) are important for discriminating among elite popcorn lines.

Species delimitation in the Stenocereus griseus (Cactaceae) species complex reveals a new species, S. huastecorum

PubMed Central

Alvarado-Sizzo, Hernán; Parra, Fabiola; Arreola-Nava, Hilda Julieta; Terrazas, Teresa; Sánchez, Cristian

2018-01-01

The Stenocereus griseus species complex (SGSC) has long been considered taxonomically challenging because the number of taxa belonging to the complex and their geographical boundaries remain poorly understood. Bayesian clustering and genetic distance-based methods were used based on nine microsatellite loci in 377 individuals of three main putative species of the complex. The resulting genetic clusters were assessed for ecological niche divergence and areolar morphology, particularly spination patterns. We based our species boundaries on concordance between genetic, ecological, and morphological data, and were able to resolve four species, three of them corresponding to S. pruinosus from central Mexico, S. laevigatus from southern Mexico, and S. griseus from northern South America. A fourth species, previously considered to be S. griseus and commonly misidentified as S. pruinosus in northern Mexico showed significant genetic, ecological, and morphological differentiation suggesting that it should be considered a new species, S. huastecorum, which we describe here. We show that population genetic analyses, ecological niche modeling, and morphological studies are complementary approaches for delimiting species in taxonomically challenging plant groups such as the SGSC. PMID:29342184

Species delimitation in the Stenocereus griseus (Cactaceae) species complex reveals a new species, S. huastecorum.

PubMed

Alvarado-Sizzo, Hernán; Casas, Alejandro; Parra, Fabiola; Arreola-Nava, Hilda Julieta; Terrazas, Teresa; Sánchez, Cristian

2018-01-01

The Stenocereus griseus species complex (SGSC) has long been considered taxonomically challenging because the number of taxa belonging to the complex and their geographical boundaries remain poorly understood. Bayesian clustering and genetic distance-based methods were used based on nine microsatellite loci in 377 individuals of three main putative species of the complex. The resulting genetic clusters were assessed for ecological niche divergence and areolar morphology, particularly spination patterns. We based our species boundaries on concordance between genetic, ecological, and morphological data, and were able to resolve four species, three of them corresponding to S. pruinosus from central Mexico, S. laevigatus from southern Mexico, and S. griseus from northern South America. A fourth species, previously considered to be S. griseus and commonly misidentified as S. pruinosus in northern Mexico showed significant genetic, ecological, and morphological differentiation suggesting that it should be considered a new species, S. huastecorum, which we describe here. We show that population genetic analyses, ecological niche modeling, and morphological studies are complementary approaches for delimiting species in taxonomically challenging plant groups such as the SGSC.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Yu, Chen

2017-06-01

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

Bayesian estimates of the incidence of rare cancers in Europe.

PubMed

Botta, Laura; Capocaccia, Riccardo; Trama, Annalisa; Herrmann, Christian; Salmerón, Diego; De Angelis, Roberta; Mallone, Sandra; Bidoli, Ettore; Marcos-Gragera, Rafael; Dudek-Godeau, Dorota; Gatta, Gemma; Cleries, Ramon

2018-04-21

The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. We analyzed about 2,000,000 rare cancers diagnosed in 2000-2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Bayesian and classical estimates did not differ much; substantial differences (>10%) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. Copyright © 2018 Elsevier Ltd. All rights reserved.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

NASA Astrophysics Data System (ADS)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Social deprivation and population density are not associated with small area risk of amyotrophic lateral sclerosis.

PubMed

Rooney, James P K; Tobin, Katy; Crampsie, Arlene; Vajda, Alice; Heverin, Mark; McLaughlin, Russell; Staines, Anthony; Hardiman, Orla

2015-10-01

Evidence of an association between areal ALS risk and population density has been previously reported. We aim to examine ALS spatial incidence in Ireland using small areas, to compare this analysis with our previous analysis of larger areas and to examine the associations between population density, social deprivation and ALS incidence. Residential area social deprivation has not been previously investigated as a risk factor for ALS. Using the Irish ALS register, we included all cases of ALS diagnosed in Ireland from 1995-2013. 2006 census data was used to calculate age and sex standardised expected cases per small area. Social deprivation was assessed using the pobalHP deprivation index. Bayesian smoothing was used to calculate small area relative risk for ALS, whilst cluster analysis was performed using SaTScan. The effects of population density and social deprivation were tested in two ways: (1) as covariates in the Bayesian spatial model; (2) via post-Bayesian regression. 1701 cases were included. Bayesian smoothed maps of relative risk at small area resolution matched closely to our previous analysis at a larger area resolution. Cluster analysis identified two areas of significant low risk. These areas did not correlate with population density or social deprivation indices. Two areas showing low frequency of ALS have been identified in the Republic of Ireland. These areas do not correlate with population density or residential area social deprivation, indicating that other reasons, such as genetic admixture may account for the observed findings. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

On the uncertainty in single molecule fluorescent lifetime and energy emission measurements

NASA Technical Reports Server (NTRS)

Brown, Emery N.; Zhang, Zhenhua; Mccollom, Alex D.

1995-01-01

Time-correlated single photon counting has recently been combined with mode-locked picosecond pulsed excitation to measure the fluorescent lifetimes and energy emissions of single molecules in a flow stream. Maximum likelihood (ML) and least square methods agree and are optimal when the number of detected photons is large however, in single molecule fluorescence experiments the number of detected photons can be less than 20, 67% of those can be noise and the detection time is restricted to 10 nanoseconds. Under the assumption that the photon signal and background noise are two independent inhomogeneous poisson processes, we derive the exact joint arrival time probably density of the photons collected in a single counting experiment performed in the presence of background noise. The model obviates the need to bin experimental data for analysis, and makes it possible to analyze formally the effect of background noise on the photon detection experiment using both ML or Bayesian methods. For both methods we derive the joint and marginal probability densities of the fluorescent lifetime and fluorescent emission. the ML and Bayesian methods are compared in an analysis of simulated single molecule fluorescence experiments of Rhodamine 110 using different combinations of expected background nose and expected fluorescence emission. While both the ML or Bayesian procedures perform well for analyzing fluorescence emissions, the Bayesian methods provide more realistic measures of uncertainty in the fluorescent lifetimes. The Bayesian methods would be especially useful for measuring uncertainty in fluorescent lifetime estimates in current single molecule flow stream experiments where the expected fluorescence emission is low. Both the ML and Bayesian algorithms can be automated for applications in molecular biology.

On the Uncertainty in Single Molecule Fluorescent Lifetime and Energy Emission Measurements

NASA Technical Reports Server (NTRS)

Brown, Emery N.; Zhang, Zhenhua; McCollom, Alex D.

1996-01-01

Time-correlated single photon counting has recently been combined with mode-locked picosecond pulsed excitation to measure the fluorescent lifetimes and energy emissions of single molecules in a flow stream. Maximum likelihood (ML) and least squares methods agree and are optimal when the number of detected photons is large, however, in single molecule fluorescence experiments the number of detected photons can be less than 20, 67 percent of those can be noise, and the detection time is restricted to 10 nanoseconds. Under the assumption that the photon signal and background noise are two independent inhomogeneous Poisson processes, we derive the exact joint arrival time probability density of the photons collected in a single counting experiment performed in the presence of background noise. The model obviates the need to bin experimental data for analysis, and makes it possible to analyze formally the effect of background noise on the photon detection experiment using both ML or Bayesian methods. For both methods we derive the joint and marginal probability densities of the fluorescent lifetime and fluorescent emission. The ML and Bayesian methods are compared in an analysis of simulated single molecule fluorescence experiments of Rhodamine 110 using different combinations of expected background noise and expected fluorescence emission. While both the ML or Bayesian procedures perform well for analyzing fluorescence emissions, the Bayesian methods provide more realistic measures of uncertainty in the fluorescent lifetimes. The Bayesian methods would be especially useful for measuring uncertainty in fluorescent lifetime estimates in current single molecule flow stream experiments where the expected fluorescence emission is low. Both the ML and Bayesian algorithms can be automated for applications in molecular biology.

Bayesian Analysis of Biogeography when the Number of Areas is Large

PubMed Central

Landis, Michael J.; Matzke, Nicholas J.; Moore, Brian R.; Huelsenbeck, John P.

2013-01-01

Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a “data-augmentation” approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea. [ancestral area analysis; Bayesian biogeographic inference; data augmentation; historical biogeography; Markov chain Monte Carlo.] PMID:23736102

A Defence of the AR4’s Bayesian Approach to Quantifying Uncertainty

NASA Astrophysics Data System (ADS)

Vezer, M. A.

2009-12-01

The field of climate change research is a kimberlite pipe filled with philosophic diamonds waiting to be mined and analyzed by philosophers. Within the scientific literature on climate change, there is much philosophical dialogue regarding the methods and implications of climate studies. To this date, however, discourse regarding the philosophy of climate science has been confined predominately to scientific - rather than philosophical - investigations. In this paper, I hope to bring one such issue to the surface for explicit philosophical analysis: The purpose of this paper is to address a philosophical debate pertaining to the expressions of uncertainty in the International Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), which, as will be noted, has received significant attention in scientific journals and books, as well as sporadic glances from the popular press. My thesis is that the AR4’s Bayesian method of uncertainty analysis and uncertainty expression is justifiable on pragmatic grounds: it overcomes problems associated with vagueness, thereby facilitating communication between scientists and policy makers such that the latter can formulate decision analyses in response to the views of the former. Further, I argue that the most pronounced criticisms against the AR4’s Bayesian approach, which are outlined below, are misguided. §1 Introduction Central to AR4 is a list of terms related to uncertainty that in colloquial conversations would be considered vague. The IPCC attempts to reduce the vagueness of its expressions of uncertainty by calibrating uncertainty terms with numerical probability values derived from a subjective Bayesian methodology. This style of analysis and expression has stimulated some controversy, as critics reject as inappropriate and even misleading the association of uncertainty terms with Bayesian probabilities. [...] The format of the paper is as follows. The investigation begins (§2) with an explanation of background considerations relevant to the IPCC and its use of uncertainty expressions. It then (§3) outlines some general philosophical worries regarding vague expressions and (§4) relates those worries to the AR4 and its method of dealing with them, which is a subjective Bayesian probability analysis. The next phase of the paper (§5) examines the notions of ‘objective’ and ‘subjective’ probability interpretations and compares the IPCC’s subjective Bayesian strategy with a frequentist approach. It then (§6) addresses objections to that methodology, and concludes (§7) that those objections are wrongheaded.

Effect of intraarticular inoculation of mesenchymal stem cells in dogs with hip osteoarthritis by means of objective force platform gait analysis: concordance with numeric subjective scoring scales.

PubMed

Vilar, Jose M; Cuervo, Belen; Rubio, Monica; Sopena, Joaquín; Domínguez, Juan M; Santana, Angelo; Carrillo, Jose M

2016-10-07

Subjective pain assessment scales have been widely used for assessing lameness in response to pain, but the accuracy of these scales has been questioned. To assess scale accuracy, 10 lame, presa Canario dogs with osteoarthritis (OA) associated with bilateral hip dysplasia were first treated with mesenchymal stem cells. Then, potential lameness improvement was analyzed using two pain scales (Bioarth and visual analog scale). These data were compared with similar data collected using a force platform with the same animals during a period of 6 months after treatment. The F test for intraclass correlation showed that concordance in pain/lameness scores between the 2 measuring methodologies was not significant (P value ≥ 0.9213; 95 % confidence interval, -0.56, 0.11). Although subjective pain assessment showed improvement after 6 months, force platform data demonstrated those same animals had returned to the initial lameness state. Use of pain assessment scales to measure lameness associated with OA did not have great accuracy and concordance when compared with quantitative force platform gait analysis.

Consistency of the Planck CMB data and ΛCDM cosmology

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

Shafieloo, Arman; Hazra, Dhiraj Kumar, E-mail: shafieloo@kasi.re.kr, E-mail: dhiraj.kumar.hazra@apc.univ-paris7.fr

We test the consistency between Planck temperature and polarization power spectra and the concordance model of Λ Cold Dark Matter cosmology (ΛCDM) within the framework of Crossing statistics. We find that Planck TT best fit ΛCDM power spectrum is completely consistent with EE power spectrum data while EE best fit ΛCDM power spectrum is not consistent with TT data. However, this does not point to any systematic or model-data discrepancy since in the Planck EE data, uncertainties are much larger compared to the TT data. We also investigate the possibility of any deviation from ΛCDM model analyzing the Planck 2015more » data. Results from TT, TE and EE data analysis indicate that no deviation is required beyond the flexibility of the concordance ΛCDM model. Our analysis thus rules out any strong evidence for beyond the concordance model in the Planck spectra data. We also report a mild amplitude difference comparing temperature and polarization data, where temperature data seems to have slightly lower amplitude than expected (consistently at all multiples), as we assume both temperature and polarization data are realizations of the same underlying cosmology.« less

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

PubMed

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Spiritual and ceremonial plants in North America: an assessment of Moerman's ethnobotanical database comparing Residual, Binomial, Bayesian and Imprecise Dirichlet Model (IDM) analysis.

PubMed

Turi, Christina E; Murch, Susan J

2013-07-09

Ethnobotanical research and the study of plants used for rituals, ceremonies and to connect with the spirit world have led to the discovery of many novel psychoactive compounds such as nicotine, caffeine, and cocaine. In North America, spiritual and ceremonial uses of plants are well documented and can be accessed online via the University of Michigan's Native American Ethnobotany Database. The objective of the study was to compare Residual, Bayesian, Binomial and Imprecise Dirichlet Model (IDM) analyses of ritual, ceremonial and spiritual plants in Moerman's ethnobotanical database and to identify genera that may be good candidates for the discovery of novel psychoactive compounds. The database was queried with the following format "Family Name AND Ceremonial OR Spiritual" for 263 North American botanical families. Spiritual and ceremonial flora consisted of 86 families with 517 species belonging to 292 genera. Spiritual taxa were then grouped further into ceremonial medicines and items categories. Residual, Bayesian, Binomial and IDM analysis were performed to identify over and under-utilized families. The 4 statistical approaches were in good agreement when identifying under-utilized families but large families (>393 species) were underemphasized by Binomial, Bayesian and IDM approaches for over-utilization. Residual, Binomial, and IDM analysis identified similar families as over-utilized in the medium (92-392 species) and small (<92 species) classes. The families Apiaceae, Asteraceae, Ericacea, Pinaceae and Salicaceae were identified as significantly over-utilized as ceremonial medicines in medium and large sized families. Analysis of genera within the Apiaceae and Asteraceae suggest that the genus Ligusticum and Artemisia are good candidates for facilitating the discovery of novel psychoactive compounds. The 4 statistical approaches were not consistent in the selection of over-utilization of flora. Residual analysis revealed overall trends that were supported by Binomial analysis when separated into small, medium and large families. The Bayesian, Binomial and IDM approaches identified different genera as potentially important. Species belonging to the genus Artemisia and Ligusticum were most consistently identified and may be valuable in future studies of the ethnopharmacology. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Gene expression profile of mouse prostate tumors reveals dysregulations in major biological processes and identifies potential murine targets for preclinical development of human prostate cancer therapy.

PubMed

Haram, Kerstyn M; Peltier, Heidi J; Lu, Bin; Bhasin, Manoj; Otu, Hasan H; Choy, Bob; Regan, Meredith; Libermann, Towia A; Latham, Gary J; Sanda, Martin G; Arredouani, Mohamed S

2008-10-01

Translation of preclinical studies into effective human cancer therapy is hampered by the lack of defined molecular expression patterns in mouse models that correspond to the human counterpart. We sought to generate an open source TRAMP mouse microarray dataset and to use this array to identify differentially expressed genes from human prostate cancer (PCa) that have concordant expression in TRAMP tumors, and thereby represent lead targets for preclinical therapy development. We performed microarrays on total RNA extracted and amplified from eight TRAMP tumors and nine normal prostates. A subset of differentially expressed genes was validated by QRT-PCR. Differentially expressed TRAMP genes were analyzed for concordant expression in publicly available human prostate array datasets and a subset of resulting genes was analyzed by QRT-PCR. Cross-referencing differentially expressed TRAMP genes to public human prostate array datasets revealed 66 genes with concordant expression in mouse and human PCa; 56 between metastases and normal and 10 between primary tumor and normal tissues. Of these 10 genes, two, Sox4 and Tubb2a, were validated by QRT-PCR. Our analysis also revealed various dysregulations in major biologic pathways in the TRAMP prostates. We report a TRAMP microarray dataset of which a gene subset was validated by QRT-PCR with expression patterns consistent with previous gene-specific TRAMP studies. Concordance analysis between TRAMP and human PCa associated genes supports the utility of the model and suggests several novel molecular targets for preclinical therapy.

Formal analysis of the surgical pathway and development of a new software tool to assist surgeons in the decision making in primary breast surgery.

PubMed

Catanuto, Giuseppe; Pappalardo, Francesco; Rocco, Nicola; Leotta, Marco; Ursino, Venera; Chiodini, Paolo; Buggi, Federico; Folli, Secondo; Catalano, Francesca; Nava, Maurizio B

2016-10-01

The increased complexity of the decisional process in breast cancer surgery is well documented. With this study we aimed to create a software tool able to assist patients and surgeons in taking proper decisions. We hypothesized that the endpoints of breast cancer surgery could be addressed combining a set of decisional drivers. We created a decision support system software tool (DSS) and an interactive decision tree. A formal analysis estimated the information gain derived from each feature in the process. We tested the DSS on 52 patients and we analyzed the concordance of decisions obtained by different users and between the DSS suggestions and the actual surgery. We also tested the ability of the system to prevent post breast conservation deformities. The information gain revealed that patients preferences are the root of our decision tree. An observed concordance respectively of 0.98 and 0.88 was reported when the DSS was used twice by an expert operator or by a newly trained operator vs. an expert one. The observed concordance between the DSS suggestion and the actual decision was 0.69. A significantly higher incidence of post breast conservation defects was reported among patients who did not follow the DSS decision (Type III of Fitoussi, N = 4; 33.3%, p = 0.004). The DSS decisions can be reproduced by operators with different experience. The concordance between suggestions and actual decision is quite low, however the DSS is able to prevent post- breast conservation deformities. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Siblings With Ischemic Stroke Study (SWISS) Protocol

PubMed Central

Meschia, James F; Brown, Robert D; Brott, Thomas G; Chukwudelunzu, Felix E; Hardy, John; Rich, Stephen S

2002-01-01

Background Family history and twins studies suggest an inherited component to ischemic stroke risk. Candidate gene association studies have been performed but have limited capacity to identify novel risk factor genes. The Siblings With Ischemic Stroke Study (SWISS) aims to conduct a genome-wide scan in sibling pairs concordant or discordant for ischemic stroke to identify novel genetic risk factors through linkage analysis. Methods Screening at multiple clinical centers identifies patients (probands) with radiographically confirmed ischemic stroke and a family history of at least 1 living full sibling with stroke. After giving informed consent, without violating privacy among other family members, the proband invites siblings concordant and discordant for stroke to participate. Siblings then contact the study coordinating center. The diagnosis of ischemic stroke in potentially concordant siblings is confirmed by systematic centralized review of medical records. The stroke-free status of potentially discordant siblings is confirmed by validated structured telephone interview. Blood samples for DNA analysis are taken from concordant sibling pairs and, if applicable, from 1 discordant sibling. Epstein-Barr virus-transformed lymphoblastoid cell lines are created, and a scan of the human genome is planned. Discussion Conducting adequately powered genomics studies of stroke in humans is challenging because of the heterogeneity of the stroke phenotype and the difficulty of obtaining DNA samples from clinically well-characterized members of a cohort of stroke pedigrees. The multicentered design of this study is intended to efficiently assemble a cohort of ischemic stroke pedigrees without invoking community consent or using cold-calling of pedigree members. PMID:11882254

Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Shengli; Jiang, Xiaomo; Huang, Jinzhi; Yang, Shuhua; Wang, Xiaofang

2016-12-01

Centrifugal compressor often suffers various defects such as impeller cracking, resulting in forced outage of the total plant. Damage diagnostics and condition monitoring of such a turbomachinery system has become an increasingly important and powerful tool to prevent potential failure in components and reduce unplanned forced outage and further maintenance costs, while improving reliability, availability and maintainability of a turbomachinery system. This paper presents a probabilistic signal processing methodology for damage diagnostics using multiple time history data collected from different locations of a turbomachine, considering data uncertainty and multivariate correlation. The proposed methodology is based on the integration of three advanced state-of-the-art data mining techniques: discrete wavelet packet transform, Bayesian hypothesis testing, and probabilistic principal component analysis. The multiresolution wavelet analysis approach is employed to decompose a time series signal into different levels of wavelet coefficients. These coefficients represent multiple time-frequency resolutions of a signal. Bayesian hypothesis testing is then applied to each level of wavelet coefficient to remove possible imperfections. The ratio of posterior odds Bayesian approach provides a direct means to assess whether there is imperfection in the decomposed coefficients, thus avoiding over-denoising. Power spectral density estimated by the Welch method is utilized to evaluate the effectiveness of Bayesian wavelet cleansing method. Furthermore, the probabilistic principal component analysis approach is developed to reduce dimensionality of multiple time series and to address multivariate correlation and data uncertainty for damage diagnostics. The proposed methodology and generalized framework is demonstrated with a set of sensor data collected from a real-world centrifugal compressor with impeller cracks, through both time series and contour analyses of vibration signal and principal components.

Substantial advantage of a combined Bayesian and genotyping approach in testosterone doping tests.

PubMed

Schulze, Jenny Jakobsson; Lundmark, Jonas; Garle, Mats; Ekström, Lena; Sottas, Pierre-Edouard; Rane, Anders

2009-03-01

Testosterone abuse is conventionally assessed by the urinary testosterone/epitestosterone (T/E) ratio, levels above 4.0 being considered suspicious. A deletion polymorphism in the gene coding for UGT2B17 is strongly associated with reduced testosterone glucuronide (TG) levels in urine. Many of the individuals devoid of the gene would not reach a T/E ratio of 4.0 after testosterone intake. Future test programs will most likely shift from population based- to individual-based T/E cut-off ratios using Bayesian inference. A longitudinal analysis is dependent on an individual's true negative baseline T/E ratio. The aim was to investigate whether it is possible to increase the sensitivity and specificity of the T/E test by addition of UGT2B17 genotype information in a Bayesian framework. A single intramuscular dose of 500mg testosterone enanthate was given to 55 healthy male volunteers with either two, one or no allele (ins/ins, ins/del or del/del) of the UGT2B17 gene. Urinary excretion of TG and the T/E ratio was measured during 15 days. The Bayesian analysis was conducted to calculate the individual T/E cut-off ratio. When adding the genotype information, the program returned lower individual cut-off ratios in all del/del subjects increasing the sensitivity of the test considerably. It will be difficult, if not impossible, to discriminate between a true negative baseline T/E value and a false negative one without knowledge of the UGT2B17 genotype. UGT2B17 genotype information is crucial, both to decide which initial cut-off ratio to use for an individual, and for increasing the sensitivity of the Bayesian analysis.

Comparing energy sources for surgical ablation of atrial fibrillation: a Bayesian network meta-analysis of randomized, controlled trials.

PubMed

Phan, Kevin; Xie, Ashleigh; Kumar, Narendra; Wong, Sophia; Medi, Caroline; La Meir, Mark; Yan, Tristan D

2015-08-01

Simplified maze procedures involving radiofrequency, cryoenergy and microwave energy sources have been increasingly utilized for surgical treatment of atrial fibrillation as an alternative to the traditional cut-and-sew approach. In the absence of direct comparisons, a Bayesian network meta-analysis is another alternative to assess the relative effect of different treatments, using indirect evidence. A Bayesian meta-analysis of indirect evidence was performed using 16 published randomized trials identified from 6 databases. Rank probability analysis was used to rank each intervention in terms of their probability of having the best outcome. Sinus rhythm prevalence beyond the 12-month follow-up was similar between the cut-and-sew, microwave and radiofrequency approaches, which were all ranked better than cryoablation (respectively, 39, 36, and 25 vs 1%). The cut-and-sew maze was ranked worst in terms of mortality outcomes compared with microwave, radiofrequency and cryoenergy (2 vs 19, 34, and 24%, respectively). The cut-and-sew maze procedure was associated with significantly lower stroke rates compared with microwave ablation [odds ratio <0.01; 95% confidence interval 0.00, 0.82], and ranked the best in terms of pacemaker requirements compared with microwave, radiofrequency and cryoenergy (81 vs 14, and 1, <0.01% respectively). Bayesian rank probability analysis shows that the cut-and-sew approach is associated with the best outcomes in terms of sinus rhythm prevalence and stroke outcomes, and remains the gold standard approach for AF treatment. Given the limitations of indirect comparison analysis, these results should be viewed with caution and not over-interpreted. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Spatiotemporal Phylogenetic Analysis and Molecular Characterisation of Infectious Bursal Disease Viruses Based on the VP2 Hyper-Variable Region

PubMed Central

Dolz, Roser; Valle, Rosa; Perera, Carmen L.; Bertran, Kateri; Frías, Maria T.; Majó, Natàlia; Ganges, Llilianne; Pérez, Lester J.

2013-01-01

Background Infectious bursal disease is a highly contagious and acute viral disease caused by the infectious bursal disease virus (IBDV); it affects all major poultry producing areas of the world. The current study was designed to rigorously measure the global phylogeographic dynamics of IBDV strains to gain insight into viral population expansion as well as the emergence, spread and pattern of the geographical structure of very virulent IBDV (vvIBDV) strains. Methodology/Principal Findings Sequences of the hyper-variable region of the VP2 (HVR-VP2) gene from IBDV strains isolated from diverse geographic locations were obtained from the GenBank database; Cuban sequences were obtained in the current work. All sequences were analysed by Bayesian phylogeographic analysis, implemented in the Bayesian Evolutionary Analysis Sampling Trees (BEAST), Bayesian Tip-association Significance testing (BaTS) and Spatial Phylogenetic Reconstruction of Evolutionary Dynamics (SPREAD) software packages. Selection pressure on the HVR-VP2 was also assessed. The phylogeographic association-trait analysis showed that viruses sampled from individual countries tend to cluster together, suggesting a geographic pattern for IBDV strains. Spatial analysis from this study revealed that strains carrying sequences that were linked to increased virulence of IBDV appeared in Iran in 1981 and spread to Western Europe (Belgium) in 1987, Africa (Egypt) around 1990, East Asia (China and Japan) in 1993, the Caribbean Region (Cuba) by 1995 and South America (Brazil) around 2000. Selection pressure analysis showed that several codons in the HVR-VP2 region were under purifying selection. Conclusions/Significance To our knowledge, this work is the first study applying the Bayesian phylogeographic reconstruction approach to analyse the emergence and spread of vvIBDV strains worldwide. PMID:23805195

Spatiotemporal Phylogenetic Analysis and Molecular Characterisation of Infectious Bursal Disease Viruses Based on the VP2 Hyper-Variable Region.

PubMed

Alfonso-Morales, Abdulahi; Martínez-Pérez, Orlando; Dolz, Roser; Valle, Rosa; Perera, Carmen L; Bertran, Kateri; Frías, Maria T; Majó, Natàlia; Ganges, Llilianne; Pérez, Lester J

2013-01-01

Infectious bursal disease is a highly contagious and acute viral disease caused by the infectious bursal disease virus (IBDV); it affects all major poultry producing areas of the world. The current study was designed to rigorously measure the global phylogeographic dynamics of IBDV strains to gain insight into viral population expansion as well as the emergence, spread and pattern of the geographical structure of very virulent IBDV (vvIBDV) strains. Sequences of the hyper-variable region of the VP2 (HVR-VP2) gene from IBDV strains isolated from diverse geographic locations were obtained from the GenBank database; Cuban sequences were obtained in the current work. All sequences were analysed by Bayesian phylogeographic analysis, implemented in the Bayesian Evolutionary Analysis Sampling Trees (BEAST), Bayesian Tip-association Significance testing (BaTS) and Spatial Phylogenetic Reconstruction of Evolutionary Dynamics (SPREAD) software packages. Selection pressure on the HVR-VP2 was also assessed. The phylogeographic association-trait analysis showed that viruses sampled from individual countries tend to cluster together, suggesting a geographic pattern for IBDV strains. Spatial analysis from this study revealed that strains carrying sequences that were linked to increased virulence of IBDV appeared in Iran in 1981 and spread to Western Europe (Belgium) in 1987, Africa (Egypt) around 1990, East Asia (China and Japan) in 1993, the Caribbean Region (Cuba) by 1995 and South America (Brazil) around 2000. Selection pressure analysis showed that several codons in the HVR-VP2 region were under purifying selection. To our knowledge, this work is the first study applying the Bayesian phylogeographic reconstruction approach to analyse the emergence and spread of vvIBDV strains worldwide.

Parameterization of aquatic ecosystem functioning and its natural variation: Hierarchical Bayesian modelling of plankton food web dynamics

NASA Astrophysics Data System (ADS)

Norros, Veera; Laine, Marko; Lignell, Risto; Thingstad, Frede

2017-10-01

Methods for extracting empirically and theoretically sound parameter values are urgently needed in aquatic ecosystem modelling to describe key flows and their variation in the system. Here, we compare three Bayesian formulations for mechanistic model parameterization that differ in their assumptions about the variation in parameter values between various datasets: 1) global analysis - no variation, 2) separate analysis - independent variation and 3) hierarchical analysis - variation arising from a shared distribution defined by hyperparameters. We tested these methods, using computer-generated and empirical data, coupled with simplified and reasonably realistic plankton food web models, respectively. While all methods were adequate, the simulated example demonstrated that a well-designed hierarchical analysis can result in the most accurate and precise parameter estimates and predictions, due to its ability to combine information across datasets. However, our results also highlighted sensitivity to hyperparameter prior distributions as an important caveat of hierarchical analysis. In the more complex empirical example, hierarchical analysis was able to combine precise identification of parameter values with reasonably good predictive performance, although the ranking of the methods was less straightforward. We conclude that hierarchical Bayesian analysis is a promising tool for identifying key ecosystem-functioning parameters and their variation from empirical datasets.

Iterative Assessment of Statistically-Oriented and Standard Algorithms for Determining Muscle Onset with Intramuscular Electromyography.

PubMed

Tenan, Matthew S; Tweedell, Andrew J; Haynes, Courtney A

2017-12-01

The onset of muscle activity, as measured by electromyography (EMG), is a commonly applied metric in biomechanics. Intramuscular EMG is often used to examine deep musculature and there are currently no studies examining the effectiveness of algorithms for intramuscular EMG onset. The present study examines standard surface EMG onset algorithms (linear envelope, Teager-Kaiser Energy Operator, and sample entropy) and novel algorithms (time series mean-variance analysis, sequential/batch processing with parametric and nonparametric methods, and Bayesian changepoint analysis). Thirteen male and 5 female subjects had intramuscular EMG collected during isolated biceps brachii and vastus lateralis contractions, resulting in 103 trials. EMG onset was visually determined twice by 3 blinded reviewers. Since the reliability of visual onset was high (ICC (1,1) : 0.92), the mean of the 6 visual assessments was contrasted with the algorithmic approaches. Poorly performing algorithms were stepwise eliminated via (1) root mean square error analysis, (2) algorithm failure to identify onset/premature onset, (3) linear regression analysis, and (4) Bland-Altman plots. The top performing algorithms were all based on Bayesian changepoint analysis of rectified EMG and were statistically indistinguishable from visual analysis. Bayesian changepoint analysis has the potential to produce more reliable, accurate, and objective intramuscular EMG onset results than standard methodologies.

Quantitative trait nucleotide analysis using Bayesian model selection.

PubMed

Blangero, John; Goring, Harald H H; Kent, Jack W; Williams, Jeff T; Peterson, Charles P; Almasy, Laura; Dyer, Thomas D

2005-10-01

Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.

Bayesian data analysis tools for atomic physics

NASA Astrophysics Data System (ADS)

Trassinelli, Martino

2017-10-01

We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.

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.

Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: forensic fire debris analysis.

PubMed

Williams, Mary R; Sigman, Michael E; Lewis, Jennifer; Pitan, Kelly McHugh

2012-10-10

A bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Bayesian structural equation modeling: a more flexible representation of substantive theory.

PubMed

Muthén, Bengt; Asparouhov, Tihomir

2012-09-01

This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.

Does Repeated Testing Impact Concordance Between Genital and Self-Reported Sexual Arousal in Women?

PubMed

Velten, Julia; Chivers, Meredith L; Brotto, Lori A

2018-04-01

Women show a substantial variability in their genital and subjective responses to sexual stimuli. The level of agreement between these two aspects of response is termed sexual concordance and has been increasingly investigated because of its implications for understanding models of sexual response and as a potential endpoint in clinical trials of treatments to improve women's sexual dysfunction. However, interpreting changes in sexual concordance may be problematic because, to date, it still is unclear how repeated testing itself influences sexual concordance in women. We are aware of only one study that evaluated temporal stability of concordance in women, and it found no evidence of stability. However, time stability would be necessary for arguing that concordance is a stable individual difference. The main goal of this study was to investigate the test-retest reliability of sexual concordance in a sample of 30 women with sexual difficulties. Using hierarchical linear modeling, we found that sexual concordance was not influenced by repeated testing 12 weeks later, but showed test-retest reliability suggesting temporal stability. Our findings support the hypothesis that sexual concordance is a relatively stable individual difference and that changes in sexual concordance after treatment or experimental conditions could, therefore, be attributed to effects of those conditions.

Comparative radiographic analysis on the anatomical axis in knee osteoarthritis cases: inter and intraobserver evaluation.

PubMed

Matos, Luiz Felipe; Giordano, Marcos; Cardoso, Gustavo Novaes; Farias, Rafael Baptista; E Albuquerque, Rodrigo Pires

2015-01-01

To make a comparative inter and intraobserver analysis on measurements of the anatomical axis between panoramic radiographs of the lower limbs in anteroposterior (AP) view with bipedal weight-bearing, on short film. An accuracy study comparing radiographic measurements on 47 knees of patients attending the knee surgery outpatient clinic due to osteoarthritis. The radiographic evaluation used was as standardized for the total knee arthroplasty program, including panoramic AP views of the lower limbs and short radiographs of the knees in AP and lateral views, all with bipedal weight-bearing. Following this, the anatomical axis of the lower limbs or the femorotibial angle was measured by five independent examiners on the panoramic and short AP radiographs; three of the examiners were considered to be more experienced and two, less experienced. All the measurements were made again by the same examiners after an interval of not less than 15 days. The statistical analysis was performed using the intraclass correlation coefficient, in order to evaluate the inter and intraobserver concordance of the anatomical axis measurements. From the statistical analysis, it was observed that there was strongly significant concordance between the anatomical axis measurements on the panoramic and short radiographs, for all the five examiners and for both measurements. Under the conditions studied, short radiographs were equivalent to panoramic radiographs for evaluating the anatomical axis of the lower limbs in patients with advanced osteoarthritis. The measurements used also showed high rates of inter and intraobserver concordance and reproducibility.

Constructing "Nerdiness": Characterisation in "The Big Bang Theory"

ERIC Educational Resources Information Center

Bednarek, Monika

2012-01-01

This paper analyses the linguistic construction of the televisual character Sheldon--the "main nerd" in the sitcom "The Big Bang Theory" (CBS, 2007-), approaching this construction of character through both computerised and "manual" linguistic analysis. More specifically, a computer analysis of dialogue (using concordances and keyword analysis) in…

Bayesian Analysis of Item Response Curves. Research Report 84-1. Mathematical Sciences Technical Report No. 132.

ERIC Educational Resources Information Center

Tsutakawa, Robert K.; Lin, Hsin Ying

Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. The procedure is illustrated by data…

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

PubMed Central

2010-01-01

Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data. PMID:21062443

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

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

DOE PAGES

Alderman, Phillip D.; Stanfill, Bryan

2016-10-06

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relativemore » contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.« less

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

Numerical study on the sequential Bayesian approach for radioactive materials detection

NASA Astrophysics Data System (ADS)

Qingpei, Xiang; Dongfeng, Tian; Jianyu, Zhu; Fanhua, Hao; Ge, Ding; Jun, Zeng

2013-01-01

A new detection method, based on the sequential Bayesian approach proposed by Candy et al., offers new horizons for the research of radioactive detection. Compared with the commonly adopted detection methods incorporated with statistical theory, the sequential Bayesian approach offers the advantages of shorter verification time during the analysis of spectra that contain low total counts, especially in complex radionuclide components. In this paper, a simulation experiment platform implanted with the methodology of sequential Bayesian approach was developed. Events sequences of γ-rays associating with the true parameters of a LaBr3(Ce) detector were obtained based on an events sequence generator using Monte Carlo sampling theory to study the performance of the sequential Bayesian approach. The numerical experimental results are in accordance with those of Candy. Moreover, the relationship between the detection model and the event generator, respectively represented by the expected detection rate (Am) and the tested detection rate (Gm) parameters, is investigated. To achieve an optimal performance for this processor, the interval of the tested detection rate as a function of the expected detection rate is also presented.

Bayesian Inference for Time Trends in Parameter Values using Weighted Evidence Sets

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

D. L. Kelly; A. Malkhasyan

2010-09-01

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 “in-dustry lacks tools to perform time-trend analysis with Bayesian updating.” This paper describes an applica-tion 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 an approach to incorporating multiple sources of data via applicability weighting factors that address differences in key influences, such as vendor, component boundaries, conditions of the operating environment, etc.« less

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

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods

PubMed Central

Teng, Ming; Nathoo, Farouk S.; Johnson, Timothy D.

2017-01-01

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data. PMID:29200537

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.

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software.

PubMed

Bott, Marjorie; Karanevich, Alex G; Garrard, Lili; Price, Larry R; Mudaranthakam, Dinesh Pal; Gajewski, Byron

2018-02-01

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

Assessing noninferiority in a three-arm trial using the Bayesian approach.

PubMed

Ghosh, Pulak; Nathoo, Farouk; Gönen, Mithat; Tiwari, Ram C

2011-07-10

Non-inferiority trials, which aim to demonstrate that a test product is not worse than a competitor by more than a pre-specified small amount, are of great importance to the pharmaceutical community. As a result, methodology for designing and analyzing such trials is required, and developing new methods for such analysis is an important area of statistical research. The three-arm trial consists of a placebo, a reference and an experimental treatment, and simultaneously tests the superiority of the reference over the placebo along with comparing this reference to an experimental treatment. In this paper, we consider the analysis of non-inferiority trials using Bayesian methods which incorporate both parametric as well as semi-parametric models. The resulting testing approach is both flexible and robust. The benefit of the proposed Bayesian methods is assessed via simulation, based on a study examining home-based blood pressure interventions. Copyright © 2011 John Wiley & Sons, Ltd.

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

Bayesian tomography and integrated data analysis in fusion diagnostics

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

Li, Dong, E-mail: lid@swip.ac.cn; Dong, Y. B.; Deng, Wei

2016-11-15

In this article, a Bayesian tomography method using non-stationary Gaussian process for a prior has been introduced. The Bayesian formalism allows quantities which bear uncertainty to be expressed in the probabilistic form so that the uncertainty of a final solution can be fully resolved from the confidence interval of a posterior probability. Moreover, a consistency check of that solution can be performed by checking whether the misfits between predicted and measured data are reasonably within an assumed data error. In particular, the accuracy of reconstructions is significantly improved by using the non-stationary Gaussian process that can adapt to the varyingmore » smoothness of emission distribution. The implementation of this method to a soft X-ray diagnostics on HL-2A has been used to explore relevant physics in equilibrium and MHD instability modes. This project is carried out within a large size inference framework, aiming at an integrated analysis of heterogeneous diagnostics.« less

A statistical model investigating the prevalence of tuberculosis in New York City using counting processes with two change-points

PubMed Central

ACHCAR, J. A.; MARTINEZ, E. Z.; RUFFINO-NETTO, A.; PAULINO, C. D.; SOARES, P.

2008-01-01

SUMMARY We considered a Bayesian analysis for the prevalence of tuberculosis cases in New York City from 1970 to 2000. This counting dataset presented two change-points during this period. We modelled this counting dataset considering non-homogeneous Poisson processes in the presence of the two-change points. A Bayesian analysis for the data is considered using Markov chain Monte Carlo methods. Simulated Gibbs samples for the parameters of interest were obtained using WinBugs software. PMID:18346287

Crystalline nucleation in undercooled liquids: a Bayesian data-analysis approach for a nonhomogeneous Poisson process.

PubMed

Filipponi, A; Di Cicco, A; Principi, E

2012-12-01

A Bayesian data-analysis approach to data sets of maximum undercooling temperatures recorded in repeated melting-cooling cycles of high-purity samples is proposed. The crystallization phenomenon is described in terms of a nonhomogeneous Poisson process driven by a temperature-dependent sample nucleation rate J(T). The method was extensively tested by computer simulations and applied to real data for undercooled liquid Ge. It proved to be particularly useful in the case of scarce data sets where the usage of binned data would degrade the available experimental information.

Statistical analysis of modal parameters of a suspension bridge based on Bayesian spectral density approach and SHM data

NASA Astrophysics Data System (ADS)

Li, Zhijun; Feng, Maria Q.; Luo, Longxi; Feng, Dongming; Xu, Xiuli

2018-01-01

Uncertainty of modal parameters estimation appear in structural health monitoring (SHM) practice of civil engineering to quite some significant extent due to environmental influences and modeling errors. Reasonable methodologies are needed for processing the uncertainty. Bayesian inference can provide a promising and feasible identification solution for the purpose of SHM. However, there are relatively few researches on the application of Bayesian spectral method in the modal identification using SHM data sets. To extract modal parameters from large data sets collected by SHM system, the Bayesian spectral density algorithm was applied to address the uncertainty of mode extraction from output-only response of a long-span suspension bridge. The posterior most possible values of modal parameters and their uncertainties were estimated through Bayesian inference. A long-term variation and statistical analysis was performed using the sensor data sets collected from the SHM system of the suspension bridge over a one-year period. The t location-scale distribution was shown to be a better candidate function for frequencies of lower modes. On the other hand, the burr distribution provided the best fitting to the higher modes which are sensitive to the temperature. In addition, wind-induced variation of modal parameters was also investigated. It was observed that both the damping ratios and modal forces increased during the period of typhoon excitations. Meanwhile, the modal damping ratios exhibit significant correlation with the spectral intensities of the corresponding modal forces.

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

PubMed

Zhang, Yanmin; Jiao, Yu; Xiong, Xiao; Liu, Haichun; Ran, Ting; Xu, Jinxing; Lu, Shuai; Xu, Anyang; Pan, Jing; Qiao, Xin; Shi, Zhihao; Lu, Tao; Chen, Yadong

2015-11-01

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

DOE PAGES

Wang, Hongrui; Wang, Cheng; Wang, Ying; ...

2017-04-05

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLEmore » confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.« less

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.

A Bayesian hierarchical diffusion model decomposition of performance in Approach–Avoidance Tasks

PubMed Central

Krypotos, Angelos-Miltiadis; Beckers, Tom; Kindt, Merel; Wagenmakers, Eric-Jan

2015-01-01

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. PMID:25491372

Asteroid orbital error analysis: Theory and application

NASA Technical Reports Server (NTRS)

Muinonen, K.; Bowell, Edward

1992-01-01

We present a rigorous Bayesian theory for asteroid orbital error estimation in which the probability density of the orbital elements is derived from the noise statistics of the observations. For Gaussian noise in a linearized approximation the probability density is also Gaussian, and the errors of the orbital elements at a given epoch are fully described by the covariance matrix. The law of error propagation can then be applied to calculate past and future positional uncertainty ellipsoids (Cappellari et al. 1976, Yeomans et al. 1987, Whipple et al. 1991). To our knowledge, this is the first time a Bayesian approach has been formulated for orbital element estimation. In contrast to the classical Fisherian school of statistics, the Bayesian school allows a priori information to be formally present in the final estimation. However, Bayesian estimation does give the same results as Fisherian estimation when no priori information is assumed (Lehtinen 1988, and reference therein).

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Spectral Analysis of B Stars: An Application of Bayesian Statistics

NASA Astrophysics Data System (ADS)

Mugnes, J.-M.; Robert, C.

2012-12-01

To better understand the processes involved in stellar physics, it is necessary to obtain accurate stellar parameters (effective temperature, surface gravity, abundances…). Spectral analysis is a powerful tool for investigating stars, but it is also vital to reduce uncertainties at a decent computational cost. Here we present a spectral analysis method based on a combination of Bayesian statistics and grids of synthetic spectra obtained with TLUSTY. This method simultaneously constrains the stellar parameters by using all the lines accessible in observed spectra and thus greatly reduces uncertainties and improves the overall spectrum fitting. Preliminary results are shown using spectra from the Observatoire du Mont-Mégantic.

Suggestions for presenting the results of data analyses

USGS Publications Warehouse

Anderson, David R.; Link, William A.; Johnson, Douglas H.; Burnham, Kenneth P.

2001-01-01

We give suggestions for the presentation of research results from frequentist, information-theoretic, and Bayesian analysis paradigms, followed by several general suggestions. The information-theoretic and Bayesian methods offer alternative approaches to data analysis and inference compared to traditionally used methods. Guidance is lacking on the presentation of results under these alternative procedures and on nontesting aspects of classical frequentists methods of statistical analysis. Null hypothesis testing has come under intense criticism. We recommend less reporting of the results of statistical tests of null hypotheses in cases where the null is surely false anyway, or where the null hypothesis is of little interest to science or management.

Use of whole-genome sequencing for Campylobacter surveillance from NARMS retail poultry in the United States in 2015.

PubMed

Whitehouse, Chris A; Young, Shenia; Li, Cong; Hsu, Chih-Hao; Martin, Gordon; Zhao, Shaohua

2018-08-01

Whole genome sequencing (WGS) has become a rapid and affordable tool for public health surveillance and outbreak detection. In this study, we used the Illuminia MiSeq ® to sequence 589 Campylobacter isolates obtained in 2015 from retail poultry meats as part of the National Antimicrobial Resistance Monitoring System (NARMS). WGS data were used to identify the Campylobacter species and to compare the concordance between resistance genotypes and phenotypes. WGS accurately identified 386 C. jejuni and 203 C. coli using gyrA sequence information. Ten resistance genes, including tetO, bla OXA-61 , aph(2″)-Ic, aph(2″)-If, aph(2″)-Ig, aph(3')-III, ant(6)-1a, aadE, aph(3")-VIIa, and Inu(C), plus mutations in housekeeping genes (gyrA at position 86, 23S rRNA at position 2074 and 2075), were identified by WGS analysis. Overall, there was a high concordance between phenotypic resistance to a given drug and the presence of known resistance genes. Concordance between both resistance and susceptible phenotypes and genotype was 100% for ciprofloxacin, nalidixic acid, gentamicin, azithromycin, and florfenicol. A few discrepancies were observed for tetracycline, clindamycin, and telithromycin. The concordance between resistance phenotype and genotype ranged from 67.9% to 100%; whereas, the concordance between susceptible phenotype and genotype ranged from 98.0% to 99.6%. Our study demonstrates that WGS can correctly identify Campylobacter species and predict antimicrobial resistance with a high degree of accuracy. Published by Elsevier Ltd.

Do agreements between adolescent and parent reports on family socioeconomic status vary with household financial stress?

PubMed

Pu, Christy; Huang, Nicole; Chou, Yiing-Jenq

2011-04-19

Many studies compared the degree of concordance between adolescents' and parents' reports on family socioeconomic status (SES). However, none of these studies analyzed whether the degree of concordance varies by different levels of household financial stress. This research examines whether the degree of concordance between adolescents' and parent reports for the three traditional SES measures (parental education, parental occupation and household income) varied with parent-reported household financial stress and relative standard of living. 2,593 adolescents with a mean age of 13 years, and one of their corresponding parents from the Taiwan Longitudinal Youth Project conducted in 2000 were analyzed. Consistency of adolescents' and parents' reports on parental educational attainment, parental occupation and household income were examined by parent-reported household financial stress and relative standard of living. Parent-reported SES variables are closely associated with family financial stress. For all levels of household financial stress, the degree of concordance between adolescent's and parent's reports are highest for parental education (κ ranging from 0.87 to 0.71) followed by parental occupation (κ ranging from 0.50 to 0.34) and household income (κ ranging from 0.43 to 0.31). Concordance for father's education and parental occupation decreases with higher parent-reported financial stress. This phenomenon was less significant for parent-reported relative standard of living. Though the agreement between adolescents' and parents' reports on the three SES measures is generally judged to be good in most cases, using adolescents reports for family SES may still be biased if analysis is not stratified by family financial stress.

Practical limitations to a positive deviance approach for identifying dietary patterns compatible with the reduction of cancer risk.

PubMed

Vossenaar, M; Bermúdez, O I; Anderson, A S; Solomons, N W

2010-08-01

The positive deviance (PD) approach seeks to devise and promote health-promoting practices identified within the most successful member of a society. The World Cancer Research Fund and the American Institute for Cancer Research (WCRF/AICR) recommendations indicate the need for specific dietary behaviours, which may be considered impractical. Thus, it is important to demonstrate ways in which these dietary practices have been achieved from concordant individuals. The present study aimed to assess the feasibility of constructing healthy eating guides in four international settings. Adult participants from the Netherlands (n = 1052), Scotland (n = 849), Mexico (n = 790) and Guatemala (n = 873) enrolled in an international diet survey project. Participants with inadequate diets and current smokers were excluded from the analysis. Concordance with selected WCRF/AICR individual guideline components related to diet and lifestyle were evaluated. A selection of participants was made towards making a set of 14 rotating menus for a cancer-prevention healthy-eating guide. Overall concordance with the WCRF/AICR recommendations was low in all four nations and no participants with an ideal behaviour were found. The selection of candidates for constructing 14 daily menus for a single national guide identified 51, 13 and 12 individuals concordant with 11 of 14 WCRF/AICR recommendation components in Guatemala, Scotland and Mexico, respectively, and 24 individuals concordant with eight of 14 WCRF/AICR components in the Netherlands. The basis for PD guidance for developing dietary recommendations for cancer prevention was strong across all social classes in Guatemala, marginal for Mexico and Scotland, and effectively impossible for the Netherlands.

Rigorous Approach in Investigation of Seismic Structure and Source Characteristicsin Northeast Asia: Hierarchical and Trans-dimensional Bayesian Inversion

NASA Astrophysics Data System (ADS)

Mustac, M.; Kim, S.; Tkalcic, H.; Rhie, J.; Chen, Y.; Ford, S. R.; Sebastian, N.

2015-12-01

Conventional approaches to inverse problems suffer from non-linearity and non-uniqueness in estimations of seismic structures and source properties. Estimated results and associated uncertainties are often biased by applied regularizations and additional constraints, which are commonly introduced to solve such problems. Bayesian methods, however, provide statistically meaningful estimations of models and their uncertainties constrained by data information. In addition, hierarchical and trans-dimensional (trans-D) techniques are inherently implemented in the Bayesian framework to account for involved error statistics and model parameterizations, and, in turn, allow more rigorous estimations of the same. Here, we apply Bayesian methods throughout the entire inference process to estimate seismic structures and source properties in Northeast Asia including east China, the Korean peninsula, and the Japanese islands. Ambient noise analysis is first performed to obtain a base three-dimensional (3-D) heterogeneity model using continuous broadband waveforms from more than 300 stations. As for the tomography of surface wave group and phase velocities in the 5-70 s band, we adopt a hierarchical and trans-D Bayesian inversion method using Voronoi partition. The 3-D heterogeneity model is further improved by joint inversions of teleseismic receiver functions and dispersion data using a newly developed high-efficiency Bayesian technique. The obtained model is subsequently used to prepare 3-D structural Green's functions for the source characterization. A hierarchical Bayesian method for point source inversion using regional complete waveform data is applied to selected events from the region. The seismic structure and source characteristics with rigorously estimated uncertainties from the novel Bayesian methods provide enhanced monitoring and discrimination of seismic events in northeast Asia.

Can Alberta infant motor scale and milani comparetti motor development screening test be rapid alternatives to bayley scales of infant development-II at high-risk infants

PubMed Central

Yıldırım, Zeynep Hoşbay; Aydınlı, Nur; Ekici, Barış; Tatlı, Burak; Çalişkan, Mine

2012-01-01

Purpose: The main object of the present study is to assess neuromotor development of high-risk infants by using three tests, and to determine inter-test concordance and the feasibility of these tests. Materials and Methods: One-hundred and nine patients aged between 0 and 6 months and identified as “high-risk infant” according to the Kliegman's criteria were enrolled to the study. Three different tests were used to assess neuromotor development of the patients: Bayley scales of infant development-II (BSID-II), Alberta infant motor scale (AIMS), and Milani Comparetti Motor Development Screening Test (MCMDST). Results: Correlation analysis was performed between pure scores of BSID-II motor scale and total scores of AIMS. These two tests were highly correlated (r:0.92). Moderate concordance was found between BSID-II and AIMS (k:0.35). Slight concordance was found between BSID-II and MCMDST; and the concordance was slight again for AIMS and MCMDST (k:0.11 and k:0.16, respectively) too. Conclusion: AIMS has a high correlation and consistency with BSID-II and can be used with routine neurological examination as it is based on observations, has few items, and requires less time to complete. PMID:22919192

Can Alberta infant motor scale and milani comparetti motor development screening test be rapid alternatives to bayley scales of infant development-II at high-risk infants.

PubMed

Yıldırım, Zeynep Hoşbay; Aydınlı, Nur; Ekici, Barış; Tatlı, Burak; Calişkan, Mine

2012-07-01

The main object of the present study is to assess neuromotor development of high-risk infants by using three tests, and to determine inter-test concordance and the feasibility of these tests. One-hundred and nine patients aged between 0 and 6 months and identified as "high-risk infant" according to the Kliegman's criteria were enrolled to the study. Three different tests were used to assess neuromotor development of the patients: Bayley scales of infant development-II (BSID-II), Alberta infant motor scale (AIMS), and Milani Comparetti Motor Development Screening Test (MCMDST). Correlation analysis was performed between pure scores of BSID-II motor scale and total scores of AIMS. These two tests were highly correlated (r:0.92). Moderate concordance was found between BSID-II and AIMS (k:0.35). Slight concordance was found between BSID-II and MCMDST; and the concordance was slight again for AIMS and MCMDST (k:0.11 and k:0.16, respectively) too. AIMS has a high correlation and consistency with BSID-II and can be used with routine neurological examination as it is based on observations, has few items, and requires less time to complete.

Guideline-concordant weight-loss programs in an urban area are uncommon and difficult to identify through the Internet

PubMed Central

Bloom, Benjamin; Mehta, Ambereen K.; Clark, Jeanne M.; Gudzune, Kimberly A.

2015-01-01

Objective To determine the reliability of Internet-based information on community-based weight-loss programs and grade their degree of concordance with 2013 American Heart Association, American College of Cardiology, and The Obesity Society weight management guidelines. Methods We conducted an online search for weight-loss programs in the Maryland-Washington, DC-Virginia corridor. We performed content analysis to abstract program components from their websites, and then randomly selected 80 programs for a telephone survey to verify this information. We determined reliability of Internet information in comparison with telephone interview responses. Results Of the 191 programs, we graded 1% as high, 8% as moderate, and 91% as low with respect to guideline concordance based on website content. Fifty-two programs participated in the telephone survey (65% response rate). Program intensity, diet, physical activity, and use of behavioral strategies were underreported on websites as compared to description of these activities during phone interview. Within our subsample, we graded 6% of programs as high based on website information, whereas we graded 19% as high after telephone interview. Conclusions Most weight-loss programs in an urban, mid-Atlantic region do not currently offer guideline-concordant practices and fail to disclose key information online, which may make clinician referrals challenging. PMID:26861769

Liver fibrosis diagnosis by blood test and elastography in chronic hepatitis C: agreement or combination?

PubMed

Calès, P; Boursier, J; Lebigot, J; de Ledinghen, V; Aubé, C; Hubert, I; Oberti, F

2017-04-01

In chronic hepatitis C, the European Association for the Study of the Liver and the Asociacion Latinoamericana para el Estudio del Higado recommend performing transient elastography plus a blood test to diagnose significant fibrosis; test concordance confirms the diagnosis. To validate this rule and improve it by combining a blood test, FibroMeter (virus second generation, Echosens, Paris, France) and transient elastography (constitutive tests) into a single combined test, as suggested by the American Association for the Study of Liver Diseases and the Infectious Diseases Society of America. A total of 1199 patients were included in an exploratory set (HCV, n = 679) or in two validation sets (HCV ± HIV, HBV, n = 520). Accuracy was mainly evaluated by correct diagnosis rate for severe fibrosis (pathological Metavir F ≥ 3, primary outcome) by classical test scores or a fibrosis classification, reflecting Metavir staging, as a function of test concordance. Score accuracy: there were no significant differences between the blood test (75.7%), elastography (79.1%) and the combined test (79.4%) (P = 0.066); the score accuracy of each test was significantly (P < 0.001) decreased in discordant vs. concordant tests. Classification accuracy: combined test accuracy (91.7%) was significantly (P < 0.001) increased vs. the blood test (84.1%) and elastography (88.2%); accuracy of each constitutive test was significantly (P < 0.001) decreased in discordant vs. concordant tests but not with combined test: 89.0 vs. 92.7% (P = 0.118). Multivariate analysis for accuracy showed an interaction between concordance and fibrosis level: in the 1% of patients with full classification discordance and severe fibrosis, non-invasive tests were unreliable. The advantage of combined test classification was confirmed in the validation sets. The concordance recommendation is validated. A combined test, expressed in classification instead of score, improves this rule and validates the recommendation of a combined test, avoiding 99% of biopsies, and offering precise staging. © 2017 John Wiley & Sons Ltd.

76 FR 59167 - Siemens Medical Solutions USA, Inc., Oncology Care Systems Division, Concord, CA; Siemens Medical...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-23

... Medical Solutions USA, Inc., Oncology Care Systems Division, Concord, CA; Siemens Medical Solutions USA... Solutions USA, Inc. (Siemens), Oncology Care Systems Division, Concord, California (subject firm). The...., Oncology Care Systems Division, Concord, California (TA-W-73,158) and Siemens Medical Solutions USA, Inc...

Are Physicians and Patients in Agreement? Exploring Dyadic Concordance

ERIC Educational Resources Information Center

Coran, Justin J.; Koropeckyj-Cox, Tanya; Arnold, Christa L.

2013-01-01

Dyadic concordance in physician-patient interactions can be defined as the extent of agreement between physicians and patients in their perceptions of the clinical encounter. The current research specifically examined two types of concordance: informational concordance--the extent of agreement in physician and patient responses regarding patient…

Understanding the Uncertainty of an Effectiveness-Cost Ratio in Educational Resource Allocation: A Bayesian Approach

ERIC Educational Resources Information Center

Pan, Yilin

2016-01-01

Given the necessity to bridge the gap between what happened and what is likely to happen, this paper aims to explore how to apply Bayesian inference to cost-effectiveness analysis so as to capture the uncertainty of a ratio-type efficiency measure. The first part of the paper summarizes the characteristics of the evaluation data that are commonly…

Bayesian hierarchical model for large-scale covariance matrix estimation.

PubMed

Zhu, Dongxiao; Hero, Alfred O

2007-12-01

Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

Treesearch

Xiaoqian Sun; Zhuoqiong He; John Kabrick

2008-01-01

This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availability, we select three variables, the aspect class, the soil depth and the land type association as covariates for analysis. To allow great flexibility of the smoothness of the random field,...

Quantity of dates trumps quality of dates for dense Bayesian radiocarbon sediment chronologies - Gas ion source 14C dating instructed by simultaneous Bayesian accumulation rate modeling

NASA Astrophysics Data System (ADS)

Rosenheim, B. E.; Firesinger, D.; Roberts, M. L.; Burton, J. R.; Khan, N.; Moyer, R. P.

2016-12-01

Radiocarbon (14C) sediment core chronologies benefit from a high density of dates, even when precision of individual dates is sacrificed. This is demonstrated by a combined approach of rapid 14C analysis of CO2 gas generated from carbonates and organic material coupled with Bayesian statistical modeling. Analysis of 14C is facilitated by the gas ion source on the Continuous Flow Accelerator Mass Spectrometry (CFAMS) system at the Woods Hole Oceanographic Institution's National Ocean Sciences Accelerator Mass Spectrometry facility. This instrument is capable of producing a 14C determination of +/- 100 14C y precision every 4-5 minutes, with limited sample handling (dissolution of carbonates and/or combustion of organic carbon in evacuated containers). Rapid analysis allows over-preparation of samples to include replicates at each depth and/or comparison of different sample types at particular depths in a sediment or peat core. Analysis priority is given to depths that have the least chronologic precision as determined by Bayesian modeling of the chronology of calibrated ages. Use of such a statistical approach to determine the order in which samples are run ensures that the chronology constantly improves so long as material is available for the analysis of chronologic weak points. Ultimately, accuracy of the chronology is determined by the material that is actually being dated, and our combined approach allows testing of different constituents of the organic carbon pool and the carbonate minerals within a core. We will present preliminary results from a deep-sea sediment core abundant in deep-sea foraminifera as well as coastal wetland peat cores to demonstrate statistical improvements in sediment- and peat-core chronologies obtained by increasing the quantity and decreasing the quality of individual dates.

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

Gagné, Jonathan; Lafrenière, David; Doyon, René

We present Bayesian Analysis for Nearby Young AssociatioNs II (BANYAN II), a modified Bayesian analysis for assessing the membership of later-than-M5 objects to any of several Nearby Young Associations (NYAs). In addition to using kinematic information (from sky position and proper motion), this analysis exploits 2MASS-WISE color-magnitude diagrams in which old and young objects follow distinct sequences. As an improvement over our earlier work, the spatial and kinematic distributions for each association are now modeled as ellipsoids whose axes need not be aligned with the Galactic coordinate axes, and we use prior probabilities matching the expected populations of the NYAsmore » considered versus field stars. We present an extensive contamination analysis to characterize the performance of our new method. We find that Bayesian probabilities are generally representative of contamination rates, except when a parallax measurement is considered. In this case contamination rates become significantly smaller and hence Bayesian probabilities for NYA memberships are pessimistic. We apply this new algorithm to a sample of 158 objects from the literature that are either known to display spectroscopic signs of youth or have unusually red near-infrared colors for their spectral type. Based on our analysis, we identify 25 objects as new highly probable candidates to NYAs, including a new M7.5 bona fide member to Tucana-Horologium, making it the latest-type member. In addition, we reveal that a known L2γ dwarf is co-moving with a bright M5 dwarf, and we show for the first time that two of the currently known ultra red L dwarfs are strong candidates to the AB Doradus moving group. Several objects identified here as highly probable members to NYAs could be free-floating planetary-mass objects if their membership is confirmed.« less

Risk analysis of new oral anticoagulants for gastrointestinal bleeding and intracranial hemorrhage in atrial fibrillation patients: a systematic review and network meta-analysis.

PubMed

Xu, Wei-Wei; Hu, Shen-Jiang; Wu, Tao

2017-07-01

Antithrombotic therapy using new oral anticoagulants (NOACs) in patients with atrial fibrillation (AF) has been generally shown to have a favorable risk-benefit profile. Since there has been dispute about the risks of gastrointestinal bleeding (GIB) and intracranial hemorrhage (ICH), we sought to conduct a systematic review and network meta-analysis using Bayesian inference to analyze the risks of GIB and ICH in AF patients taking NOACs. We analyzed data from 20 randomized controlled trials of 91 671 AF patients receiving anticoagulants, antiplatelet drugs, or placebo. Bayesian network meta-analysis of two different evidence networks was performed using a binomial likelihood model, based on a network in which different agents (and doses) were treated as separate nodes. Odds ratios (ORs) and 95% confidence intervals (CIs) were modeled using Markov chain Monte Carlo methods. Indirect comparisons with the Bayesian model confirmed that aspirin+clopidogrel significantly increased the risk of GIB in AF patients compared to the placebo (OR 0.33, 95% CI 0.01-0.92). Warfarin was identified as greatly increasing the risk of ICH compared to edoxaban 30 mg (OR 3.42, 95% CI 1.22-7.24) and dabigatran 110 mg (OR 3.56, 95% CI 1.10-8.45). We further ranked the NOACs for the lowest risk of GIB (apixaban 5 mg) and ICH (apixaban 5 mg, dabigatran 110 mg, and edoxaban 30 mg). Bayesian network meta-analysis of treatment of non-valvular AF patients with anticoagulants suggested that NOACs do not increase risks of GIB and/or ICH, compared to each other.

Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography.

PubMed

Tenan, Matthew S; Tweedell, Andrew J; Haynes, Courtney A

2017-01-01

The timing of muscle activity is a commonly applied analytic method to understand how the nervous system controls movement. This study systematically evaluates six classes of standard and statistical algorithms to determine muscle onset in both experimental surface electromyography (EMG) and simulated EMG with a known onset time. Eighteen participants had EMG collected from the biceps brachii and vastus lateralis while performing a biceps curl or knee extension, respectively. Three established methods and three statistical methods for EMG onset were evaluated. Linear envelope, Teager-Kaiser energy operator + linear envelope and sample entropy were the established methods evaluated while general time series mean/variance, sequential and batch processing of parametric and nonparametric tools, and Bayesian changepoint analysis were the statistical techniques used. Visual EMG onset (experimental data) and objective EMG onset (simulated data) were compared with algorithmic EMG onset via root mean square error and linear regression models for stepwise elimination of inferior algorithms. The top algorithms for both data types were analyzed for their mean agreement with the gold standard onset and evaluation of 95% confidence intervals. The top algorithms were all Bayesian changepoint analysis iterations where the parameter of the prior (p0) was zero. The best performing Bayesian algorithms were p0 = 0 and a posterior probability for onset determination at 60-90%. While existing algorithms performed reasonably, the Bayesian changepoint analysis methodology provides greater reliability and accuracy when determining the singular onset of EMG activity in a time series. Further research is needed to determine if this class of algorithms perform equally well when the time series has multiple bursts of muscle activity.

"A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis".

PubMed

Zhang, Xiang; Faries, Douglas E; Boytsov, Natalie; Stamey, James D; Seaman, John W

2016-09-01

Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care administrative database, in which key clinical measures are often not available. This paper provides an approach to conducting a sensitivity analyses to investigate the impact of unmeasured confounding in observational studies. In a real world osteoporosis comparative effectiveness study, the bone mineral density (BMD) score, an important predictor of fracture risk and a factor in the selection of osteoporosis treatments, is unavailable in the data base and lack of baseline BMD could potentially lead to significant selection bias. We implemented Bayesian twin-regression models, which simultaneously model both the observed outcome and the unobserved unmeasured confounder, using information from external sources. A sensitivity analysis was also conducted to assess the robustness of our conclusions to changes in such external data. The use of Bayesian modeling in this study suggests that the lack of baseline BMD did have a strong impact on the analysis, reversing the direction of the estimated effect (odds ratio of fracture incidence at 24 months: 0.40 vs. 1.36, with/without adjusting for unmeasured baseline BMD). The Bayesian twin-regression models provide a flexible sensitivity analysis tool to quantitatively assess the impact of unmeasured confounding in observational studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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

Testing the effects of suppression and reappraisal on emotional concordance using a multivariate multilevel model.

PubMed

Butler, Emily A; Gross, James J; Barnard, Kobus

2014-04-01

In theory, the essence of emotion is coordination across experiential, behavioral, and physiological systems in the service of functional responding to environmental demands. However, people often regulate emotions, which could either reduce or enhance cross-system concordance. The present study tested the effects of two forms of emotion regulation (expressive suppression, positive reappraisal) on concordance of subjective experience (positive-negative valence), expressive behavior (positive and negative), and physiology (inter-beat interval, skin conductance, blood pressure) during conversations between unacquainted young women. As predicted, participants asked to suppress showed reduced concordance for both positive and negative emotions. Reappraisal instructions also reduced concordance for negative emotions, but increased concordance for positive ones. Both regulation strategies had contagious interpersonal effects on average levels of responding. Suppression reduced overall expression for both regulating and uninstructed partners, while reappraisal reduced negative experience. Neither strategy influenced the uninstructed partners' concordance. These results suggest that emotion regulation impacts concordance by altering the temporal coupling of phasic subsystem responses, rather than by having divergent effects on subsystem tonic levels. Copyright © 2013 Elsevier B.V. All rights reserved.

Concordance Between Clinical Practice and Published Evidence: Findings From Virginia Commonwealth University School of Dentistry.

PubMed

Chiang, Harmeet K; Best, Al M; Sarrett, David C

2017-09-01

To evaluate the concordance between clinical practice and published evidence by dental faculty and graduating students of the Virginia Commonwealth University School of Dentistry. A questionnaire previously developed by the National Dental Practice-Based Research Network with 12 clinical scenarios was administered to VCU faculty and graduating students. Responses were scored as either consistent or inconsistent with published evidence and then analyzed for differences between dental faculty, graduating students, and the national results. There were 43 dental faculty members with at least half-time student contact who responded to the survey. Faculty concordance ranged from 33% to 100%, and general practice faculty had the highest concordance (82%). Eighty-five of the graduating class of 98 responded to the survey, and student concordance ranged from 18% to 92% and averaged 67%. General practice faculty had higher concordance with published evidence than recently graduated dental students. Graduating students and dental faculty demonstrated higher concordance with evidence-based practice than practitioners in the National Dental Practice-Based Research Network. General practice dental faculty demonstrated adequate concordance, but students demonstrated only a medium-level concordance. Practitioners involved in teaching dental students are better able to keep up with evolving evidence and are better able to demonstrate evidence-based practice. Copyright © 2017 Elsevier Inc. All rights reserved.

A quality control study on cytotechnologist-cytopathologist concordance and its relationship to the number of dots on the slide.

PubMed

Bongiovanni, Massimo; De Saussure, Barbara; Kumar, Neeta; Pache, Jean-Claude; Cibas, Edmund S

2009-01-01

To study cytotechnologist (CT)-cytopathologist (CP) concordance for evaluating individual CTs' performance and for quality assurance and educational feedback. The interpretations of individual CTs were compared with the final interpretations (according to the 2001 Bethesda System) of the CP. Concordance percentages and kappa values were calculated for each CT and correlated with the numbers of dots on each slide, years of experience and percentage of work hours devoted to cytology. A total of 10,453 Pap tests were screened by 9 CTs during one year, out of which 993 (9.5%) were referred to one CP for a final interpretation. Mean concordance between the aggregate CT interpretations and those of the CP was 65.5%. Five CTs had good concordance, 3 had moderately good concordance, and one had surprisingly poor concordance that contrasted with good subjective impressions. No correlation was found between concordance and the average number of dots per slide, screening experience in cervicovaginal cytology or percentage of work hours devoted to cytology. Monitoring CT-CP concordance rates can unveil performance issues not detected by subjective impressions. An excessive number of dots per slide may not reflect poor diagnostic precision so much as a lack of confidence in interpretation.

DATMAN: A reliability data analysis program using Bayesian updating

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

Becker, M.; Feltus, M.A.

1996-12-31

Preventive maintenance (PM) techniques focus on the prevention of failures, in particular, system components that are important to plant functions. Reliability-centered maintenance (RCM) improves on the PM techniques by introducing a set of guidelines by which to evaluate the system functions. It also minimizes intrusive maintenance, labor, and equipment downtime without sacrificing system performance when its function is essential for plant safety. Both the PM and RCM approaches require that system reliability data be updated as more component failures and operation time are acquired. Systems reliability and the likelihood of component failures can be calculated by Bayesian statistical methods, whichmore » can update these data. The DATMAN computer code has been developed at Penn State to simplify the Bayesian analysis by performing tedious calculations needed for RCM reliability analysis. DATMAN reads data for updating, fits a distribution that best fits the data, and calculates component reliability. DATMAN provides a user-friendly interface menu that allows the user to choose from several common prior and posterior distributions, insert new failure data, and visually select the distribution that matches the data most accurately.« less

Bayesian dynamic mediation analysis.

PubMed

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Genotype-Specific Concordance of Chlamydia trachomatis Genital Infection Within Heterosexual Partnerships.

PubMed

Schillinger, Julia A; Katz, Barry P; Markowitz, Lauri E; Braslins, Phillip G; Shrier, Lydia A; Madico, Guillermo; Van Der Pol, Barbara; Orr, Donald P; Rice, Peter A; Batteiger, Byron E

2016-12-01

Sexual transmission rates of Chlamydia trachomatis (Ct) cannot be measured directly; however, the study of concordance of Ct infection in sexual partnerships (dyads) can help to illuminate factors influencing Ct transmission. Heterosexual men and women with Ct infection and their sex partners were enrolled and partner-specific coital and behavioral data collected for the prior 30 days. Microbiological data included Ct culture, and nucleic acid amplification testing (NAAT), quantitative Ct polymerase chain reaction, and ompA genotyping. We measured Ct concordance in dyads and factors (correlates) associated with concordance. One hundred twenty-one women and 125 men formed 128 dyads. Overall, 72.9% of male partners of NAAT-positive women and 68.6% of female partners of NAAT-positive men were Ct-infected. Concordance was more common in dyads with culture-positive members (78.6% of male partners, 77% of female partners). Partners of women and men who were NAAT-positive only had lower concordance (33.3%, 46.4%, respectively). Women in concordant dyads had significantly higher median endocervical quantitative Ct polymerase chain reaction values (3,032) compared with CT-infected women in discordant dyads (1013 inclusion forming units DNA equivalents per mL; P < 0.01). Among 54 Ct-concordant dyads with ompA genotype data for both members, 96.2% had identical genotypes. Higher organism load appears associated with concordance among women. Same-genotype chlamydial concordance was high in sexual partnerships. No behavioral factors were sufficiently discriminating to guide partner services activities. Findings may help model coitus-specific transmission probabilities.

Ordering the mob: Insights into replicon and MOB typing schemes from analysis of a curated dataset of publicly available plasmids.

PubMed

Orlek, Alex; Phan, Hang; Sheppard, Anna E; Doumith, Michel; Ellington, Matthew; Peto, Tim; Crook, Derrick; Walker, A Sarah; Woodford, Neil; Anjum, Muna F; Stoesser, Nicole

2017-05-01

Plasmid typing can provide insights into the epidemiology and transmission of plasmid-mediated antibiotic resistance. The principal plasmid typing schemes are replicon typing and MOB typing, which utilize variation in replication loci and relaxase proteins respectively. Previous studies investigating the proportion of plasmids assigned a type by these schemes ('typeability') have yielded conflicting results; moreover, thousands of plasmid sequences have been added to NCBI in recent years, without consistent annotation to indicate which sequences represent complete plasmids. Here, a curated dataset of complete Enterobacteriaceae plasmids from NCBI was compiled, and used to assess the typeability and concordance of in silico replicon and MOB typing schemes. Concordance was assessed at hierarchical replicon type resolutions, from replicon family-level to plasmid multilocus sequence type (pMLST)-level, where available. We found that 85% and 65% of the curated plasmids could be replicon and MOB typed, respectively. Overall, plasmid size and the number of resistance genes were significant independent predictors of replicon and MOB typing success. We found some degree of non-concordance between replicon families and MOB types, which was only partly resolved when partitioning plasmids into finer-resolution groups (replicon and pMLST types). In some cases, non-concordance was attributed to ambiguous boundaries between MOBP and MOBQ types; in other cases, backbone mosaicism was considered a more plausible explanation. β-lactamase resistance genes tended not to show fidelity to a particular plasmid type, though some previously reported associations were supported. Overall, replicon and MOB typing schemes are likely to continue playing an important role in plasmid analysis, but their performance is constrained by the diverse and dynamic nature of plasmid genomes. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Some critical methodological issues in secondary analysis of world health organization data on elderly suicide rates.

PubMed

Shah, Ajit

2009-07-01

Suicides may be misclassified as accidental deaths in countries with strict legal definitions of suicide, with cultural and religious factors leading to poor registration of suicide and stigma attached to suicide. The concordance between four different definitions of suicides was evaluated by examining the relationship between pure suicide and accidental death rates, gender differences, age-associated trends and potential distil risk and protective factors by conducting secondary analysis of the latest World Health Organisation data on elderly death rates. The four definitions of suicide were: (i) one-year pure suicides rates; one-year combined suicide rates (pure suicide rates combined with accidental death rates); (iii) five-year average pure suicide rates; and (iv) five-year average combined suicides rates (pure suicides rates combined with accidental death rates). The predicted negative correlation between pure suicide and accidental death rates was not observed. Gender differences were similar for all four definitions of suicide. There was a highly significant concordance for the findings of age-associated trends between one-year pure and combined suicide rates, one-year and five-year average pure suicide rates, and five-year average pure and combined suicide rates. There was poor concordance between pure and combined suicide rates for both one-year and five-year average data for the 14 potential distil risk and protective factors, but this concordance between one-year and five-year average pure suicide rates was highly significant. The use of one-year pure suicide rates in cross-national ecological studies examining gender differences, age-associated trends and potential distil risk and protective factors is likely to be practical, pragmatic and resource-efficient.

No decision about me without me: concordance operationalised.

PubMed

Snowden, Austyn; Marland, Glenn

2013-05-01

To demonstrate that concordance can be operationalised to the benefit of patients. Concordance can be understood as a composite of knowledge, health beliefs and collaboration. In discussing any clinical decision, it would be ideal if different views could be incorporated to reach the most coherent decision. This is a definition of concordance, a widely agreed ideal in nursing. There are limits, however, that make the practice of concordance problematic. Sometimes there is little time or willingness to discuss issues in depth. Some views of the world are considered more worthy than others. As a consequence, clinical guidelines currently prioritise easier to measure outcomes of negotiation, such as adherence. This discursive article argues that prioritising adherence is a fundamental error, incoherent with current strategic rhetoric such as the Department of Health's 'no decision about me without me'. The impact of prioritising concordance is contrasted with adherence-based interventions. Where adherence is a goal of treatment, non-adherence is considered problematic. This value judgment is not useful and does not occur in a consultation that prioritises concordance. However, concordance is difficult to translate into clinical practice. This article shows that concordance can be operationalised by considering it a composite of health beliefs, knowledge and collaboration. The main thesis is that different behaviours can always be incorporated into a concordance framework. This negates the necessity for adherence as an endpoint in itself. Fifty per cent of people do not take medicines as prescribed. Interventions focused towards improving adherence are only ever partially successful. This is because it presupposes the clinician is right. Concordance by contrast is more coherent with person centred care and thus more likely to generate clinically meaningful outcomes for patients. © 2012 Blackwell Publishing Ltd.

Actual versus perceived peer sexual risk behavior in online youth social networks.

PubMed

Black, Sandra R; Schmiege, Sarah; Bull, Sheana

2013-09-01

Perception of peer behaviors is an important predictor of actual risk behaviors among youth. However, we lack understanding of peer influence through social media and of actual and perceived peer behavior concordance. The purpose of this research is to document the relationship between individual perception of and actual peer sexual risk behavior using online social networks. The data are a result of a secondary analysis of baseline self-reported and peer-reported sexual risk behavior from a cluster randomized trial including 1,029 persons from 162 virtual networks. Individuals (seeds) recruited up to three friends who then recruited additional friends, extending three waves from the seed. ANOVA models compared network means of actual participant behavior across categories of perceived behavior. Concordance varied between reported and perceived behavior, with higher concordance between perceived and reported condom use, multiple partners, concurrent partners, sexual pressure, and drug and alcohol use during sex. Individuals significantly over-reported risk and under-reported protective peer behaviors related to sex.

Impact of the Supervisor Feedback Environment on Creative Performance: A Moderated Mediation Model

PubMed Central

Zhang, Jian; Gong, Zhenxing; Zhang, Shuangyu; Zhao, Yujia

2017-01-01

Studies on the relationship between feedback and creative performance have only focused on the feedback-self and have underestimated the value of the feedback environment. Building on Self Determined Theory, the purpose of this article is to examine the relationship among feedback environment, creative personality, goal self-concordance and creative performance. Hierarchical regression analysis of a sample of 162 supervisor–employee dyads from nine industry firms. The results indicate that supervisor feedback environment is positively related to creative performance, the relationship between the supervisor feedback environment and creative performance is mediated by goal self-concordance perfectly and moderated by creative personality significantly. The mediation effort of goal self-concordance is significantly influenced by creative personality. The implication of improving employees’ creative performance is further discussed. The present study advances several perspectives of previous studies, echoes recent suggestions that organizations interested in stimulating employee creativity might profitably focus on developing work contexts that support it. PMID:28275362

Prediction future asset price which is non-concordant with the historical distribution

NASA Astrophysics Data System (ADS)

Seong, Ng Yew; Hin, Pooi Ah

2015-12-01

This paper attempts to predict the major characteristics of the future asset price which is non-concordant with the distribution estimated from the price today and the prices on a large number of previous days. The three major characteristics of the i-th non-concordant asset price are the length of the interval between the occurrence time of the previous non-concordant asset price and that of the present non-concordant asset price, the indicator which denotes that the non-concordant price is extremely small or large by its values -1 and 1 respectively, and the degree of non-concordance given by the negative logarithm of the probability of the left tail or right tail of which one of the end points is given by the observed future price. The vector of three major characteristics of the next non-concordant price is modelled to be dependent on the vectors corresponding to the present and l - 1 previous non-concordant prices via a 3-dimensional conditional distribution which is derived from a 3(l + 1)-dimensional power-normal mixture distribution. The marginal distribution for each of the three major characteristics can then be derived from the conditional distribution. The mean of the j-th marginal distribution is an estimate of the value of the j-th characteristics of the next non-concordant price. Meanwhile, the 100(α/2) % and 100(1 - α/2) % points of the j-th marginal distribution can be used to form a prediction interval for the j-th characteristic of the next non-concordant price. The performance measures of the above estimates and prediction intervals indicate that the fitted conditional distribution is satisfactory. Thus the incorporation of the distribution of the characteristics of the next non-concordant price in the model for asset price has a good potential of yielding a more realistic model.

Physics of ultrasonic wave propagation in bone and heart characterized using Bayesian parameter estimation

NASA Astrophysics Data System (ADS)

Anderson, Christian Carl

This Dissertation explores the physics underlying the propagation of ultrasonic waves in bone and in heart tissue through the use of Bayesian probability theory. Quantitative ultrasound is a noninvasive modality used for clinical detection, characterization, and evaluation of bone quality and cardiovascular disease. Approaches that extend the state of knowledge of the physics underpinning the interaction of ultrasound with inherently inhomogeneous and isotropic tissue have the potential to enhance its clinical utility. Simulations of fast and slow compressional wave propagation in cancellous bone were carried out to demonstrate the plausibility of a proposed explanation for the widely reported anomalous negative dispersion in cancellous bone. The results showed that negative dispersion could arise from analysis that proceeded under the assumption that the data consist of only a single ultrasonic wave, when in fact two overlapping and interfering waves are present. The confounding effect of overlapping fast and slow waves was addressed by applying Bayesian parameter estimation to simulated data, to experimental data acquired on bone-mimicking phantoms, and to data acquired in vitro on cancellous bone. The Bayesian approach successfully estimated the properties of the individual fast and slow waves even when they strongly overlapped in the acquired data. The Bayesian parameter estimation technique was further applied to an investigation of the anisotropy of ultrasonic properties in cancellous bone. The degree to which fast and slow waves overlap is partially determined by the angle of insonation of ultrasound relative to the predominant direction of trabecular orientation. In the past, studies of anisotropy have been limited by interference between fast and slow waves over a portion of the range of insonation angles. Bayesian analysis estimated attenuation, velocity, and amplitude parameters over the entire range of insonation angles, allowing a more complete characterization of anisotropy. A novel piecewise linear model for the cyclic variation of ultrasonic backscatter from myocardium was proposed. Models of cyclic variation for 100 type 2 diabetes patients and 43 normal control subjects were constructed using Bayesian parameter estimation. Parameters determined from the model, specifically rise time and slew rate, were found to be more reliable in differentiating between subject groups than the previously employed magnitude parameter.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

PubMed

Karabatsos, George

2018-06-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis

PubMed Central

Fancher, Chris M.; Han, Zhen; Levin, Igor; Page, Katharine; Reich, Brian J.; Smith, Ralph C.; Wilson, Alyson G.; Jones, Jacob L.

2016-01-01

A Bayesian inference method for refining crystallographic structures is presented. The distribution of model parameters is stochastically sampled using Markov chain Monte Carlo. Posterior probability distributions are constructed for all model parameters to properly quantify uncertainty by appropriately modeling the heteroskedasticity and correlation of the error structure. The proposed method is demonstrated by analyzing a National Institute of Standards and Technology silicon standard reference material. The results obtained by Bayesian inference are compared with those determined by Rietveld refinement. Posterior probability distributions of model parameters provide both estimates and uncertainties. The new method better estimates the true uncertainties in the model as compared to the Rietveld method. PMID:27550221

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

PubMed

Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D

2016-10-01

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. © The Author 2016. Published by Oxford University Press.

Cross-view gait recognition using joint Bayesian

NASA Astrophysics Data System (ADS)

Li, Chao; Sun, Shouqian; Chen, Xiaoyu; Min, Xin

2017-07-01

Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performance under cross-view condition, we propose Joint Bayesian to model the view variance. We evaluated our prosed method with the largest population (OULP) dataset which makes our result reliable in a statically way. As a result, we confirmed our proposed method significantly outperformed state-of-the-art approaches for both identification and verification tasks. Finally, sensitivity analysis on the number of training subjects was conducted, we find Joint Bayesian could achieve competitive results even with a small subset of training subjects (100 subjects). For further comparison, experimental results, learning models, and test codes are available.

Genetic effects of landscape, habitat preference and demography on three co-occurring turtle species.

PubMed

Reid, Brendan N; Mladenoff, David J; Peery, M Zachariah

2017-02-01

Expanding the scope of landscape genetics beyond the level of single species can help to reveal how species traits influence responses to environmental change. Multispecies studies are particularly valuable in highly threatened taxa, such as turtles, in which the impacts of anthropogenic change are strongly influenced by interspecific differences in life history strategies, habitat preferences and mobility. We sampled approximately 1500 individuals of three co-occurring turtle species across a gradient of habitat change (including varying loss of wetlands and agricultural conversion of upland habitats) in the Midwestern USA. We used genetic clustering and multiple regression methods to identify associations between genetic structure and permanent landscape features, past landscape composition and landscape change in each species. Two aquatic generalists (the painted turtle, Chrysemys picta, and the snapping turtle Chelydra serpentina) both exhibited population genetic structure consistent with isolation by distance, modulated by aquatic landscape features. Genetic divergence for the more terrestrial Blanding's turtle (Emydoidea blandingii), on the other hand, was not strongly associated with geographic distance or aquatic features, and Bayesian clustering analysis indicated that many Emydoidea populations were genetically isolated. Despite long generation times, all three species exhibited associations between genetic structure and postsettlement habitat change, indicating that long generation times may not be sufficient to delay genetic drift resulting from recent habitat fragmentation. The concordances in genetic structure observed between aquatic species, as well as isolation in the endangered, long-lived Emydoidea, reinforce the need to consider both landscape composition and demographic factors in assessing differential responses to habitat change in co-occurring species. © 2016 John Wiley & Sons Ltd.

The Atlantic Ocean: An Impassable Barrier for the Common Octopus, Octopus vulgaris

NASA Astrophysics Data System (ADS)

Perez-Viscasillas, J.; Schizas, N. V.; Jassoud, A.

2016-02-01

Octopus vulgaris (Lamarck 1798) inhabits the Mediterranean, the temperate and tropical coastal waters of the Atlantic Ocean and is also present in the south Indian Ocean and Japan. We questioned the reported widespread distribution and especially the amphi-Atlantic distribution of O. vulgaris by comparing patterns of genetic variation in the Cytochrome Oxidase Subunit I (COI), the 17th intron of the Na(+)/K(+)-ATPase alpha subunit (Na/K-ATPase 17th intron), and 16S genes from several populations throughout the presumed distribution. Bayesian genealogies based on COI sequences resulted in three monophyletic lineages: a Caribbean, a Eurafrican and a Japanese one. The Eurafrican lineage is more closely related to the Japanese than to the Caribbean lineage. Within the Caribbean, the most common mitochondrial haplotype is shared by all sampled locations except for Curaçao. The most common COI haplotype in the Eurafrican group is shared by all populations. The Caribbean octopus exhibits a divergence of 11.5% compared to the Eurafrican and Japanese octopus, whereas the latter groups are 3.1% divergent. The Na/K-ATPase 17th intron data from Caribbean and Mediterranean/Atlantic Spain octopods is concordant with the mitochondrial data set, separating these two populations. The 16s data is still being analysed, but preliminary analysis supports the dual population hypothesis. The reciprocal monophyly observed with both COI and Na/K-ATPase 17th intron between the Caribbean and European O. vulgaris suggests the historical cessation of gene flow between the two sides of the Atlantic and highlights the presence of a highly differentiated Caribbean lineage.

Application and Evaluation of an Expert Judgment Elicitation Procedure for Correlations.

PubMed

Zondervan-Zwijnenburg, Mariëlle; van de Schoot-Hubeek, Wenneke; Lek, Kimberley; Hoijtink, Herbert; van de Schoot, Rens

2017-01-01

The purpose of the current study was to apply and evaluate a procedure to elicit expert judgments about correlations, and to update this information with empirical data. The result is a face-to-face group elicitation procedure with as its central element a trial roulette question that elicits experts' judgments expressed as distributions. During the elicitation procedure, a concordance probability question was used to provide feedback to the experts on their judgments. We evaluated the elicitation procedure in terms of validity and reliability by means of an application with a small sample of experts. Validity means that the elicited distributions accurately represent the experts' judgments. Reliability concerns the consistency of the elicited judgments over time. Four behavioral scientists provided their judgments with respect to the correlation between cognitive potential and academic performance for two separate populations enrolled at a specific school in the Netherlands that provides special education to youth with severe behavioral problems: youth with autism spectrum disorder (ASD), and youth with diagnoses other than ASD. Measures of face-validity, feasibility, convergent validity, coherence, and intra-rater reliability showed promising results. Furthermore, the current study illustrates the use of the elicitation procedure and elicited distributions in a social science application. The elicited distributions were used as a prior for the correlation, and updated with data for both populations collected at the school of interest. The current study shows that the newly developed elicitation procedure combining the trial roulette method with the elicitation of correlations is a promising tool, and that the results of the procedure are useful as prior information in a Bayesian analysis.

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint

PubMed Central

2012-01-01

Background A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). Results The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. Conclusions The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint. PMID:22962944

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.

PubMed

Adrion, Christine; Mansmann, Ulrich

2012-09-10

A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.

Assessing the concordance between illicit drug laws on the books and drug law enforcement: Comparison of three states on the continuum from "decriminalised" to "punitive".

PubMed

Belackova, Vendula; Ritter, Alison; Shanahan, Marian; Hughes, Caitlin E

2017-03-01

Variations in drug laws, as well as variations in enforcement practice, exist across jurisdictions. This study explored the feasibility of categorising drug laws "on the books" in terms of their punitiveness, and the extent of their concordance with "laws in practice" in a cross-national comparison. "Law on the books", classified with respect to both cannabis and other drug offences in the Czech Republic, NSW (AU) and Florida (USA) were analysed in order to establish an ordinal relationship between the three states. Indicators to assess the "laws in practice" covered both police (arrests) and court (sentencing) activity between 2002 and 2013. Parametric and non-parametric tests of equality of means, tests of stationarity and correlation analysis were used to examine the concordance between the ordinal categorisation of "laws on the books" and "laws in practice", as well as trends over time. The Czech Republic had the most lenient drug laws; Florida had the most punitive and NSW was in-between. Examining the indicators of "laws in practice", we found that the population adjusted number of individuals sentenced to prison ranked across the three states was concordant with categorisation of "laws on the books", but the average sentence length and percentage of court cases sentenced to prison were not. Also, the de jure decriminalisation of drug possession in the Czech Republic yielded a far greater share of administrative offenses than the de facto decriminalisation of cannabis use / possession in NSW. Finally, the mean value of most "laws in practice" indicators changed significantly over time although the "laws on the books" didn't change. While some indicators of "laws in practice" were concordant with the ordinal categorisation of drug laws, several indicators of "laws in practice" appeared to operate independently from the drug laws as stated. This has significant implications for drug policy analysis and means that research should not assume they are interchangeable and should consider each separately when designing research. Copyright © 2016 Elsevier B.V. All rights reserved.

[Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

PubMed

Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

2009-01-01

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

Novel species in Talaromyces sect. Islandici isolated from maize and other substrates

USDA-ARS?s Scientific Manuscript database

Talaromyces sect. Islandici was reexamined to determine the prevalence of isolates from maize and the built environment. Using phenotypic analysis, DNA sequencing and phylogenetic and concordance analysis we discovered and described ten new species in section Islandici and one new species in section...

33 CFR 165.1198 - Safety zone; Military Ocean Terminal Concord Safety Zone, Suisun Bay, Military Ocean Terminal...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false Safety zone; Military Ocean Terminal Concord Safety Zone, Suisun Bay, Military Ocean Terminal Concord, CA. 165.1198 Section 165.1198... Limited Access Areas Eleventh Coast Guard District § 165.1198 Safety zone; Military Ocean Terminal Concord...

33 CFR 165.1198 - Safety zone; Military Ocean Terminal Concord Safety Zone, Suisun Bay, Military Ocean Terminal...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false Safety zone; Military Ocean Terminal Concord Safety Zone, Suisun Bay, Military Ocean Terminal Concord, CA. 165.1198 Section 165.1198... Limited Access Areas Eleventh Coast Guard District § 165.1198 Safety zone; Military Ocean Terminal Concord...

Convergence analysis of surrogate-based methods for Bayesian inverse problems

NASA Astrophysics Data System (ADS)

Yan, Liang; Zhang, Yuan-Xiang

2017-12-01

The major challenges in the Bayesian inverse problems arise from the need for repeated evaluations of the forward model, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. Many attempts at accelerating Bayesian inference have relied on surrogates for the forward model, typically constructed through repeated forward simulations that are performed in an offline phase. Although such approaches can be quite effective at reducing computation cost, there has been little analysis of the approximation on posterior inference. In this work, we prove error bounds on the Kullback-Leibler (KL) distance between the true posterior distribution and the approximation based on surrogate models. Our rigorous error analysis show that if the forward model approximation converges at certain rate in the prior-weighted L 2 norm, then the posterior distribution generated by the approximation converges to the true posterior at least two times faster in the KL sense. The error bound on the Hellinger distance is also provided. To provide concrete examples focusing on the use of the surrogate model based methods, we present an efficient technique for constructing stochastic surrogate models to accelerate the Bayesian inference approach. The Christoffel least squares algorithms, based on generalized polynomial chaos, are used to construct a polynomial approximation of the forward solution over the support of the prior distribution. The numerical strategy and the predicted convergence rates are then demonstrated on the nonlinear inverse problems, involving the inference of parameters appearing in partial differential equations.

A novel Bayesian approach to acoustic emission data analysis.

PubMed

Agletdinov, E; Pomponi, E; Merson, D; Vinogradov, A

2016-12-01

Acoustic emission (AE) technique is a popular tool for materials characterization and non-destructive testing. Originating from the stochastic motion of defects in solids, AE is a random process by nature. The challenging problem arises whenever an attempt is made to identify specific points corresponding to the changes in the trends in the fluctuating AE time series. A general Bayesian framework is proposed for the analysis of AE time series, aiming at automated finding the breakpoints signaling a crossover in the dynamics of underlying AE sources. Copyright © 2016 Elsevier B.V. All rights reserved.

Wavelet-Bayesian inference of cosmic strings embedded in the cosmic microwave background

NASA Astrophysics Data System (ADS)

McEwen, J. D.; Feeney, S. M.; Peiris, H. V.; Wiaux, Y.; Ringeval, C.; Bouchet, F. R.

2017-12-01

Cosmic strings are a well-motivated extension to the standard cosmological model and could induce a subdominant component in the anisotropies of the cosmic microwave background (CMB), in addition to the standard inflationary component. The detection of strings, while observationally challenging, would provide a direct probe of physics at very high-energy scales. We develop a framework for cosmic string inference from observations of the CMB made over the celestial sphere, performing a Bayesian analysis in wavelet space where the string-induced CMB component has distinct statistical properties to the standard inflationary component. Our wavelet-Bayesian framework provides a principled approach to compute the posterior distribution of the string tension Gμ and the Bayesian evidence ratio comparing the string model to the standard inflationary model. Furthermore, we present a technique to recover an estimate of any string-induced CMB map embedded in observational data. Using Planck-like simulations, we demonstrate the application of our framework and evaluate its performance. The method is sensitive to Gμ ∼ 5 × 10-7 for Nambu-Goto string simulations that include an integrated Sachs-Wolfe contribution only and do not include any recombination effects, before any parameters of the analysis are optimized. The sensitivity of the method compares favourably with other techniques applied to the same simulations.

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.

Bayesian Analysis for Risk Assessment of Selected Medical Events in Support of the Integrated Medical Model Effort

NASA Technical Reports Server (NTRS)

Gilkey, Kelly M.; Myers, Jerry G.; McRae, Michael P.; Griffin, Elise A.; Kallrui, Aditya S.

2012-01-01

The Exploration Medical Capability project is creating a catalog of risk assessments using the Integrated Medical Model (IMM). The IMM is a software-based system intended to assist mission planners in preparing for spaceflight missions by helping them to make informed decisions about medical preparations and supplies needed for combating and treating various medical events using Probabilistic Risk Assessment. The objective is to use statistical analyses to inform the IMM decision tool with estimated probabilities of medical events occurring during an exploration mission. Because data regarding astronaut health are limited, Bayesian statistical analysis is used. Bayesian inference combines prior knowledge, such as data from the general U.S. population, the U.S. Submarine Force, or the analog astronaut population located at the NASA Johnson Space Center, with observed data for the medical condition of interest. The posterior results reflect the best evidence for specific medical events occurring in flight. Bayes theorem provides a formal mechanism for combining available observed data with data from similar studies to support the quantification process. The IMM team performed Bayesian updates on the following medical events: angina, appendicitis, atrial fibrillation, atrial flutter, dental abscess, dental caries, dental periodontal disease, gallstone disease, herpes zoster, renal stones, seizure, and stroke.

BaTMAn: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2016-12-01

Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.

A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?

PubMed

Pressman, Alice R; Avins, Andrew L; Hubbard, Alan; Satariano, William A

2011-07-01

There is a paucity of literature comparing Bayesian analytic techniques with traditional approaches for analyzing clinical trials using real trial data. We compared Bayesian and frequentist group sequential methods using data from two published clinical trials. We chose two widely accepted frequentist rules, O'Brien-Fleming and Lan-DeMets, and conjugate Bayesian priors. Using the nonparametric bootstrap, we estimated a sampling distribution of stopping times for each method. Because current practice dictates the preservation of an experiment-wise false positive rate (Type I error), we approximated these error rates for our Bayesian and frequentist analyses with the posterior probability of detecting an effect in a simulated null sample. Thus for the data-generated distribution represented by these trials, we were able to compare the relative performance of these techniques. No final outcomes differed from those of the original trials. However, the timing of trial termination differed substantially by method and varied by trial. For one trial, group sequential designs of either type dictated early stopping of the study. In the other, stopping times were dependent upon the choice of spending function and prior distribution. Results indicate that trialists ought to consider Bayesian methods in addition to traditional approaches for analysis of clinical trials. Though findings from this small sample did not demonstrate either method to consistently outperform the other, they did suggest the need to replicate these comparisons using data from varied clinical trials in order to determine the conditions under which the different methods would be most efficient. Copyright © 2011 Elsevier Inc. All rights reserved.

A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?

PubMed Central

Pressman, Alice R.; Avins, Andrew L.; Hubbard, Alan; Satariano, William A.

2014-01-01

Background There is a paucity of literature comparing Bayesian analytic techniques with traditional approaches for analyzing clinical trials using real trial data. Methods We compared Bayesian and frequentist group sequential methods using data from two published clinical trials. We chose two widely accepted frequentist rules, O'Brien–Fleming and Lan–DeMets, and conjugate Bayesian priors. Using the nonparametric bootstrap, we estimated a sampling distribution of stopping times for each method. Because current practice dictates the preservation of an experiment-wise false positive rate (Type I error), we approximated these error rates for our Bayesian and frequentist analyses with the posterior probability of detecting an effect in a simulated null sample. Thus for the data-generated distribution represented by these trials, we were able to compare the relative performance of these techniques. Results No final outcomes differed from those of the original trials. However, the timing of trial termination differed substantially by method and varied by trial. For one trial, group sequential designs of either type dictated early stopping of the study. In the other, stopping times were dependent upon the choice of spending function and prior distribution. Conclusions Results indicate that trialists ought to consider Bayesian methods in addition to traditional approaches for analysis of clinical trials. Though findings from this small sample did not demonstrate either method to consistently outperform the other, they did suggest the need to replicate these comparisons using data from varied clinical trials in order to determine the conditions under which the different methods would be most efficient. PMID:21453792

Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials.

PubMed

Wijeysundera, Duminda N; Austin, Peter C; Hux, Janet E; Beattie, W Scott; Laupacis, Andreas

2009-01-01

Randomized trials generally use "frequentist" statistics based on P-values and 95% confidence intervals. Frequentist methods have limitations that might be overcome, in part, by Bayesian inference. To illustrate these advantages, we re-analyzed randomized trials published in four general medical journals during 2004. We used Medline to identify randomized superiority trials with two parallel arms, individual-level randomization and dichotomous or time-to-event primary outcomes. Studies with P<0.05 in favor of the intervention were deemed "positive"; otherwise, they were "negative." We used several prior distributions and exact conjugate analyses to calculate Bayesian posterior probabilities for clinically relevant effects. Of 88 included studies, 39 were positive using a frequentist analysis. Although the Bayesian posterior probabilities of any benefit (relative risk or hazard ratio<1) were high in positive studies, these probabilities were lower and variable for larger benefits. The positive studies had only moderate probabilities for exceeding the effects that were assumed for calculating the sample size. By comparison, there were moderate probabilities of any benefit in negative studies. Bayesian and frequentist analyses complement each other when interpreting the results of randomized trials. Future reports of randomized trials should include both.

Testing adaptive toolbox models: a Bayesian hierarchical approach.

PubMed

Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

2013-01-01

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

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.

Rediscovery of Good-Turing estimators via Bayesian nonparametrics.

PubMed

Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye

2016-03-01

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library. © 2015, The International Biometric Society.

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

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.

A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test.

PubMed

Yu, Qingzhao; Zhu, Lin; Zhu, Han

2017-11-01

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size. Copyright © 2017 John Wiley & Sons, Ltd.

The Influence of Respondent Characteristics on the Validity of Self-Reported Survey Responses.

PubMed

Guerard, Barbara; Omachonu, Vincent; Harvey, Raymond A; Hernandez, S Robert; Sen, Bisakha

2016-06-01

To examine concordance between member self-reports and the organization's administrative claims data for two key health factors: number of chronic conditions, and number of prescription drugs. Medicare Advantage plan claims data and member survey data from 2011 to 2012. Mailed surveys to 15,000 members, enrolled minimum 6 months, drawn from a random sample of primary care physician practices with at least 200 members. Descriptive statistics were generated for extent of concordance. Multivariable logistic regressions were used to analyze the association of selected respondent characteristics with likelihood of concordance. Concordance for number of chronic conditions was 58.4 percent, with 27.3 percent under-reporting, 14.2 percent over-reporting. Concordance for number of prescription drugs was 56.6 percent with 38.9 percent under-reporting, 4.5 percent over-reporting. Number of prescriptions and assistance in survey completion were associated with higher likelihood of concordance for chronic conditions. Assistance in survey completion and number of chronic conditions were associated with higher concordance, and age and number of prescriptions were associated with lower concordance, for prescription drugs. Self-reported number of chronic conditions and prescription medications are not in high concordance with claims data. Health care researchers and policy makers using patient self-reported data should be aware of these potential biases. © Health Research and Educational Trust.

Concordance of Time-of-Flight MRA and Digital Subtraction Angiography in Adult Primary Central Nervous System Vasculitis.

PubMed

de Boysson, H; Boulouis, G; Parienti, J-J; Touzé, E; Zuber, M; Arquizan, C; Dequatre, N; Detante, O; Bienvenu, B; Aouba, A; Guillevin, L; Pagnoux, C; Naggara, O

2017-10-01

3D-TOF-MRA and DSA are 2 available tools to demonstrate neurovascular involvement in primary central nervous system vasculitis. We aimed to compare the diagnostic concordance of vessel imaging using 3D-TOF-MRA and DSA in patients with primary central nervous system vasculitis. We retrospectively identified all patients included in the French primary central nervous system vasculitis cohort of 85 patients who underwent, at baseline, both intracranial 3D-TOF-MRA and DSA in an interval of no more than 2 weeks and before treatment initiation. Two neuroradiologists independently reviewed all 3D-TOF-MRA and DSA imaging. Brain vasculature was divided into 25 arterial segments. Concordance between 3D-TOF-MRA and DSA for the identification of arterial stenosis was assessed by the Cohen κ Index. Thirty-one patients met the inclusion criteria, including 20 imaged with a 1.5T MR unit and 11 with a 3T MR unit. Among the 25 patients (81%) with abnormal DSA findings, 24 demonstrated abnormal 3D-TOF-MRA findings, whereas all 6 remaining patients with normal DSA findings had normal 3D-TOF-MRA findings. In the per-segment analysis, concordance between 1.5T 3D-TOF-MRA and DSA was 0.82 (95% CI, 0.75-0.93), and between 3T 3D-TOF-MRA and DSA, it was 0.87 (95% CI, 0.78-0.91). 3D-TOF-MRA shows a high concordance with DSA in diagnostic performance when analyzing brain vasculature in patients with primary central nervous system vasculitis. In patients with negative 3T 3D-TOF-MRA findings, the added diagnostic value of DSA is limited. © 2017 by American Journal of Neuroradiology.

Double match of 18F-fluorodeoxyglucose-PET and iomazenil-SPECT improves outcomes of focus resection surgery.

PubMed

Fujimoto, Ayataka; Okanishi, Tohru; Kanai, Sotaro; Sato, Keishiro; Itamura, Shinji; Baba, Shimpei; Nishimura, Mitsuyo; Masui, Takayuki; Enoki, Hideo

2018-06-01

When the results of electroencephalography (EEG), magnetic resonance imaging (MRI), and seizure semiology are discordant or no structural lesion is evident on MRI, single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are important examinations for lateralization or localization of epileptic regions. We hypothesized that the concordance between interictal 2-[ 18 F]fluoro-2-deoxy-D-glucose ( 18 FDG)-PET and iomazenil (IMZ)-SPECT could suggest the epileptogenic lobe in patients with non-lesional findings on MRI. Fifty-nine patients (31 females, 28 males; mean age, 29 years; median age, 27 years; range, 7-56 years) underwent subdural electrode implantation followed by focus resection. All patients underwent 18 FDG-PET, IMZ-SPECT, and focus resection surgery. Follow-up was continued for ≥ 2 years. We evaluated surgical outcomes as seizure-free or not and analyzed correlations between outcomes and concordances of low-uptake lobes on PET, SPECT, or both PET and SPECT to the resection lobes. We used uni- and multivariate logistic regression analyses. In univariate analyses, all three concordances correlated significantly with seizure-free outcomes (PET, p = 0.017; SPECT, p = 0.030; both PET and SPECT, p = 0.006). In multivariate analysis, concordance between resection and low-uptake lobes in both PET and SPECT correlated significantly with seizure-free outcomes (p = 0.004). The odds ratio was 6.0. Concordance between interictal 18 FDG-PET and IMZ-SPECT suggested that the epileptogenic lobe is six times better than each examination alone among patients with non-lesional findings on MRI. IMZ-SPECT and 18 FDG-PET are complementary examinations in the assessment of localization-related epilepsy.

Simultaneous PET-MRI Studies of the Concordance of Atrophy and Hypometabolism in Syndromic Variants of Alzheimer's Disease and Frontotemporal Dementia: An Extended Case Series.

PubMed

Moodley, Kuven K; Perani, Daniela; Minati, Ludovico; Della Rosa, Pasquale Anthony; Pennycook, Frank; Dickson, John C; Barnes, Anna; Contarino, Valeria Elisa; Michopoulou, Sofia; D'Incerti, Ludovico; Good, Catriona; Fallanca, Federico; Vanoli, Emilia Giovanna; Ell, Peter J; Chan, Dennis

2015-01-01

Simultaneous PET-MRI is used to compare patterns of cerebral hypometabolism and atrophy in six different dementia syndromes. The primary objective was to conduct an initial exploratory study regarding the concordance of atrophy and hypometabolism in syndromic variants of Alzheimer's disease (AD) and frontotemporal dementia (FTD). The secondary objective was to determine the effect of image analysis methods on determination of atrophy and hypometabolism. PET and MRI data were acquired simultaneously on 24 subjects with six variants of AD and FTD (n = 4 per group). Atrophy was rated visually and also quantified with measures of cortical thickness. Hypometabolism was rated visually and also quantified using atlas- and SPM-based approaches. Concordance was measured using weighted Cohen's kappa. Atrophy-hypometabolism concordance differed markedly between patient groups; kappa scores ranged from 0.13 (nonfluent/agrammatic variant of primary progressive aphasia, nfvPPA) to 0.49 (posterior cortical variant of AD, PCA). Heterogeneity was also observed within groups; the confidence intervals of kappa scores ranging from 0-0.25 for PCA to 0.29-0.61 for nfvPPA. More widespread MRI and PET changes were identified using quantitative methods than on visual rating. The marked differences in concordance identified in this initial study may reflect differences in the molecular pathologies underlying AD and FTD syndromic variants but also operational differences in the methods used to diagnose these syndromes. The superior ability of quantitative methodologies to detect changes on PET and MRI, if confirmed on larger cohorts, may favor their usage over qualitative visual inspection in future clinical diagnostic practice.

Receipt of National Comprehensive Cancer Network guideline-concordant prostate cancer care among African American and Caucasian American men in North Carolina.

PubMed

Ellis, Shellie D; Blackard, Bonny; Carpenter, William R; Mishel, Merle; Chen, Ronald C; Godley, Paul A; Mohler, James L; Bensen, Jeannette T

2013-06-15

African Americans have a higher incidence of prostate cancer and experience poorer outcomes compared with Caucasian Americans. Racial differences in care are well documented; however, few studies have characterized patients based on their prostate cancer risk category, which is required to differentiate appropriate from inappropriate guideline application. The medical records of a population-based sample of 777 North Carolina men with newly diagnosed prostate cancer were studied to assess the association among patient race, clinical factors, and National Comprehensive Cancer Network (NCCN) guideline-concordant prostate cancer care. African Americans presented with significantly higher Gleason scores (P = .025) and prostate-specific antigen levels (P = .008) than did Caucasian Americans. However, when clinical T stage was considered as well, difference in overall risk category only approached statistical significance (P = .055). Across risk categories, African Americans were less likely to have surgery (58.1% versus 68.0%, P = .004) and more likely to have radiation (39.0% versus 27.4%, P = .001) compared with Caucasian Americans. However, 83.5% of men received guideline-concordant care within 1 year of diagnosis, which did not differ by race in multivariable analysis (odds ratio = 0.83; 95% confidence interval  = 0.54-1.25). Greater patient-perceived access to care was associated with greater odds of receiving guideline-concordant care (odds ratio = 1.06; 95% confidence interval = 1.01-1.12). After controlling for NCCN risk category, there were no racial differences in receipt of guideline-concordant care. Efforts to improve prostate cancer treatment outcomes should focus on improving access to the health care system. Copyright © 2013 American Cancer Society.

Capturing tumor heterogeneity and clonal evolution in solid cancers using circulating tumor DNA analysis.

PubMed

Perdigones, Nieves; Murtaza, Muhammed

2017-06-01

Circulating tumor DNA analysis has emerged as a potential noninvasive alternative to tissue biopsies for tumor genotyping in patients with metastatic cancer. This is particularly attractive in cases where tissue biopsies are contraindicated or repeat genotyping after progression on treatment is required. However, tissue and plasma analysis results are not always concordant and clinical interpretation of discordant results is not completely understood. Discordant results could arise due to analytical limits of assays used for tumor and plasma DNA analysis or due to low overall contribution of tumor-specific DNA in plasma. Once these factors are ruled out, tissue-plasma concordance and quantitative levels of somatic mutations in plasma can capture tumor heterogeneity. During longitudinal follow-up of patients, this feature can be leveraged to track subclonal evolution and to guide combination or sequential adaptive treatment. Here, we summarize recent results evaluating the opportunities and limitations of circulating tumor DNA analysis in the context of tumor heterogeneity and subclonal evolution in patients with advanced cancers. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparison of vacuum and non-vacuum urine tubes for urinary sediment analysis.

PubMed

Topcuoglu, Canan; Sezer, Sevilay; Kosem, Arzu; Ercan, Mujgan; Turhan, Turan

2017-12-01

Urine collection systems with aspiration system for vacuum tubes are becoming increasingly common for urinalysis, especially for microscopic examination of the urine. In this study, we aimed to examine whether vacuum aspiration of the urine sample has any adverse effect on sediment analysis by comparing results from vacuum and non-vacuum urine tubes. The study included totally 213 urine samples obtained from inpatients and outpatients in our hospital. Urine samples were collected to containers with aspiration system for vacuum tubes. Each sample was aliquoted to both vacuum and non-vacuum urine tubes. Urinary sediment analysis was performed using manual microscope. Results were evaluated using chi-square test. Comparison of the sediment analysis results from vacuum and non-vacuum urine tubes showed that results were highly concordant for erythrocyte, leukocyte and epithelial cells (gamma values 1, 0.997, and 0.994, respectively; p < .001). Results were also concordant for urinary casts, crystals and yeast (kappa values 0.815, 0.945 and 1, respectively; p < .001). The results show that in urinary sediment analysis, vacuum aspiration has no adverse effect on the cellular components except on casts.