Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

PubMed Central

Powell, Graham A.; Tudur Smith, Catrin; Marson, Anthony G.

2017-01-01

Objectives To develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment. Methods We analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model. Results Significant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision. Conclusions This is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials. PMID:29267375

Multivariate Normal Tissue Complication Probability Modeling of Heart Valve Dysfunction in Hodgkin Lymphoma Survivors

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

Cella, Laura, E-mail: laura.cella@cnr.it; Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples; Liuzzi, Raffaele

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced asymptomatic heart valvular defects (RVD). Methods and Materials: Fifty-six patients treated with sequential chemoradiation therapy for Hodgkin lymphoma (HL) were retrospectively reviewed for RVD events. Clinical information along with whole heart, cardiac chambers, and lung dose distribution parameters was collected, and the correlations to RVD were analyzed by means of Spearman's rank correlation coefficient (Rs). For the selection of the model order and parameters for NTCP modeling, a multivariate logistic regression method using resampling techniques (bootstrapping) was applied. Model performance was evaluated using the area under themore » receiver operating characteristic curve (AUC). Results: When we analyzed the whole heart, a 3-variable NTCP model including the maximum dose, whole heart volume, and lung volume was shown to be the optimal predictive model for RVD (Rs = 0.573, P<.001, AUC = 0.83). When we analyzed the cardiac chambers individually, for the left atrium and for the left ventricle, an NTCP model based on 3 variables including the percentage volume exceeding 30 Gy (V30), cardiac chamber volume, and lung volume was selected as the most predictive model (Rs = 0.539, P<.001, AUC = 0.83; and Rs = 0.557, P<.001, AUC = 0.82, respectively). The NTCP values increase as heart maximum dose or cardiac chambers V30 increase. They also increase with larger volumes of the heart or cardiac chambers and decrease when lung volume is larger. Conclusions: We propose logistic NTCP models for RVD considering not only heart irradiation dose but also the combined effects of lung and heart volumes. Our study establishes the statistical evidence of the indirect effect of lung size on radio-induced heart toxicity.« less

Does Investor Ownership of Nursing Homes Compromise the Quality of Care?

PubMed Central

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U.

2001-01-01

Objectives. Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. Methods. We analyzed 1998 data from state inspections of 13 693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Results. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Conclusions. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes. PMID:11527781

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

PubMed

Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix

2014-02-15

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2013-05-01

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

Fluid moments of the nonlinear Landau collision operator

DOE PAGES

Hirvijoki, E.; Lingam, M.; Pfefferle, D.; ...

2016-08-09

An important problem in plasma physics is the lack of an accurate and complete description of Coulomb collisions in associated fluid models. To shed light on the problem, this Letter introduces an integral identity involving the multivariate Hermite tensor polynomials and presents a method for computing exact expressions for the fluid moments of the nonlinear Landau collision operator. In conclusion, the proposed methodology provides a systematic and rigorous means of extending the validity of fluid models that have an underlying inverse-square force particle dynamics to arbitrary collisionality and flow.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Factors Associated with Sexual Violence against Men Who Have Sex with Men and Transgendered Individuals in Karnataka, India

PubMed Central

Shaw, Souradet Y.; Lorway, Robert R.; Deering, Kathleen N.; Avery, Lisa; Mohan, H. L.; Bhattacharjee, Parinita; Reza-Paul, Sushena; Isac, Shajy; Ramesh, Banadakoppa M.; Washington, Reynold; Moses, Stephen; Blanchard, James F.

2012-01-01

Objectives There is a lack of information on sexual violence (SV) among men who have sex with men and transgendered individuals (MSM-T) in southern India. As SV has been associated with HIV vulnerability, this study examined health related behaviours and practices associated with SV among MSM-T. Design Data were from cross-sectional surveys from four districts in Karnataka, India. Methods Multivariable logistic regression models were constructed to examine factors related to SV. Multivariable negative binomial regression models examined the association between physician visits and SV. Results A total of 543 MSM-T were included in the study. Prevalence of SV was 18% in the past year. HIV prevalence among those reporting SV was 20%, compared to 12% among those not reporting SV (p = .104). In multivariable models, and among sex workers, those reporting SV were more likely to report anal sex with 5+ casual sex partners in the past week (AOR: 4.1; 95%CI: 1.2–14.3, p = .029). Increased physician visits among those reporting SV was reported only for those involved in sex work (ARR: 1.7; 95%CI: 1.1–2.7, p = .012). Conclusions These results demonstrate high levels of SV among MSM-T populations, highlighting the importance of integrating interventions to reduce violence as part of HIV prevention programs and health services. PMID:22448214

A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods

PubMed Central

Stürmer, Til; Joshi, Manisha; Glynn, Robert J.; Avorn, Jerry; Rothman, Kenneth J.; Schneeweiss, Sebastian

2006-01-01

Objective Propensity score analyses attempt to control for confounding in non-experimental studies by adjusting for the likelihood that a given patient is exposed. Such analyses have been proposed to address confounding by indication, but there is little empirical evidence that they achieve better control than conventional multivariate outcome modeling. Study design and methods Using PubMed and Science Citation Index, we assessed the use of propensity scores over time and critically evaluated studies published through 2003. Results Use of propensity scores increased from a total of 8 papers before 1998 to 71 in 2003. Most of the 177 published studies abstracted assessed medications (N=60) or surgical interventions (N=51), mainly in cardiology and cardiac surgery (N=90). Whether PS methods or conventional outcome models were used to control for confounding had little effect on results in those studies in which such comparison was possible. Only 9 out of 69 studies (13%) had an effect estimate that differed by more than 20% from that obtained with a conventional outcome model in all PS analyses presented. Conclusions Publication of results based on propensity score methods has increased dramatically, but there is little evidence that these methods yield substantially different estimates compared with conventional multivariable methods. PMID:16632131

Assessing alternative measures of wealth in health research.

PubMed

Cubbin, Catherine; Pollack, Craig; Flaherty, Brian; Hayward, Mark; Sania, Ayesha; Vallone, Donna; Braveman, Paula

2011-05-01

We assessed whether it would be feasible to replace the standard measure of net worth with simpler measures of wealth in population-based studies examining associations between wealth and health. We used data from the 2004 Survey of Consumer Finances (respondents aged 25-64 years) and the 2004 Health and Retirement Survey (respondents aged 50 years or older) to construct logistic regression models relating wealth to health status and smoking. For our wealth measure, we used the standard measure of net worth as well as 9 simpler measures of wealth, and we compared results among the 10 models. In both data sets and for both health indicators, models using simpler wealth measures generated conclusions about the association between wealth and health that were similar to the conclusions generated by models using net worth. The magnitude and significance of the odds ratios were similar for the covariates in multivariate models, and the model-fit statistics for models using these simpler measures were similar to those for models using net worth. Our findings suggest that simpler measures of wealth may be acceptable in population-based studies of health.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

Genetic and Environmental Influences on Female Sexual Orientation, Childhood Gender Typicality and Adult Gender Identity

PubMed Central

Burri, Andrea; Cherkas, Lynn; Spector, Timothy; Rahman, Qazi

2011-01-01

Background Human sexual orientation is influenced by genetic and non-shared environmental factors as are two important psychological correlates – childhood gender typicality (CGT) and adult gender identity (AGI). However, researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation between these traits, particularly in women. Methodology/Principal Findings Here we performed a multivariate genetic analysis in a large sample of British female twins (N = 4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a common pathway model best fitted the data. Conclusions/Significance This indicated that a single latent variable influenced by a genetic component and common non-shared environmental component explained the association between the three traits but there was substantial measurement error. These findings highlight common developmental factors affecting differences in sexual orientation. PMID:21760939

Effect of abdominopelvic abscess drain size on drainage time and probability of occlusion

PubMed Central

Rotman, Jessica A.; Getrajdman, George I.; Maybody, Majid; Erinjeri, Joseph P.; Yarmohammadi, Hooman; Sofocleous, Constantinos T.; Solomon, Stephen B.; Boas, F. Edward

2016-01-01

Background The purpose of this study is to determine whether larger abdominopelvic abscess drains reduce the time required for abscess resolution, or the probability of tube occlusion. Methods 144 consecutive patients who underwent abscess drainage at a single institution were reviewed retrospectively. Results: Larger initial drain size did not reduce drainage time, drain occlusion, or drain exchanges (p>0.05). Subgroup analysis did not find any type of collection that benefitted from larger drains. A multivariate model predicting drainage time showed that large collections (>200 ml) required 16 days longer drainage time than small collections (<50 ml). Collections with a fistula to bowel required 17 days longer drainage time than collections without a fistula. Initial drain size and the viscosity of the fluid in the collection had no significant effect on drainage time in the multivariate model. Conclusions 8 F drains are adequate for initial drainage of most serous and serosanguineous collections. 10 F drains are adequate for initial drainage of most purulent or bloody collections. PMID:27634422

Health-state utilities in a prisoner population: a cross-sectional survey

PubMed Central

Chong, Christopher AKY; Li, Sicong; Nguyen, Geoffrey C; Sutton, Andrew; Levy, Michael H; Butler, Tony; Krahn, Murray D; Thein, Hla-Hla

2009-01-01

Background Health-state utilities for prisoners have not been described. Methods We used data from a 1996 cross-sectional survey of Australian prisoners (n = 734). Respondent-level SF-36 data was transformed into utility scores by both the SF-6D and Nichol's method. Socio-demographic and clinical predictors of SF-6D utility were assessed in univariate analyses and a multivariate general linear model. Results The overall mean SF-6D utility was 0.725 (SD 0.119). When subdivided by various medical conditions, prisoner SF-6D utilities ranged from 0.620 for angina to 0.764 for those with none/mild depressive symptoms. Utilities derived by the Nichol's method were higher than SF-6D scores, often by more than 0.1. In multivariate analysis, significant independent predictors of worse utility included female gender, increasing age, increasing number of comorbidities and more severe depressive symptoms. Conclusion The utilities presented may prove useful for future economic and decision models evaluating prison-based health programs. PMID:19715571

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

A Prospective Cohort Study on Radiation-induced Hypothyroidism: Development of an NTCP Model

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

Boomsma, Marjolein J.; Bijl, Hendrik P.; Christianen, Miranda E.M.C.

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism. Methods and Materials: The thyroid-stimulating hormone (TSH) level of 105 patients treated with (chemo-) radiation therapy for head-and-neck cancer was prospectively measured during a median follow-up of 2.5 years. Hypothyroidism was defined as elevated serum TSH with decreased or normal free thyroxin (T4). A multivariate logistic regression model with bootstrapping was used to determine the most important prognostic variables for radiation-induced hypothyroidism. Results: Thirty-five patients (33%) developed primary hypothyroidism within 2 years after radiation therapy. An NTCP model based on 2 variables, including the mean thyroidmore » gland dose and the thyroid gland volume, was most predictive for radiation-induced hypothyroidism. NTCP values increased with higher mean thyroid gland dose (odds ratio [OR]: 1.064/Gy) and decreased with higher thyroid gland volume (OR: 0.826/cm{sup 3}). Model performance was good with an area under the curve (AUC) of 0.85. Conclusions: This is the first prospective study resulting in an NTCP model for radiation-induced hypothyroidism. The probability of hypothyroidism rises with increasing dose to the thyroid gland, whereas it reduces with increasing thyroid gland volume.« less

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Disparate molecular, histopathology, and clinical factors in HNSCC racial groups

PubMed Central

Worsham, Maria J.; Stephen, Josena K.; Lu, Mei; Chen, Kang Mei; Havard, Shaleta; Shah, Veena; Schweitzer, Vanessa P.

2013-01-01

Objective The causes of the differences in the higher incidence of and the mortality from head and neck squamous cell carcinoma (HNSCC) in African American (AA) versus Caucasian Americans (CA) lack a consensus. We examined a comprehensive array of risk factors influencing health and disease in an access to care, racially diverse, primary HNSCC cohort. Study Design Cross-sectional study. Setting Primary care academic health care system. Subjects and Methods The cohort of 673 comprised 391 CA and 282 AA (42%). Risk variables included demographic, histopathology, and clinical/epidemiologic factors. Tumor DNA was interrogated for loss and gain of 113 genes with known involvement in HNSCC/cancer. Logistic regression for univariate analysis was followed by multivariate modeling with determination of model predictability (c-index). Results Of the 39 univariate differences between AA and CA, multivariate modeling (c-index=0.81) retained seven (p<0.05). AA were less likely to be married, more likely to have tumor lymphocytic response, undergo radiation treatment, and smoke. Insurance type was a significant predictor of race. AA were more likely to have Medicaid, Medicare, and other HMO types. AA tumors were more likely to have loss of CDKN2A and gain of SCYA3 versus CA. Conclusions Multivariate modeling indicated significant differences between AA and CA HNSCC for histopathology, treatment, smoking, marital status, type of insurance, as well as tumor gene copy number alterations. Our data reiterate that for HNSCC as in the case of other complex diseases, tumor genetics or biology is only one of many potential contributors to differences among racial groups. PMID:22412179

A retrospective review of fall risk factors in the bone marrow transplant inpatient service.

PubMed

Vela, Cory M; Grate, Lisa M; McBride, Ali; Devine, Steven; Andritsos, Leslie A

2018-06-01

Purpose The purpose of this study was to compare medications and potential risk factors between patients who experienced a fall during hospitalization compared to those who did not fall while admitted to the Blood and Marrow Transplant inpatient setting at The James Cancer Hospital. Secondary objectives included evaluation of transplant-related disease states and medications in the post-transplant setting that may lead to an increased risk of falls, post-fall variables, and number of tests ordered after a fall. Methods This retrospective, case-control study matched patients in a 2:1 ratio of nonfallers to fallers. Data from The Ohio State University Wexner Medical Center (OSUWMC) reported fall events and patient electronic medical records were utilized. A total of 168 adult Blood and Marrow Transplant inpatients with a hematological malignancy diagnosis were evaluated from 1 January 2010 to 30 September 2012. Results Univariable and multivariable conditional logistic regression models were used to assess the relationship between potential predictor variables of interest and falls. Variables that were found to be significant predictors of falls from the univariable models include age group, incontinence, benzodiazepines, corticosteroids, anticonvulsants and antidepressants, and number of days status-post transplant. When considered for a multivariable model age group, corticosteroids, and a cancer diagnosis of leukemia were significant in the final model. Conclusion Recent medication utilization such as benzodiazepines, anticonvulsants, corticosteroids, and antidepressants placed patients at a higher risk of experiencing a fall. Other significant factors identified from a multivariable analysis found were patients older than age 65, patients with recent corticosteroid administration and a cancer diagnosis of leukemia.

Multivariate statistical assessment of predictors of firefighters' muscular and aerobic work capacity.

PubMed

Lindberg, Ann-Sofie; Oksa, Juha; Antti, Henrik; Malm, Christer

2015-01-01

Physical capacity has previously been deemed important for firefighters physical work capacity, and aerobic fitness, muscular strength, and muscular endurance are the most frequently investigated parameters of importance. Traditionally, bivariate and multivariate linear regression statistics have been used to study relationships between physical capacities and work capacities among firefighters. An alternative way to handle datasets consisting of numerous correlated variables is to use multivariate projection analyses, such as Orthogonal Projection to Latent Structures. The first aim of the present study was to evaluate the prediction and predictive power of field and laboratory tests, respectively, on firefighters' physical work capacity on selected work tasks. Also, to study if valid predictions could be achieved without anthropometric data. The second aim was to externally validate selected models. The third aim was to validate selected models on firefighters' and on civilians'. A total of 38 (26 men and 12 women) + 90 (38 men and 52 women) subjects were included in the models and the external validation, respectively. The best prediction (R2) and predictive power (Q2) of Stairs, Pulling, Demolition, Terrain, and Rescue work capacities included field tests (R2 = 0.73 to 0.84, Q2 = 0.68 to 0.82). The best external validation was for Stairs work capacity (R2 = 0.80) and worst for Demolition work capacity (R2 = 0.40). In conclusion, field and laboratory tests could equally well predict physical work capacities for firefighting work tasks, and models excluding anthropometric data were valid. The predictive power was satisfactory for all included work tasks except Demolition.

Multivariate Analyses of Rotator Cuff Pathologies in Shoulder Disability

PubMed Central

Henseler, Jan F.; Raz, Yotam; Nagels, Jochem; van Zwet, Erik W.; Raz, Vered; Nelissen, Rob G. H. H.

2015-01-01

Background Disability of the shoulder joint is often caused by a tear in the rotator cuff (RC) muscles. Four RC muscles coordinate shoulder movement and stability, among them the supraspinatus and infraspinatus muscle which are predominantly torn. The contribution of each RC muscle to tear pathology is not fully understood. We hypothesized that muscle atrophy and fatty infiltration, features of RC muscle degeneration, are predictive of superior humeral head translation and shoulder functional disability. Methods Shoulder features, including RC muscle surface area and fatty infiltration, superior humeral translation and RC tear size were obtained from a consecutive series of Magnetic Resonance Imaging with arthrography (MRA). We investigated patients with superior (supraspinatus, n = 39) and posterosuperior (supraspinatus and infraspinatus, n = 30) RC tears, and patients with an intact RC (n = 52) as controls. The individual or combinatorial contribution of RC measures to superior humeral translation, as a sign of RC dysfunction, was investigated with univariate or multivariate models, respectively. Results Using the univariate model the infraspinatus surface area and fatty infiltration in both the supraspinatus and infraspinatus had a significant contribution to RC dysfunction. With the multivariate model, however, the infraspinatus surface area only affected superior humeral translation (p<0.001) and discriminated between superior and posterosuperior tears. In contrast neither tear size nor fatty infiltration of the supraspinatus or infraspinatus contributed to superior humeral translation. Conclusion Our study reveals that infraspinatus atrophy has the strongest contribution to RC tear pathologies. This suggests a pivotal role for the infraspinatus in preventing shoulder disability. PMID:25710703

Factorial Design Based Multivariate Modeling and Optimization of Tunable Bioresponsive Arginine Grafted Poly(cystaminebis(acrylamide)-diaminohexane) Polymeric Matrix Based Nanocarriers.

PubMed

Yang, Rongbing; Nam, Kihoon; Kim, Sung Wan; Turkson, James; Zou, Ye; Zuo, Yi Y; Haware, Rahul V; Chougule, Mahavir B

2017-01-03

Desired characteristics of nanocarriers are crucial to explore its therapeutic potential. This investigation aimed to develop tunable bioresponsive newly synthesized unique arginine grafted poly(cystaminebis(acrylamide)-diaminohexane) [ABP] polymeric matrix based nanocarriers by using L9 Taguchi factorial design, desirability function, and multivariate method. The selected formulation and process parameters were ABP concentration, acetone concentration, the volume ratio of acetone to ABP solution, and drug concentration. The measured nanocarrier characteristics were particle size, polydispersity index, zeta potential, and percentage drug loading. Experimental validation of nanocarrier characteristics computed from initially developed predictive model showed nonsignificant differences (p > 0.05). The multivariate modeling based optimized cationic nanocarrier formulation of <100 nm loaded with hydrophilic acetaminophen was readapted for a hydrophobic etoposide loading without significant changes (p > 0.05) except for improved loading percentage. This is the first study focusing on ABP polymeric matrix based nanocarrier development. Nanocarrier particle size was stable in PBS 7.4 for 48 h. The increase of zeta potential at lower pH 6.4, compared to the physiological pH, showed possible endosomal escape capability. The glutathione triggered release at the physiological conditions indicated the competence of cytosolic targeting delivery of the loaded drug from bioresponsive nanocarriers. In conclusion, this unique systematic approach provides rational evaluation and prediction of a tunable bioresponsive ABP based matrix nanocarrier, which was built on selected limited number of smart experimentation.

Pooled Analysis of Individual Patient Data on Concurrent Chemoradiotherapy for Stage III Non-Small-Cell Lung Cancer in Elderly Patients Compared With Younger Patients Who Participated in US National Cancer Institute Cooperative Group Studies.

PubMed

Stinchcombe, Thomas E; Zhang, Ying; Vokes, Everett E; Schiller, Joan H; Bradley, Jeffrey D; Kelly, Karen; Curran, Walter J; Schild, Steven E; Movsas, Benjamin; Clamon, Gerald; Govindan, Ramaswamy; Blumenschein, George R; Socinski, Mark A; Ready, Neal E; Akerley, Wallace L; Cohen, Harvey J; Pang, Herbert H; Wang, Xiaofei

2017-09-01

Purpose Concurrent chemoradiotherapy is standard treatment for patients with stage III non-small-cell lung cancer. Elderly patients may experience increased rates of adverse events (AEs) or less benefit from concurrent chemoradiotherapy. Patients and Methods Individual patient data were collected from 16 phase II or III trials conducted by US National Cancer Institute-supported cooperative groups of concurrent chemoradiotherapy alone or with consolidation or induction chemotherapy for stage III non-small-cell lung cancer from 1990 to 2012. Overall survival (OS), progression-free survival, and AEs were compared between patients age ≥ 70 (elderly) and those younger than 70 years (younger). Unadjusted and adjusted hazard ratios (HRs) for survival time and CIs were estimated by single-predictor and multivariable frailty Cox models. Unadjusted and adjusted odds ratio (ORs) for AEs and CIs were obtained from single-predictor and multivariable generalized linear mixed-effect models. Results A total of 2,768 patients were classified as younger and 832 as elderly. In unadjusted and multivariable models, elderly patients had worse OS (HR, 1.20; 95% CI, 1.09 to 1.31 and HR, 1.17; 95% CI, 1.07 to 1.29, respectively). In unadjusted and multivariable models, elderly and younger patients had similar progression-free survival (HR, 1.01; 95% CI, 0.93 to 1.10 and HR, 1.00; 95% CI, 0.91 to 1.09, respectively). Elderly patients had a higher rate of grade ≥ 3 AEs in unadjusted and multivariable models (OR, 1.35; 95% CI, 1.07 to 1.70 and OR, 1.38; 95% CI, 1.10 to 1.74, respectively). Grade 5 AEs were significantly higher in elderly compared with younger patients (9% v 4%; P < .01). Fewer elderly compared with younger patients completed treatment (47% v 57%; P < .01), and more discontinued treatment because of AEs (20% v 13%; P < .01), died during treatment (7.8% v 2.9%; P < .01), and refused further treatment (5.8% v 3.9%; P = .02). Conclusion Elderly patients in concurrent chemoradiotherapy trials experienced worse OS, more toxicity, and had a higher rate of death during treatment than younger patients.

PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

2014-10-01

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the multivariate adaptive regression splines (MARS) technique, conclusions of this research work are exposed.

Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

PubMed Central

Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT. PMID:24586971

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Fasting Glucose, Obesity, and Coronary Artery Calcification in Community-Based People Without Diabetes

PubMed Central

Rutter, Martin K.; Massaro, Joseph M.; Hoffmann, Udo; O’Donnell, Christopher J.; Fox, Caroline S.

2012-01-01

OBJECTIVE Our objective was to assess whether impaired fasting glucose (IFG) and obesity are independently related to coronary artery calcification (CAC) in a community-based population. RESEARCH DESIGN AND METHODS We assessed CAC using multidetector computed tomography in 3,054 Framingham Heart Study participants (mean [SD] age was 50 [10] years, 49% were women, 29% had IFG, and 25% were obese) free from known vascular disease or diabetes. We tested the hypothesis that IFG (5.6–6.9 mmol/L) and obesity (BMI ≥30 kg/m2) were independently associated with high CAC (>90th percentile for age and sex) after adjusting for hypertension, lipids, smoking, and medication. RESULTS High CAC was significantly related to IFG in an age- and sex-adjusted model (odds ratio 1.4 [95% CI 1.1–1.7], P = 0.002; referent: normal fasting glucose) and after further adjustment for obesity (1.3 [1.0–1.6], P = 0.045). However, IFG was not associated with high CAC in multivariable-adjusted models before (1.2 [0.9–1.4], P = 0.20) or after adjustment for obesity. Obesity was associated with high CAC in age- and sex-adjusted models (1.6 [1.3–2.0], P < 0.001) and in multivariable models that included IFG (1.4 [1.1–1.7], P = 0.005). Multivariable-adjusted spline regression models suggested nonlinear relationships linking high CAC with BMI (J-shaped), waist circumference (J-shaped), and fasting glucose. CONCLUSIONS In this community-based cohort, CAC was associated with obesity, but not IFG, after adjusting for important confounders. With the increasing worldwide prevalence of obesity and nondiabetic hyperglycemia, these data underscore the importance of obesity in the pathogenesis of CAC. PMID:22773705

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

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

Tucker, Susan L., E-mail: sltucker@mdanderson.org; Li Minghuan; Xu Ting

2013-01-01

Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk ofmore » severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.« less

Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

PubMed Central

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.

2014-01-01

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

Health Literacy, Cognitive Abilities, and Mortality Among Elderly Persons

PubMed Central

Wolf, Michael S.; Feinglass, Joseph; Thompson, Jason A.

2008-01-01

Background Low health literacy and low cognitive abilities both predict mortality, but no study has jointly examined these relationships. Methods We conducted a prospective cohort study of 3,260 community-dwelling adults age 65 and older. Participants were interviewed in 1997 and administered the Short Test of Functional Health Literacy in Adults and the Mini Mental Status Examination. Mortality was determined using the National Death Index through 2003. Measurements and Main Results In multivariate models with only literacy (not cognition), the adjusted hazard ratio was 1.50 (95% confidence of interval [CI] 1.24–1.81) for inadequate versus adequate literacy. In multivariate models without literacy, delayed recall of 3 items and the ability to serial subtract numbers were associated with higher mortality (e.g., adjusted hazard ratios [AHR] 1.74 [95% CI 1.30–2.34] for recall of zero versus 3 items, and 1.32 [95% CI 1.09–1.60] for 0–2 vs 5 correct subtractions). In multivariate analysis with both literacy and cognition, the AHRs for the cognition items were similar, but the AHR for inadequate literacy decreased to 1.27 (95% CI 1.03 – 1.57). Conclusions Both health literacy and cognitive abilities independently predict mortality. Interventions to improve patient knowledge and self-management skills should consider both the reading level and cognitive demands of the materials. PMID:18330654

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

PubMed

Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

2011-11-15

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

PubMed Central

2014-01-01

Background Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. Methods The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Results Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Conclusions Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately. PMID:25047164

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Examining a Comprehensive Model of Disaster-Related Posttraumatic Stress Disorder in Systematically Studied Survivors of 10 Disasters

PubMed Central

Oliver, Julianne; Pandya, Anand

2012-01-01

Objectives. Using a comprehensive disaster model, we examined predictors of posttraumatic stress disorder (PTSD) in combined data from 10 different disasters. Methods. The combined sample included data from 811 directly exposed survivors of 10 disasters between 1987 and 1995. We used consistent methods across all 10 disaster samples, including full diagnostic assessment. Results. In multivariate analyses, predictors of PTSD were female gender, younger age, Hispanic ethnicity, less education, ever-married status, predisaster psychopathology, disaster injury, and witnessing injury or death; exposure through death or injury to friends or family members and witnessing the disaster aftermath did not confer additional PTSD risk. Intentionally caused disasters associated with PTSD in bivariate analysis did not independently predict PTSD in multivariate analysis. Avoidance and numbing symptoms represented a PTSD marker. Conclusions. Despite confirming some previous research findings, we found no associations between PTSD and disaster typology. Prospective research is needed to determine whether early avoidance and numbing symptoms identify individuals likely to develop PTSD later. Our findings may help identify at-risk populations for treatment research. PMID:22897543

Sex-specific predictors of inpatient rehabilitation outcomes after traumatic brain injury

PubMed Central

Chan, Vincy; Mollayeva, Tatyana; Ottenbacher, Kenneth J.; Colantonio, Angela

2016-01-01

Objective To identify sex-specific predictors of inpatient rehabilitation outcomes among patients with a traumatic brain injury (TBI) from a population based perspective. Design Retrospective cohort study Setting Ontario, Canada Participants Patients in inpatient rehabilitation for a TBI within one year of acute care discharge between 2008/09 and 2011/12 (N=1,730, 70% male, 30% female). Interventions None Main Outcome Measures Inpatient rehabilitation length of stay, total Functional Independence Measure (FIM™) score, and motor and cognitive FIM™ ratings at discharge. Results Sex, as a covariate in multivariable linear regression models, was not a significant predictor of rehabilitation outcomes. While many of the predictors examined were similar across males and females, sex-specific multivariable models identified some predictors of rehabilitation outcome that are specific for males and females; mechanism of injury (p<.0001) was a significant predictor of functional outcome only among females while comorbidities (p<.0001) was a significant predictor for males only. Conclusions Predictors of outcomes after inpatient rehabilitation differed by sex, providing evidence for a sex-specific approach in planning and resource allocation for inpatient rehabilitation services for patients with TBI. PMID:26836952

The Upper Respiratory Pyramid: Early Factors and Later Treatment Utilization in World Trade Center Exposed Firefighters

PubMed Central

Niles, Justin K.; Webber, Mayris P.; Liu, Xiaoxue; Zeig-Owens, Rachel; Hall, Charles B.; Cohen, Hillel W.; Glaser, Michelle S.; Weakley, Jessica; Schwartz, Theresa M.; Weiden, Michael D.; Nolan, Anna; Aldrich, Thomas K.; Glass, Lara; Kelly, Kerry J.; Prezant, David J.

2015-01-01

Background We investigated early post 9/11 factors that could predict rhinosinusitis healthcare utilization costs up to 11 years later in 8,079 World Trade Center-exposed rescue/recovery workers. Methods We used bivariate and multivariate analytic techniques to investigate utilization outcomes; we also used a pyramid framework to describe rhinosinusitis healthcare groups at early (by 9/11/2005) and late (by 9/11/2012) time points. Results Multivariate models showed that pre-9/11/2005 chronic rhinosinusitis diagnoses and nasal symptoms predicted final year healthcare utilization outcomes more than a decade after WTC exposure. The relative proportion of workers on each pyramid level changed significantly during the study period. Conclusions Diagnoses of chronic rhinosinusitis within 4 years of a major inhalation event only partially explain future healthcare utilization. Exposure intensity, early symptoms and other factors must also be considered when anticipating future healthcare needs. PMID:24898816

Variables related to tobacco use among young adults: are there differences between males and females?

PubMed

Pederson, Linda L; Koval, John J; Chan, Stella S H; Zhang, Xiaohe

2007-02-01

We sought to determine the association of four categories (chunks) of variables: (1) demographic characteristics, (2) family and friends smoking and other drug use, (3) psychosocial factors and attitude, and (4) lifestyle factors to current smoking as compared to never smoking among Canadian young adults. A cohort of 1270 young adults, followed for 10 years, completed a self-administered questionnaire. In multivariable analyses, the best final model for both genders did not include the psychosocial and attitudinal categories, but did contain variables in the demographic, family and friends, and lifestyle categories. Interventions for reducing smoking among young adults may be similar for males and females, a conclusion that differs from conclusions based on findings from younger age groups.

Network Analysis of Associations between Serum Interferon Alpha Activity, Autoantibodies, and Clinical Features in Systemic Lupus Erythematosus

PubMed Central

Weckerle, Corinna E.; Franek, Beverly S.; Kelly, Jennifer A.; Kumabe, Marissa; Mikolaitis, Rachel A.; Green, Stephanie L.; Utset, Tammy O.; Jolly, Meenakshi; James, Judith A.; Harley, John B.; Niewold, Timothy B.

2010-01-01

Background Interferon-alpha (IFN-α) is a primary pathogenic factor in systemic lupus erythematosus (SLE), and high IFN-α levels may be associated with particular clinical manifestations. The prevalence of individual clinical and serologic features differs significantly by ancestry. We used multivariate and network analyses to detect associations between clinical and serologic disease manifestations and serum IFN-α activity in a large diverse SLE cohort. Methods 1089 SLE patients were studied (387 African-American, 186 Hispanic-American, and 516 European-American). Presence or absence of ACR clinical criteria for SLE, autoantibodies, and serum IFN-α activity data were analyzed in univariate and multivariate models. Iterative multivariate logistic regression was performed in each background separately to establish the network of associations between variables that were independently significant following Bonferroni correction. Results In all ancestral backgrounds, high IFN-α activity was associated with anti-Ro and anti-dsDNA antibodies (p-values 4.6×10−18 and 2.9 × 10−16 respectively). Younger age, non-European ancestry, and anti-RNP were also independently associated with increased serum IFN-α activity (p≤6.7×10−4). We found 14 unique associations between variables in network analysis, and only 7 of these associations were shared by more than one ancestral background. Associations between clinical criteria were different in different ancestral backgrounds, while autoantibody-IFN-α relationships were similar across backgrounds. IFN-α activity and autoantibodies were not associated with ACR clinical features in multivariate models. Conclusions Serum IFN-α activity was strongly and consistently associated with autoantibodies, and not independently associated with clinical features in SLE. IFN-α may be more relevant to humoral tolerance and initial pathogenesis than later clinical disease manifestations. PMID:21162028

The Association Between Internet Use and Ambulatory Care-Seeking Behaviors in Taiwan: A Cross-Sectional Study

PubMed Central

Chen, Tsung-Fu; Liang, Jyh-Chong; Lin, Tzu-Bin; Tsai, Chin-Chung

2016-01-01

Background Compared with the traditional ways of gaining health-related information from newspapers, magazines, radio, and television, the Internet is inexpensive, accessible, and conveys diverse opinions. Several studies on how increasing Internet use affected outpatient clinic visits were inconclusive. Objective The objective of this study was to examine the role of Internet use on ambulatory care-seeking behaviors as indicated by the number of outpatient clinic visits after adjusting for confounding variables. Methods We conducted this study using a sample randomly selected from the general population in Taiwan. To handle the missing data, we built a multivariate logistic regression model for propensity score matching using age and sex as the independent variables. The questionnaires with no missing data were then included in a multivariate linear regression model for examining the association between Internet use and outpatient clinic visits. Results We included a sample of 293 participants who answered the questionnaire with no missing data in the multivariate linear regression model. We found that Internet use was significantly associated with more outpatient clinic visits (P=.04). The participants with chronic diseases tended to make more outpatient clinic visits (P<.01). Conclusions The inconsistent quality of health-related information obtained from the Internet may be associated with patients’ increasing need for interpreting and discussing the information with health care professionals, thus resulting in an increasing number of outpatient clinic visits. In addition, the media literacy of Web-based health-related information seekers may also affect their ambulatory care-seeking behaviors, such as outpatient clinic visits. PMID:27927606

The EXCITE Trial: Predicting a Clinically Meaningful Motor Activity Log Outcome

PubMed Central

Park, Si-Woon; Wolf, Steven L.; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S.

2013-01-01

Background and Objective This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. Methods A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Results Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Conclusions Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT. PMID:18780883

Socio-Demographic and Clinical Characteristics are Not Clinically Useful Predictors of Refill Adherence in Patients with Hypertension

PubMed Central

Steiner, John F.; Ho, P. Michael; Beaty, Brenda L.; Dickinson, L. Miriam; Hanratty, Rebecca; Zeng, Chan; Tavel, Heather M.; Havranek, Edward P.; Davidson, Arthur J.; Magid, David J.; Estacio, Raymond O.

2009-01-01

Background Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in two health care delivery systems. Methods and Results Retrospective cohort studies of hypertension registries in an inner-city health care delivery system (N = 17176) and a health maintenance organization (N = 94297) in Denver, Colorado. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than non-adherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed less than three antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from non-adherent patients (C-statistic = 0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from non-adherent individuals (C-statistic = 0.576). Conclusions Although certain socio-demographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment. PMID:20031876

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Negative Events in Childhood Predict Trajectories of Internalizing Symptoms Up to Young Adulthood: An 18-Year Longitudinal Study

PubMed Central

Melchior, Maria; Touchette, Évelyne; Prokofyeva, Elena; Chollet, Aude; Fombonne, Eric; Elidemir, Gulizar; Galéra, Cédric

2014-01-01

Background Common negative events can precipitate the onset of internalizing symptoms. We studied whether their occurrence in childhood is associated with mental health trajectories over the course of development. Methods Using data from the TEMPO study, a French community-based cohort study of youths, we studied the association between negative events in 1991 (when participants were aged 4–16 years) and internalizing symptoms, assessed by the ASEBA family of instruments in 1991, 1999, and 2009 (n = 1503). Participants' trajectories of internalizing symptoms were estimated with semi-parametric regression methods (PROC TRAJ). Data were analyzed using multinomial regression models controlled for participants' sex, age, parental family status, socio-economic position, and parental history of depression. Results Negative childhood events were associated with an increased likelihood of concurrent internalizing symptoms which sometimes persisted into adulthood (multivariate ORs associated with > = 3 negative events respectively: high and decreasing internalizing symptoms: 5.54, 95% CI: 3.20–9.58; persistently high internalizing symptoms: 8.94, 95% CI: 2.82–28.31). Specific negative events most strongly associated with youths' persistent internalizing symptoms included: school difficulties (multivariate OR: 5.31, 95% CI: 2.24–12.59), parental stress (multivariate OR: 4.69, 95% CI: 2.02–10.87), serious illness/health problems (multivariate OR: 4.13, 95% CI: 1.76–9.70), and social isolation (multivariate OR: 2.24, 95% CI: 1.00–5.08). Conclusions Common negative events can contribute to the onset of children's lasting psychological difficulties. PMID:25485875

The genetic basis for cognitive ability, memory, and depression symptomatology in middle-aged and elderly chinese twins.

PubMed

Xu, Chunsheng; Sun, Jianping; Ji, Fuling; Tian, Xiaocao; Duan, Haiping; Zhai, Yaoming; Wang, Shaojie; Pang, Zengchang; Zhang, Dongfeng; Zhao, Zhongtang; Li, Shuxia; Hjelmborg, Jacob V B; Christensen, Kaare; Tan, Qihua

2015-02-01

The genetic influences on aging-related phenotypes, including cognition and depression, have been well confirmed in the Western populations. We performed the first twin-based analysis on cognitive performance, memory and depression status in middle-aged and elderly Chinese twins, representing the world's largest and most rapidly aging population. The sample consisted of 384 twin pairs with a median age of 50 years. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) scale; memory was assessed using the revised Wechsler Adult Intelligence scale; depression symptomatology was evaluated by the self-reported 30-item Geriatric Depression (GDS-30)scale. Both univariate and multivariate twin models were fitted to the three phenotypes with full and nested models and compared to select the best fitting models. Univariate analysis showed moderate-to-high genetic influences with heritability 0.44 for cognition and 0.56 for memory. Multivariate analysis by the reduced Cholesky model estimated significant genetic (rG = 0.69) and unique environmental (rE = 0.25) correlation between cognitive ability and memory. The model also estimated weak but significant inverse genetic correlation for depression with cognition (-0.31) and memory (-0.28). No significant unique environmental correlation was found for depression with other two phenotypes. In conclusion, there can be a common genetic architecture for cognitive ability and memory that weakly correlates with depression symptomatology, but in the opposite direction.

Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes.

PubMed

Alladio, Eugenio; Martyna, Agnieszka; Salomone, Alberto; Pirro, Valentina; Vincenti, Marco; Zadora, Grzegorz

2017-02-01

The detection of direct ethanol metabolites, such as ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs), in scalp hair is considered the optimal strategy to effectively recognize chronic alcohol misuses by means of specific cut-offs suggested by the Society of Hair Testing. However, several factors (e.g. hair treatments) may alter the correlation between alcohol intake and biomarkers concentrations, possibly introducing bias in the interpretative process and conclusions. 125 subjects with various drinking habits were subjected to blood and hair sampling to determine indirect (e.g. CDT) and direct alcohol biomarkers. The overall data were investigated using several multivariate statistical methods. A likelihood ratio (LR) approach was used for the first time to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers. LR strategies provide a more robust outcome than the plain comparison with cut-off values, where tiny changes in the analytical results can lead to dramatic divergence in the way they are interpreted. An LR model combining EtG and FAEEs hair concentrations proved to discriminate non-chronic from chronic consumers with ideal correct classification rates, whereas the contribution of indirect biomarkers proved to be negligible. Optimal results were observed using a novel approach that associates LR methods with multivariate statistics. In particular, the combination of LR approach with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) proved successful in discriminating chronic from non-chronic alcohol drinkers. These LR models were subsequently tested on an independent dataset of 43 individuals, which confirmed their high efficiency. These models proved to be less prone to bias than EtG and FAEEs independently considered. In conclusion, LR models may represent an efficient strategy to sustain the diagnosis of chronic alcohol consumption and provide a suitable gradation to support the judgment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Evaluation of dissolution profile similarity - Comparison between the f2, the multivariate statistical distance and the f2 bootstrapping methods.

PubMed

Paixão, Paulo; Gouveia, Luís F; Silva, Nuno; Morais, José A G

2017-03-01

A simulation study is presented, evaluating the performance of the f 2 , the model-independent multivariate statistical distance and the f 2 bootstrap methods in the ability to conclude similarity between two dissolution profiles. Different dissolution profiles, based on the Noyes-Whitney equation and ranging from theoretical f 2 values between 100 and 40, were simulated. Variability was introduced in the dissolution model parameters in an increasing order, ranging from a situation complying with the European guidelines requirements for the use of the f 2 metric to several situations where the f 2 metric could not be used anymore. Results have shown that the f 2 is an acceptable metric when used according to the regulatory requirements, but loses its applicability when variability increases. The multivariate statistical distance presented contradictory results in several of the simulation scenarios, which makes it an unreliable metric for dissolution profile comparisons. The bootstrap f 2 , although conservative in its conclusions is an alternative suitable method. Overall, as variability increases, all of the discussed methods reveal problems that can only be solved by increasing the number of dosage form units used in the comparison, which is usually not practical or feasible. Additionally, experimental corrective measures may be undertaken in order to reduce the overall variability, particularly when it is shown that it is mainly due to the dissolution assessment instead of being intrinsic to the dosage form. Copyright © 2016. Published by Elsevier B.V.

Circulating miRNAs in Pediatric Pulmonary Hypertension Show Promise as Biomarkers of Vascular Function

PubMed Central

Sucharov, Carmen C.; Truong, Uyen; Dunning, Jamie; Ivy, Dunbar; Miyamoto, Shelley; Shandas, Robin

2017-01-01

Background/Objectives The objective of this study was to evaluate the utility of circulating miRNAs as biomarkers of vascular function in pediatric pulmonary hypertension. Method Fourteen pediatric pulmonary arterial hypertension patients underwent simultaneous right heart catheterization (RHC) and blood biochemical analysis. Univariate and stepwise multivariate linear regression was used to identify and correlate measures of reactive and resistive afterload with circulating miRNA levels. Furthermore, circulating miRNA candidates that classified patients according to a 20% decrease in resistive afterload in response to oxygen (O2) or inhaled nitric oxide (iNO) were identified using receiver-operating curves. Results Thirty-two circulating miRNAs correlated with the pulmonary vascular resistance index (PVRi), pulmonary arterial distensibility, and PVRi decrease in response to O2 and/or iNO. Multivariate models, combining the predictive capability of multiple promising miRNA candidates, revealed a good correlation with resistive (r = 0.97, P2−tailed < 0.0001) and reactive (r = 0.86, P2−tailed < 0.005) afterloads. Bland-Altman plots showed that 95% of the differences between multivariate models and RHC would fall within 0.13 (mmHg−min/L)m2 and 0.0085/mmHg for resistive and reactive afterloads, respectively. Circulating miR-663 proved to be a good classifier for vascular responsiveness to acute O2 and iNO challenges. Conclusion This study suggests that circulating miRNAs may be biomarkers to phenotype vascular function in pediatric PAH. PMID:28819545

Coffee, caffeine, and risk of completed suicide: results from 3 prospective cohorts of American adults

PubMed Central

Lucas, Michel; O’Reilly, Eilis J.; Pan, An; Mirzaei, Fariba; Willett, Walter C.; Okereke, Olivia I.; Ascherio, Alberto

2014-01-01

Objective To evaluate the association between coffee and caffeine consumption and suicide risk in three large-scale cohorts of U.S. men and women. Methods We accessed data of 43,599 men enrolled in the Health Professionals Follow-up Study (HPFS, 1988–2008), 73,820 women in the Nurses’ Health Study (NHS, 1992–2008), and 91,005 women in the NHS II (1993–2007). Consumption of caffeine, coffee, and decaffeinated coffee, was assessed every four years by validated food-frequency questionnaires. Deaths from suicide were determined by physician review of death certificates. Multivariate adjusted relative risks (RRs) were estimated with Cox proportional hazard models. Cohort specific RRs were pooled using random-effect models. Results We documented 277 deaths from suicide. Compared to those consuming ≤1 cup/week of caffeinated coffee (≤8 oz/237 ml), the pooled multivariate RR (95% confidence interval [CI]) of suicide was 0.55 (0.38–0.78) for those consuming 2–3 cups/day and 0.47 (0.27–0.81) for those consuming ≥4 cups/day (P trend <0.001). The pooled multivariate RR (95% CI) for suicide was 0.75 (0.63–0.90) for each increment of 2 cups/day of caffeinated coffee and 0.77 (0.63–0.93) for each increment of 300 mg/day of caffeine. Conclusions These results from three large cohorts support an association between caffeine consumption and lower risk of suicide. PMID:23819683

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Inter-Hospital Transfer is Associated with Increased Mortality and Costs in Severe Sepsis and Septic Shock: An Instrumental Variables Approach

PubMed Central

Mohr, Nicholas M.; Harland, Karisa K.; Shane, Dan M.; Ahmed, Azeemuddin; Fuller, Brian M.; Torner, James C.

2016-01-01

Purpose The objective of this study was to evaluate the impact of regionalization on sepsis survival, to describe the role of inter-hospital transfer in rural sepsis care, and to measure the cost of inter-hospital transfer in a predominantly rural state. Materials and Methods Observational case-control study using statewide administrative claims data from 2005-2014 in a predominantly rural Midwestern state. Mortality and marginal costs were estimated with multivariable generalized estimating equations (GEE) models and with instrumental variables models. Results A total of 18,246 patients were included, of which 59% were transferred between hospitals. Transferred patients had higher mortality and longer hospital length-of-stay than non-transferred patients. Using a multivariable GEE model to adjust for potentially confounding factors, inter-hospital transfer was associated with increased mortality (aOR 1.7, 95%CI 1.5 – 1.9). Using an instrumental variables model, transfer was associated with a 9.2% increased risk of death. Transfer was associated with additional costs of $6,897 (95%CI $5,769-8,024). Even when limiting to only those patients who received care in the largest hospitals, transfer was still associated with $5,167 (95%CI $3,696-6,638) in additional cost. Conclusions The majority of rural sepsis patients are transferred, and these transferred patients have higher mortality and significantly increased cost of care. PMID:27546770

Disease Characteristics, Patterns of Care, and Survival in Very Elderly Patients with Diffuse Large B-Cell Lymphoma

PubMed Central

Williams, Jessica N.; Rai, Ashish; Lipscomb, Joseph; Koff, Jean L.; Nastoupil, Loretta J.; Flowers, Christopher R.

2015-01-01

Background Although rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is considered standard therapy for diffuse large B-cell lymphoma (DLBCL), patterns of use and the impact of R-CHOP on survival in patients >80 years are less clear. Methods We used the Surveillance, Epidemiology, and End Results (SEER)-Medicare database to characterize presentation, treatment, and survival patterns in DLBCL patients diagnosed from 2002–2009. Chi-squared tests compared characteristics and initial treatments of DLBCL patients >80 years and ≤80 years. Multivariable logistic regression models examined factors associated with treatment selection in patients >80 years; standard and propensity score-adjusted multivariable Cox proportional hazards models examined relationships between treatment regimen, treatment duration, and survival. Results Among 4,635 patients with DLBCL, 1,156 (25%) were >80 years. Patients >80 were less likely to receive R-CHOP and more likely to be observed or receive rituximab, cyclophosphamide, vincristine, and prednisone (R-CVP); both p<0.0001. Marital status, stage, disease site, performance status, radiation therapy, and growth factor support were associated with initial R-CHOP in patients >80. In propensity score-matched multivariable Cox proportional hazards models examining relationships between treatment regimen and survival, R-CHOP was the only regimen associated with improved OS (hazard ratio (HR) = 0.45, 95% confidence interval (CI) = 0.33–0.62) and LRS (HR=0.58, 95% CI 0.38–0.88). Conclusions Although DLBCL patients >80 years were less likely to receive R-CHOP, this regimen conferred the longest survival and should be considered for this population. Further studies are needed to characterize the impact of DLBCL treatment on quality of life in this age group. PMID:25675909

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Preventing Unintended Pregnancy Among Young Sexually Active Women: Recognizing the Role of Violence, Self-Esteem, and Depressive Symptoms on Use of Contraception

PubMed Central

Zhao, Huaqing; Corrado, Rachel; Mastrogiannnis, Dimitrios M.; Lepore, Stephen J.

2017-01-01

Abstract Objectives: Ineffective contraceptive use among young sexually active women is extremely prevalent and poses a significant risk for unintended pregnancy (UP). Ineffective contraception involves the use of the withdrawal method or the inconsistent use of other types of contraception (i.e., condoms and birth control pills). This investigation examined violence exposure and psychological factors related to ineffective contraceptive use among young sexually active women. Materials and Methods: Young, nonpregnant sexually active women (n = 315) were recruited from an urban family planning clinic in 2013 to participate in a longitudinal study. Tablet-based surveys measured childhood violence, community-level violence, intimate partner violence, depressive symptoms, and self-esteem. Follow-up surveys measured type and consistency of contraception used 9 months later. Multivariate logistic regression models assessed violence and psychological risk factors as main effects and moderators related to ineffective compared with effective use of contraception. Results: The multivariate logistic regression model showed that childhood sexual violence and low self-esteem were significantly related to ineffective use of contraception (adjusted odds ratio [aOR] = 2.69, confidence interval [95% CI]: 1.18–6.17, and aOR = 0.51, 95% CI: 0.28–0.93; respectively), although self-esteem did not moderate the relationship between childhood sexual violence and ineffective use of contraception (aOR = 0.38, 95% CI: 0.08–1.84). Depressive symptoms were not related to ineffective use of contraception in the multivariate model. Conclusions: Interventions to reduce UP should recognize the long-term effects of childhood sexual violence and address the role of low self-esteem on the ability of young sexually active women to effectively and consistently use contraception to prevent UP. PMID:28045570

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessing Multivariate Constraints to Evolution across Ten Long-Term Avian Studies

PubMed Central

Teplitsky, Celine; Tarka, Maja; Møller, Anders P.; Nakagawa, Shinichi; Balbontín, Javier; Burke, Terry A.; Doutrelant, Claire; Gregoire, Arnaud; Hansson, Bengt; Hasselquist, Dennis; Gustafsson, Lars; de Lope, Florentino; Marzal, Alfonso; Mills, James A.; Wheelwright, Nathaniel T.; Yarrall, John W.; Charmantier, Anne

2014-01-01

Background In a rapidly changing world, it is of fundamental importance to understand processes constraining or facilitating adaptation through microevolution. As different traits of an organism covary, genetic correlations are expected to affect evolutionary trajectories. However, only limited empirical data are available. Methodology/Principal Findings We investigate the extent to which multivariate constraints affect the rate of adaptation, focusing on four morphological traits often shown to harbour large amounts of genetic variance and considered to be subject to limited evolutionary constraints. Our data set includes unique long-term data for seven bird species and a total of 10 populations. We estimate population-specific matrices of genetic correlations and multivariate selection coefficients to predict evolutionary responses to selection. Using Bayesian methods that facilitate the propagation of errors in estimates, we compare (1) the rate of adaptation based on predicted response to selection when including genetic correlations with predictions from models where these genetic correlations were set to zero and (2) the multivariate evolvability in the direction of current selection to the average evolvability in random directions of the phenotypic space. We show that genetic correlations on average decrease the predicted rate of adaptation by 28%. Multivariate evolvability in the direction of current selection was systematically lower than average evolvability in random directions of space. These significant reductions in the rate of adaptation and reduced evolvability were due to a general nonalignment of selection and genetic variance, notably orthogonality of directional selection with the size axis along which most (60%) of the genetic variance is found. Conclusions These results suggest that genetic correlations can impose significant constraints on the evolution of avian morphology in wild populations. This could have important impacts on evolutionary dynamics and hence population persistence in the face of rapid environmental change. PMID:24608111

Regression models for analyzing costs and their determinants in health care: an introductory review.

PubMed

Gregori, Dario; Petrinco, Michele; Bo, Simona; Desideri, Alessandro; Merletti, Franco; Pagano, Eva

2011-06-01

This article aims to describe the various approaches in multivariable modelling of healthcare costs data and to synthesize the respective criticisms as proposed in the literature. We present regression methods suitable for the analysis of healthcare costs and then apply them to an experimental setting in cardiovascular treatment (COSTAMI study) and an observational setting in diabetes hospital care. We show how methods can produce different results depending on the degree of matching between the underlying assumptions of each method and the specific characteristics of the healthcare problem. The matching of healthcare cost models to the analytic objectives and characteristics of the data available to a study requires caution. The study results and interpretation can be heavily dependent on the choice of model with a real risk of spurious results and conclusions.

Funding source, conflict of interest and positive conclusions in neuro-oncology clinical trials.

PubMed

Moraes, Fabio Y; Mendez, Lucas C; Taunk, Neil K; Raman, Srinivas; Suh, John H; Souhami, Luis; Slotman, Ben; Weltman, Eduardo; Spratt, Daniel E; Berlin, Alejandro; Marta, Gustavo N

2018-02-01

We aimed to test any association between authors' conclusions and self-reported COI or funding sources in central nervous system (CNS) studies. A review was performed for CNS malignancy clinical trials published in the last 5 years. Two investigators independently classified study conclusions according to authors' endorsement of the experimental therapy. Statistical models were used to test for associations between positive conclusions and trials characteristics. From February 2010 to February 2015, 1256 articles were retrieved; 319 were considered eligible trials. Positive conclusions were reported in 56.8% of trials with industry-only, 55.6% with academia-only, 44.1% with academia and industry, 77.8% with none, and 76.4% with not described funding source (p = 0.011). Positive conclusions were reported in 60.4% of trials with unrelated COI, 60% with related COI, and 60% with no COI reported (p = 0.997). Factors that were significantly associated with the presence of positive conclusion included trials design (phase 1) [OR 11.64 (95 CI 4.66-29.09), p < 0.001], geographic location (outside North America or Europe) [OR 1.96 (95 CI 1.05-3.79), P = 0.025], primary outcomes (non-overall or progression free survival) [OR 3.74 (95 CI 2.27-6.18), p < 0.001], and failure to disclose funding source [OR 2.45 (95 CI 1.22-5.22), p = 0.011]. In a multivariable regression model, all these factors remained significantly associated with trial's positive conclusion. Funding source and self-reported COI did not appear to influence the CNS trials conclusion. Funding source information and COI disclosure were under-reported in 14.1 and 17.2% of the CNS trials. Continued efforts are needed to increase rates of both COI and funding source reporting.

Degenerative Changes of Spine in Helicopter Pilots

PubMed Central

Byeon, Joo Hyeon; Kim, Jung Won; Jeong, Ho Joong; Sim, Young Joo; Kim, Dong Kyu; Choi, Jong Kyoung; Im, Hyoung June

2013-01-01

Objective To determine the relationship between whole body vibration (WBV) induced helicopter flights and degenerative changes of the cervical and lumbar spine. Methods We examined 186 helicopter pilots who were exposed to WBV and 94 military clerical workers at a military hospital. Questionnaires and interviews were completed for 164 of the 186 pilots (response rate, 88.2%) and 88 of the 94 clerical workers (response rate, 93.6%). Radiographic examinations of the cervical and the lumbar spines were performed after obtaining informed consent in both groups. Degenerative changes of the cervical and lumbar spines were determined using four radiographs per subject, and diagnosed by two independent, blinded radiologists. Results There was no significant difference in general and work-related characteristics except for flight hours and frequency between helicopter pilots and clerical workers. Degenerative changes in the cervical spine were significantly more prevalent in the helicopter pilots compared with control group. In the cervical spine multivariate model, accumulated flight hours (per 100 hours) was associated with degenerative changes. And in the lumbar spine multivariate model, accumulated flight hours (per 100 hours) and age were associated with degenerative changes. Conclusion Accumulated flight hours were associated with degenerative changes of the cervical and lumbar spines in helicopter pilots. PMID:24236259

E-Cigarette Marketing Exposure is Associated with E-cigarette Use among U.S. Youth

PubMed Central

Mantey, Dale S.; Cooper, Maria R.; Clendennen, Stephanie; Pasch, Keryn; Perry, Cheryl L.

2016-01-01

Introduction E-cigarettes are currently the most commonly used tobacco product among U.S. youth. However, unlike conventional cigarettes, e-cigarettes are not subject to marketing restrictions. This study investigates the association between exposure to e-cigarette marketing and susceptibility and use of e-cigarettes in youth. Methods Data were obtained from the 2014 National Youth Tobacco Survey. Participants were 22,007 U.S. middle and high school students. Multivariate logistic regression models assessed the relationship between e-cigarette marketing (internet, print, retail, TV/movies) and current and ever use as well as susceptibility to use e-cigarettes among never e-cigarette users. Results Exposure to each type of e-cigarette marketing was significantly associated with increased likelihood of ever and current use of e-cigarettes among middle and high school students. Exposure was also associated with susceptibility to use of e-cigarettes among current non-users. In multivariate models, as the number of channels of e-cigarette marketing exposure increased, the likelihood of use and susceptibility also increased. Conclusions Findings highlight the significant associations between e-cigarette marketing and e-cigarette use among youth, and the need for longitudinal research on these relationships. PMID:27080732

Beyond Reading Alone: The Relationship Between Aural Literacy And Asthma Management

PubMed Central

Rosenfeld, Lindsay; Rudd, Rima; Emmons, Karen M.; Acevedo-García, Dolores; Martin, Laurie; Buka, Stephen

2010-01-01

Objectives To examine the relationship between literacy and asthma management with a focus on the oral exchange. Methods Study participants, all of whom reported asthma, were drawn from the New England Family Study (NEFS), an examination of links between education and health. NEFS data included reading, oral (speaking), and aural (listening) literacy measures. An additional survey was conducted with this group of study participants related to asthma issues, particularly asthma management. Data analysis focused on bivariate and multivariable logistic regression. Results In bivariate logistic regression models exploring aural literacy, there was a statistically significant association between those participants with lower aural literacy skills and less successful asthma management (OR:4.37, 95%CI:1.11, 17.32). In multivariable logistic regression analyses, controlling for gender, income, and race in separate models (one-at-a-time), there remained a statistically significant association between those participants with lower aural literacy skills and less successful asthma management. Conclusion Lower aural literacy skills seem to complicate asthma management capabilities. Practice Implications Greater attention to the oral exchange, in particular the listening skills highlighted by aural literacy, as well as other related literacy skills may help us develop strategies for clear communication related to asthma management. PMID:20399060

The Contribution of Missed Clinic Visits to Disparities in HIV Viral Load Outcomes

PubMed Central

Westfall, Andrew O.; Gardner, Lytt I.; Giordano, Thomas P.; Wilson, Tracey E.; Drainoni, Mari-Lynn; Keruly, Jeanne C.; Rodriguez, Allan E.; Malitz, Faye; Batey, D. Scott; Mugavero, Michael J.

2015-01-01

Objectives. We explored the contribution of missed primary HIV care visits (“no-show”) to observed disparities in virological failure (VF) among Black persons and persons with injection drug use (IDU) history. Methods. We used patient-level data from 6 academic clinics, before the Centers for Disease Control and Prevention and Health Resources and Services Administration Retention in Care intervention. We employed staged multivariable logistic regression and multivariable models stratified by no-show visit frequency to evaluate the association of sociodemographic factors with VF. We used multiple imputations to assign missing viral load values. Results. Among 10 053 patients (mean age = 46 years; 35% female; 64% Black; 15% with IDU history), 31% experienced VF. Although Black patients and patients with IDU history were significantly more likely to experience VF in initial analyses, race and IDU parameter estimates were attenuated after sequential addition of no-show frequency. In stratified models, race and IDU were not statistically significantly associated with VF at any no-show level. Conclusions. Because missed clinic visits contributed to observed differences in viral load outcomes among Black and IDU patients, achieving an improved understanding of differential visit attendance is imperative to reducing disparities in HIV. PMID:26270301

TH-E-BRF-03: A Multivariate Interaction Model for Assessment of Hippocampal Vascular Dose-Response and Early Prediction of Radiation-Induced Neurocognitive Dysfunction

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

Farjam, R; Pramanik, P; Srinivasan, A

Purpose: Vascular injury could be a cause of hippocampal dysfunction leading to late neurocognitive decline in patients receiving brain radiotherapy (RT). Hence, our aim was to develop a multivariate interaction model for characterization of hippocampal vascular dose-response and early prediction of radiation-induced late neurocognitive impairments. Methods: 27 patients (17 males and 10 females, age 31–80 years) were enrolled in an IRB-approved prospective longitudinal study. All patients were diagnosed with a low-grade glioma or benign tumor and treated by 3-D conformal or intensity-modulated RT with a median dose of 54 Gy (50.4–59.4 Gy in 1.8− Gy fractions). Six DCE-MRI scans weremore » performed from pre-RT to 18 months post-RT. DCE data were fitted to the modified Toft model to obtain the transfer constant of gadolinium influx from the intravascular space into the extravascular extracellular space, Ktrans, and the fraction of blood plasma volume, Vp. The hippocampus vascular property alterations after starting RT were characterized by changes in the hippocampal mean values of, μh(Ktrans)τ and μh(Vp)τ. The dose-response, Δμh(Ktrans/Vp)pre->τ, was modeled using a multivariate linear regression considering integrations of doses with age, sex, hippocampal laterality and presence of tumor/edema near a hippocampus. Finally, the early vascular dose-response in hippocampus was correlated with neurocognitive decline 6 and 18 months post-RT. Results: The μh(Ktrans) increased significantly from pre-RT to 1 month post-RT (p<0.0004). The multivariate model showed that the dose effect on Δμh(Ktrans)pre->1M post-RT was interacted with sex (p<0.0007) and age (p<0.00004), with the dose-response more pronounced in older females. Also, the vascular dose-response in the left hippocampus of females was significantly correlated with memory function decline at 6 (r = − 0.95, p<0.0006) and 18 (r = −0.88, p<0.02) months post-RT. Conclusion: The hippocampal vascular response to radiation could be sex and age dependent. The early hippocampal vascular dose-response could predict late neurocognitive dysfunction. (Support: NIH-RO1NS064973)« less

Application of the new Cross Recurrence Plots to multivariate data

NASA Astrophysics Data System (ADS)

Thiel, M.; Romano, C.; Kurths, J.

2003-04-01

We extend and then apply the method of the new Cross Recurrence Plots (XRPs) to multivariate data. After introducing the new method we carry out an analysis of spatiotemporal ecological data. We compute not only the Rényi entropies and cross entropies by XRP, that allow to draw conclusions about the coupling of the systems, but also find a prediction horizon for intermediate time scales.

Learning-based computing techniques in geoid modeling for precise height transformation

NASA Astrophysics Data System (ADS)

Erol, B.; Erol, S.

2013-03-01

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Disproportionation of rosin on an industrial Pd/C catalyst: reaction pathway and kinetic model discrimination.

PubMed

Souto, Juan Carlos; Yustos, Pedro; Ladero, Miguel; Garcia-Ochoa, Felix

2011-02-01

In this work, a phenomenological study of the isomerisation and disproportionation of rosin acids using an industrial 5% Pd on charcoal catalyst from 200 to 240°C is carried out. Medium composition is determined by elemental microanalysis, GC-MS and GC-FID. Dehydrogenated and hydrogenated acid species molar amounts in the final product show that dehydrogenation is the main reaction. Moreover, both hydrogen and non-hydrogen concentration considering kinetic models are fitted to experimental data using a multivariable non-linear technique. Statistical discrimination among the proposed kinetic models lead to the conclusion hydrogen considering models fit much better to experimental results. The final kinetic model involves first-order isomerisation reactions of neoabietic and palustric acids to abietic acid, first-order dehydrogenation and hydrogenation of this latter acid, and hydrogenation of pimaric acids. Hydrogenation reactions are partial first-order regarding the acid and hydrogen. Copyright © 2010 Elsevier Ltd. All rights reserved.

Magnesium intake, plasma C-peptide, and colorectal cancer incidence in US women: a 28-year follow-up study

PubMed Central

Zhang, X; Giovannucci, E L; Wu, K; Smith-Warner, S A; Fuchs, C S; Pollak, M; Willett, W C; Ma, J

2012-01-01

Background: Laboratory studies suggest a possible role of magnesium intake in colorectal carcinogenesis but epidemiological evidence is inconclusive. Method: We tested magnesium–colorectal cancer hypothesis in the Nurses' Health Study, in which 85 924 women free of cancer in 1980 were followed until June 2008. Cox proportional hazards regression models were used to estimate multivariable relative risks (MV RRs, 95% confidence intervals). Results: In the age-adjusted model, magnesium intake was significantly inversely associated with colorectal cancer risk; the RRs from lowest to highest decile of total magnesium intake were 1.0 (ref), 0.93, 0.81, 0.72, 0.74, 0.77, 0.72, 0.75, 0.80, and 0.67 (Ptrend<0.001). However, in the MV model adjusted for known dietary and non-dietary risk factors for colorectal cancer, the association was significantly attenuated; the MV RRs were 1.0 (ref), 0.96, 0.85, 0.78, 0.82, 0.86, 0.84, 0.91, 1.02, and 0.93 (Ptrend=0.77). Similarly, magnesium intakes were significantly inversely associated with concentrations of plasma C-peptide in age-adjusted model (Ptrend=0.002) but not in multivariate-adjusted model (Ptrend=0.61). Results did not differ by subsite or modified by calcium intakes or body mass index. Conclusion: These prospective results do not support an independent association of magnesium intake with either colorectal cancer risk or plasma C-peptide levels in women. PMID:22415230

Use of direct gradient analysis to uncover biological hypotheses in 16s survey data and beyond.

PubMed

Erb-Downward, John R; Sadighi Akha, Amir A; Wang, Juan; Shen, Ning; He, Bei; Martinez, Fernando J; Gyetko, Margaret R; Curtis, Jeffrey L; Huffnagle, Gary B

2012-01-01

This study investigated the use of direct gradient analysis of bacterial 16S pyrosequencing surveys to identify relevant bacterial community signals in the midst of a "noisy" background, and to facilitate hypothesis-testing both within and beyond the realm of ecological surveys. The results, utilizing 3 different real world data sets, demonstrate the utility of adding direct gradient analysis to any analysis that draws conclusions from indirect methods such as Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA). Direct gradient analysis produces testable models, and can identify significant patterns in the midst of noisy data. Additionally, we demonstrate that direct gradient analysis can be used with other kinds of multivariate data sets, such as flow cytometric data, to identify differentially expressed populations. The results of this study demonstrate the utility of direct gradient analysis in microbial ecology and in other areas of research where large multivariate data sets are involved.

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.

Individual, Psychosocial, and Social Correlates of Unprotected Anal Intercourse in a New Generation of Young Men Who Have Sex With Men in New York City

PubMed Central

Kapadia, Farzana; Siconolfi, Daniel E.; Moeller, Robert W.; Figueroa, Rafael Perez; Barton, Staci C.; Blachman-Forshay, Jaclyn

2013-01-01

Objectives. We examined associations of individual, psychosocial, and social factors with unprotected anal intercourse (UAI) among young men who have sex with men in New York City. Methods. Using baseline assessment data from 592 young men who have sex with men participating in an ongoing prospective cohort study, we conducted multivariable logistic regression analyses to examine the associations between covariates and likelihood of recently engaging in UAI with same-sex partners. Results. Nineteen percent reported recent UAI with a same-sex partner. In multivariable models, being in a current relationship with another man (adjusted odds ratio [AOR] = 4.87), an arrest history (AOR = 2.01), greater residential instability (AOR = 1.75), and unstable housing or homelessness (AOR = 3.10) was associated with recent UAI. Although high levels of gay community affinity and low internalized homophobia were associated with engaging in UAI in bivariate analyses, these associations did not persist in multivariable analyses. Conclusions. Associations of psychosocial and socially produced conditions with UAI among a new generation of young men who have sex with men warrant that HIV prevention programs and policies address structural factors that predispose sexual risk behaviors. PMID:23488487

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

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…

Direct to consumer and physician promotion of tegaserod correlated with physician visits, diagnoses, and prescriptions

PubMed Central

Dorn, Spencer D.; Farley, Joel F.; Hansen, Richard A.; D. Shah, Nilay; Sandler, Robert S.

2009-01-01

Background & Aims Direct to consumer advertisement (DTCA) and physician promotion of drugs can influence patient and physician behaviors. We sought to determine the relationship between promotion of tegaserod and the number of office visits for abdominal pain, constipation, and bloating; diagnoses of irritable bowel syndrome (IBS); and tegaserod prescriptions. Methods We used an Integrated Promotional Services database to estimate tegaserod DTCA and promotion expenditures, The National Ambulatory/Hospital Medical Care Surveys (1997–2005) to estimate the number of ambulatory care visits for abdominal pain, constipation, and bloating and diagnoses of IBS, and IMS Health's National Prescription Audit Plus to estimate the number of prescriptions. We constructed segmented and multivariate regression models to analyze the data. Results In the 3 months immediately following the start of tegaserod DTCA, there was a significant increase in physician visits (by 1 million; 95% CI 0.5–1.6 million) and IBS diagnoses (by 397,025; 95% CI 3,909–790,141). Subsequently, the trend of visits and IBS diagnoses reduced. In multivariate analyses that examined the overall relationship of promotion with visits, diagnoses, and prescriptions, only the relationship between physician promotion and tegaserod prescribing was significant; every $1 million spent on physician promotion resulted in an additional 4,108 prescriptions (95% CI: 2,526–5,691). Conclusions The initial DTCA of tegaserod was associated with a significant, immediate increase in physician visits and IBS diagnoses. This trend reversed and in multivariate models, neither DTCA nor physician promotion correlated with visits or diagnoses. Physician promotion (though not DTCA) correlated with tegaserod prescription volume. PMID:19445943

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

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

VanderWalde, Noam A.; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Meyer, Anne Marie

Purpose: The purpose of this study was to compare chemoradiation therapy (CRT) with radiation therapy (RT) only in an older patient population with head and neck squamous cell carcinoma (HNSCC). Methods and Materials: Using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database (1992-2007), we identified a retrospective cohort of nonmetastatic HNSCC patients and divided them into treatment groups. Comparisons were made between CRT and RT cohorts. Propensity scores for CRT were estimated from covariates associated with receipt of treatment using multivariable logistic regression. Standardized mortality ratio weights (SMRW) were created from the propensity scores and used to balance groupsmore » on measured confounders. Multivariable and SMR-weighted Cox proportional hazard models were used to estimate the hazard ratio (HR) of death for receipt of CRT versus RT among the whole group and for separate patient and tumor categories. Results: The final cohort of 10,599 patients was 68% male and 89% white. Median age was 74 years. Seventy-four percent were treated with RT, 26% were treated with CRT. Median follow-up points for CRT and RT survivors were 4.6 and 6.3 years, respectively. On multivariable analysis, HR for death with CRT was 1.13 (95% confidence interval [CI]: 1.07-1.20; P<.01). Using the SMRW model, the HR for death with CRT was 1.08 (95% CI: 1.02-1.15; P=.01). Conclusions: Although the addition of chemotherapy to radiation has proven efficacious in many randomized controlled trials, it may be less effective in an older patient population treated outside of a controlled trial setting.« less

Taste phenotype associates with cardiovascular disease risk factors via diet quality in multivariate modeling.

PubMed

Sharafi, Mastaneh; Rawal, Shristi; Fernandez, Maria Luz; Huedo-Medina, Tania B; Duffy, Valerie B

2018-05-08

Sensations from foods and beverages drive dietary choices, which in turn, affect risk of diet-related diseases. Perception of these sensation varies with environmental and genetic influences. This observational study aimed to examine associations between chemosensory phenotype, diet and cardiovascular disease (CVD) risk. Reportedly healthy women (n = 110, average age 45 ± 9 years) participated in laboratory-based measures of chemosensory phenotype (taste and smell function, propylthiouracil (PROP) bitterness) and CVD risk factors (waist circumference, blood pressure, serum lipids). Diet variables included preference and intake of sweet/high-fat foods, dietary restraint, and diet quality based on reported preference (Healthy Eating Preference Index-HEPI) and intake (Healthy Eating Index-HEI). We found that females who reported high preference yet low consumption of sweet/high-fat foods had the highest dietary restraint and depressed quinine taste function. PROP nontasters were more likely to report lower diet quality; PROP supertasters more likely to consume but not like a healthy diet. Multivariate structural models were fitted to identify predictors of CVD risk factors. Reliable latent taste (quinine taste function, PROP tasting) and smell (odor intensity) variables were identified, with taste explaining more variance in the CVD risk factors. Lower bitter taste perception was associated with elevated risk. In multivariate models, the HEPI completely mediated the taste-adiposity and taste-HDL associations and partially mediated the taste-triglyceride or taste-systolic blood pressure associations. The taste-LDL pathway was significant and direct. The HEI could not replace HEPI in adequate models. However, using a latent diet quality variable with HEPI and HEI, increased the strength of association between diet quality and adiposity or CVD risk factors. In conclusion, bitter taste phenotype was associated with CVD risk factors via diet quality, particularly when assessed by level of food liking/disliking. Copyright © 2018 Elsevier Inc. All rights reserved.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.

PubMed

Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di

2018-01-01

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples. Copyright © 2016. Published by Elsevier B.V.

Prevalence and determinants of cardiovascular disease risk factors among the residents of urban community housing projects in Malaysia

PubMed Central

2014-01-01

Objectives The objectives are to assess the prevalence and determinants of cardiovascular disease (CVD) risk factors among the residents of Community Housing Projects in metropolitan Kuala Lumpur, Malaysia. Method By using simple random sampling, we selected and surveyed 833 households which comprised of 3,722 individuals. Out of the 2,360 adults, 50.5% participated in blood sampling and anthropometric measurement sessions. Uni and bivariate data analysis and multivariate binary logistic regression were applied to identify demographic and socioeconomic determinants of the existence of having at least one CVD risk factor. Results As a Result, while obesity (54.8%), hypercholesterolemia (51.5%), and hypertension (39.3%) were the most common CVD risk factors among the low-income respondents, smoking (16.3%), diabetes mellitus (7.8%) and alcohol consumption (1.4%) were the least prevalent. Finally, the results from the multivariate binary logistic model illustrated that compared to the Malays, the Indians were 41% less likely to have at least one of the CVD risk factors (OR = 0.59; 95% CI: 0.37 - 0.93). Conclusion In Conclusion, the low-income individuals were at higher risk of developing CVDs. Prospective policies addressing preventive actions and increased awareness focusing on low-income communities are highly recommended and to consider age, gender, ethnic backgrounds, and occupation classes. PMID:25436515

An Applet to Estimate the IOP-Induced Stress and Strain within the Optic Nerve Head

PubMed Central

2011-01-01

Purpose. The ability to predict the biomechanical response of the optic nerve head (ONH) to intraocular pressure (IOP) elevation holds great promise, yet remains elusive. The objective of this work was to introduce an approach to model ONH biomechanics that combines the ease of use and speed of analytical models with the flexibility and power of numerical models. Methods. Models representing a variety of ONHs were produced, and finite element (FE) techniques used to predict the stresses (forces) and strains (relative deformations) induced on each of the models by IOP elevations (up to 10 mm Hg). Multivariate regression was used to parameterize each biomechanical response as an analytical function. These functions were encoded into a Flash-based applet. Applet utility was demonstrated by investigating hypotheses concerning ONH biomechanics posited in the literature. Results. All responses were parameterized well by polynomials (R2 values between 0.985 and 0.999), demonstrating the effectiveness of our fitting approach. Previously published univariate results were reproduced with the applet in seconds. A few minutes allowed for multivariate analysis, with which it was predicted that often, but not always, larger eyes experience higher levels of stress and strain than smaller ones, even at the same IOP. Conclusions. An applet has been presented with which it is simple to make rapid estimates of IOP-related ONH biomechanics. The applet represents a step toward bringing the power of FE modeling beyond the specialized laboratory and can thus help develop more refined biomechanics-based hypotheses. The applet is available for use at www.ocularbiomechanics.com. PMID:21527378

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Effect of simultaneous model observation and self-modeling of volleyball skill acquisition.

PubMed

Barzouka, Karolina; Bergeles, Nikolaos; Hatziharistos, Dimitris

2007-02-01

This study examined the effect of feedback with simultaneous skilled model observation and self-modeling on volleyball skill acquisition. 53 pupils 12 to 15 years old formed two experimental groups and one control group who followed an intervention program with 12 practice sessions for acquisition and retention of how to receive a ball. Groups received different types of feedback before and in the middle of each practice session. Reception performance outcome (score) and technique in every group were assessed before and at the end of the intervention program and during the retention phase. A 3 (Group) x 3 (Measurement Period) multivariate analysis of variance with repeated measures was applied to investigate differences. Results showed equivalent improvement in all three groups at the end of the intervention program. In conclusion, types of augmented feedback from the physical education teacher are effective in acquisition and retention of the skill for reception in volleyball.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

EPA Science Inventory

Abstract

Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

Does information available at admission for delivery improve prediction of vaginal birth after cesarean?

PubMed Central

Grobman, William A.; Lai, Yinglei; Landon, Mark B.; Spong, Catherine Y.; Leveno, Kenneth J.; Rouse, Dwight J.; Varner, Michael W.; Moawad, Atef H.; Simhan, Hyagriv N.; Harper, Margaret; Wapner, Ronald J.; Sorokin, Yoram; Miodovnik, Menachem; Carpenter, Marshall; O'sullivan, Mary J.; Sibai, Baha M.; Langer, Oded; Thorp, John M.; Ramin, Susan M.; Mercer, Brian M.

2010-01-01

Objective To construct a predictive model for vaginal birth after cesarean (VBAC) that combines factors that can be ascertained only as the pregnancy progresses with those known at initiation of prenatal care. Study design Using multivariable modeling, we constructed a predictive model for VBAC that included patient factors known at the initial prenatal visit as well as those that only became evident as the pregancy progressed to the admission for delivery. Results 9616 women were analyzed. The regression equation for VBAC success included multiple factors that could not be known at the first prenatal visit. The area under the curve for this model was significantly greater (P < .001) than that of a model that included only factors available at the first prenatal visit. Conclusion A prediction model for VBAC success that incorporates factors that can be ascertained only as the pregnancy progresses adds to the predictive accuracy of a model that uses only factors available at a first prenatal visit. PMID:19813165

A Novel MiRNA-Based Predictive Model for Biochemical Failure Following Post-Prostatectomy Salvage Radiation Therapy

PubMed Central

Stegmaier, Petra; Drendel, Vanessa; Mo, Xiaokui; Ling, Stella; Fabian, Denise; Manring, Isabel; Jilg, Cordula A.; Schultze-Seemann, Wolfgang; McNulty, Maureen; Zynger, Debra L.; Martin, Douglas; White, Julia; Werner, Martin; Grosu, Anca L.; Chakravarti, Arnab

2015-01-01

Purpose To develop a microRNA (miRNA)-based predictive model for prostate cancer patients of 1) time to biochemical recurrence after radical prostatectomy and 2) biochemical recurrence after salvage radiation therapy following documented biochemical disease progression post-radical prostatectomy. Methods Forty three patients who had undergone salvage radiation therapy following biochemical failure after radical prostatectomy with greater than 4 years of follow-up data were identified. Formalin-fixed, paraffin-embedded tissue blocks were collected for all patients and total RNA was isolated from 1mm cores enriched for tumor (>70%). Eight hundred miRNAs were analyzed simultaneously using the nCounter human miRNA v2 assay (NanoString Technologies; Seattle, WA). Univariate and multivariate Cox proportion hazards regression models as well as receiver operating characteristics were used to identify statistically significant miRNAs that were predictive of biochemical recurrence. Results Eighty eight miRNAs were identified to be significantly (p<0.05) associated with biochemical failure post-prostatectomy by multivariate analysis and clustered into two groups that correlated with early (≤ 36 months) versus late recurrence (>36 months). Nine miRNAs were identified to be significantly (p<0.05) associated by multivariate analysis with biochemical failure after salvage radiation therapy. A new predictive model for biochemical recurrence after salvage radiation therapy was developed; this model consisted of miR-4516 and miR-601 together with, Gleason score, and lymph node status. The area under the ROC curve (AUC) was improved to 0.83 compared to that of 0.66 for Gleason score and lymph node status alone. Conclusion miRNA signatures can distinguish patients who fail soon after radical prostatectomy versus late failures, giving insight into which patients may need adjuvant therapy. Notably, two novel miRNAs (miR-4516 and miR-601) were identified that significantly improve prediction of biochemical failure post-salvage radiation therapy compared to clinico-histopathological factors, supporting the use of miRNAs within clinically used predictive models. Both findings warrant further validation studies. PMID:25760964

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

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

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

Mental health service use among high school students exposed to interpersonal violence

PubMed Central

Johnson, Renee M.; Dunn, Erin C.; Lindsey, Michael; Xuan, Ziming; Zaslavsky, Alan M.

2013-01-01

BACKGROUND Violence-exposed youth rarely receive mental health services, even though exposure increases risk for academic and psychosocial problems. This study examines the association between violence exposure and mental health service contact. The four forms of violence exposure were peer, family, sexual, and witnessing. METHODS Data are from 1,534 Boston public high school students who participated in a 2008 self-report survey of violence exposure and its correlates. Multivariate logistic regressions estimated associations between each form of violence with service contact, then examined whether associations persisted when controlling for suicidality and self-injurious behaviors. RESULTS In unadjusted models, violence-exposed students more often reported service contact than their peers. However, in multivariate models, only exposure to family (OR=1.69, CI=1.23–2.31) and sexual violence (OR=2.34, CI=1.29–4.20) were associated with service contact. Associations attenuated when controlling for suicidality and self-injurious behaviors, indicating they were largely explained by self-harm. Sexual violence alone remained associated with mental health service contact in fully adjusted models, but only for girls (OR=3.32, CI=1.30–8.45), suggesting gender-specific pathways. CONCLUSIONS Associations between adolescent violence exposure and mental health service contact vary by form of exposure. Outreach to a broader set of exposed youth may reduce the impact of violence and its consequences for vulnerable students. PMID:25099429

The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability.

PubMed

Marković, Snežana; Kerč, Janez; Horvat, Matej

2017-03-01

We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Risk factors for baclofen pump infection in children: a multivariate analysis.

PubMed

Spader, Heather S; Bollo, Robert J; Bowers, Christian A; Riva-Cambrin, Jay

2016-06-01

OBJECTIVE Intrathecal baclofen infusion systems to manage severe spasticity and dystonia are associated with higher infection rates in children than in adults. Factors unique to this population, such as poor nutrition and physical limitations for pump placement, have been hypothesized as the reasons for this disparity. The authors assessed potential risk factors for infection in a multivariate analysis. METHODS Patients who underwent implantation of a programmable pump and intrathecal catheter for baclofen infusion at a single center between January 1, 2000, and March 1, 2012, were identified in this retrospective cohort study. The primary end point was infection. Potential risk factors investigated included preoperative (i.e., demographics, body mass index [BMI], gastrostomy tube, tracheostomy, previous spinal fusion), intraoperative (i.e., surgeon, antibiotics, pump size, catheter location), and postoperative (i.e., wound dehiscence, CSF leak, and number of revisions) factors. Univariate analysis was performed, and a multivariate logistic regression model was created to identify independent risk factors for infection. RESULTS A total of 254 patients were evaluated. The overall infection rate was 9.8%. Univariate analysis identified young age, shorter height, lower weight, dehiscence, CSF leak, and number of revisions within 6 months of pump placement as significantly associated with infection. Multivariate analysis identified young age, dehiscence, and number of revisions as independent risk factors for infection. CONCLUSIONS Young age, wound dehiscence, and number of revisions were independent risk factors for infection in this pediatric cohort. A low BMI and the presence of either a gastrostomy or tracheostomy were not associated with infection and may not be contraindications for this procedure.

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

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

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A panel of kallikrein markers can reduce unnecessary biopsy for prostate cancer: data from the European Randomized Study of Prostate Cancer Screening in Göteborg, Sweden

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Aus, Gunnar; Pihl, Carl-Gustav; Becker, Charlotte; Pettersson, Kim; Scardino, Peter T; Hugosson, Jonas; Lilja, Hans

2008-01-01

Background Prostate-specific antigen (PSA) is widely used to detect prostate cancer. The low positive predictive value of elevated PSA results in large numbers of unnecessary prostate biopsies. We set out to determine whether a multivariable model including four kallikrein forms (total, free, and intact PSA, and human kallikrein 2 (hK2)) could predict prostate biopsy outcome in previously unscreened men with elevated total PSA. Methods The study cohort comprised 740 men in Göteborg, Sweden, undergoing biopsy during the first round of the European Randomized study of Screening for Prostate Cancer. We calculated the area-under-the-curve (AUC) for predicting prostate cancer at biopsy. AUCs for a model including age and PSA (the 'laboratory' model) and age, PSA and digital rectal exam (the 'clinical' model) were compared with those for models that also included additional kallikreins. Results Addition of free and intact PSA and hK2 improved AUC from 0.68 to 0.83 and from 0.72 to 0.84, for the laboratory and clinical models respectively. Using a 20% risk of prostate cancer as the threshold for biopsy would have reduced the number of biopsies by 424 (57%) and missed only 31 out of 152 low-grade and 3 out of 40 high-grade cancers. Conclusion Multiple kallikrein forms measured in blood can predict the result of biopsy in previously unscreened men with elevated PSA. A multivariable model can determine which men should be advised to undergo biopsy and which might be advised to continue screening, but defer biopsy until there was stronger evidence of malignancy. PMID:18611265

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

Maternal Language and Adverse Birth Outcomes in a Statewide Analysis

PubMed Central

Sentell, Tetine; Chang, Ann; Jun Ahn, Hyeong; Miyamura, Jill

2016-01-01

Background Limited English proficiency is associated with disparities across diverse health outcomes. However, evidence regarding adverse birth outcomes across languages is limited, particularly among US Asian and Pacific Islander populations. The study goal was to consider the relationship of maternal language to birth outcomes using statewide hospitalization data. Methods Detailed discharge data from Hawai‘i childbirth hospitalizations from 2012 (n=11,419) were compared by maternal language (English language or not) for adverse outcomes using descriptive and multivariable log-binomial regression models, controlling for race/ethnicity, age group, and payer. Results Ten percent of mothers spoke a language other than English; 93% of these spoke an Asian or Pacific Islander language. In multivariable models, compared to English speakers non-English speakers had significantly higher risk (adjusted relative risk [ARR]: 2.02; 95% Confidence Interval [CI]: 1.34–3.04) of obstetric trauma in vaginal deliveries without instrumentation. Some significant variation was seen by language for other birth outcomes, including an increased rate of primary Caesarean sections and vaginal births after Caesarean among non-English speakers. Conclusions Non-English speakers had approximately two times higher risk of having an obstetric trauma during a vaginal birth when other factors, including race/ethnicity, were controlled. Non-English speakers also had higher rates of potentially high-risk deliveries. PMID:26361937

Impact of Trichiasis Surgery on Physical Functioning in Ethiopian Patients: STAR Trial

PubMed Central

Wolle, Meraf A.; Cassard, Sandra D.; Gower, Emily W.; Munoz, Beatriz E.; Wang, Jiangxia; Alemayehu, Wondu; West, Sheila K.

2010-01-01

Purpose To evaluate the physical functioning of Ethiopian trichiasis surgery patients before and six months after surgery. Design Nested Cohort Study Methods This study was nested within the Surgery for Trichiasis, Antibiotics to Prevent Recurrence (STAR) clinical trial conducted in Ethiopia. Demographic information, ocular examinations, and physical functioning assessments were collected before and 6 months after surgery. A single score for patients’ physical functioning was constructed using Rasch analysis. A multivariate linear regression model was used to determine if change in physical functioning was associated with change in visual acuity. Results Of the 438 participants, 411 (93.8%) had both baseline and follow-up questionnaires. Physical functioning scores at baseline ranged from −6.32 (great difficulty) to +6.01 (no difficulty). The percent of participants reporting no difficulty in physical functioning increased by 32.6%; the proportion of participants in the mild/no visual impairment category increased by 8.6%. A multivariate linear regression model showed that for every line of vision gained, physical functioning improves significantly (0.09 units; 95% CI: 0.02–0.16). Conclusions Surgery to correct trichiasis appears to improve patients’ physical functioning as measured at 6 months. More effort in promoting trichiasis surgery is essential, not only to prevent corneal blindness, but also to enable improved functioning in daily life. PMID:21333268

Correlates of Suicidality: Investigation of a Representative Sample of Manitoba First Nations Adolescents

PubMed Central

Mota, Natalie; Elias, Brenda; Tefft, Bruce; Medved, Maria; Munro, Garry

2012-01-01

Objectives. We examined individual, friend or family, and community or tribe correlates of suicidality in a representative on-reserve sample of First Nations adolescents. Methods. Data came from the 2002–2003 Manitoba First Nations Regional Longitudinal Health Survey of Youth. Interviews were conducted with adolescents aged 12 to 17 years (n = 1125) from 23 First Nations communities in Manitoba. We used bivariate logistic regression analyses to examine the relationships between a range of factors and lifetime suicidality. We conducted sex-by-correlate interactions for each significant correlate at the bivariate level. A multivariate logistic regression analysis identified those correlates most strongly related to suicidality. Results. We found several variables to be associated with an increased likelihood of suicidality in the multivariate model, including being female, depressed mood, abuse or fear of abuse, a hospital stay, and substance use (adjusted odds ratio range = 2.43–11.73). Perceived community caring was protective against suicidality (adjusted odds ratio = 0.93; 95% confidence interval = 0.88, 0.97) in the same model. Conclusions. Results of this study may be important in informing First Nations and government policy related to the implementation of suicide prevention strategies in First Nations communities. PMID:22676500

Biological and socio-cultural factors during the school years predicting women’s lifetime educational attainment

PubMed Central

Hendrick, C. Emily; Cohen, Alison K.; Deardorff, Julianna

2015-01-01

BACKGROUND Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In the current study, we examine the roles of socio-cultural factors in youth and an understudied biological life event, pubertal timing, in predicting women’s lifetime educational attainment. METHODS Using data from the National Longitudinal Survey of Youth 1997 cohort (N = 3889), we conducted sequential multivariate linear regression analyses to investigate the influences of macro-level and family-level socio-cultural contextual factors in youth (region of country, urbanicity, race/ethnicity, year of birth, household composition, mother’s education, mother’s age at first birth) and early menarche, a marker of early pubertal development, on women’s educational attainment after age 24. RESULTS Pubertal timing and all socio-cultural factors in youth, other than year of birth, predicted women’s lifetime educational attainment in bivariate models. Family factors had the strongest associations. When family factors were added to multivariate models, geographic region in youth and pubertal timing were no longer significant. CONCLUSION Our findings provide additional evidence that family factors should be considered when developing comprehensive and inclusive interventions in childhood and adolescence to promote lifetime educational attainment among girls. PMID:26830508

Canine dilated cardiomyopathy: a retrospective study of prognostic findings in 367 clinical cases.

PubMed

Martin, M W S; Stafford Johnson, M J; Strehlau, G; King, J N

2010-08-01

To review the association between clinical signs and diagnostic findings and the survival time of dogs with dilated cardiomyopathy (DCM), and any influence of treatment prescribed. A retrospective observational study of 367 dogs with DCM. Survival times until death or euthanasia for cardiac reasons were analysed using the Kaplan-Meier method plus univariate and multivariate Cox proportional hazards models. Two-tailed P values less than 0.05 were considered statistically significant. In the multivariate model, left ventricular diameter (LVDs)-index (P=0.0067), presence of pulmonary oedema on radiography (P=0.043), presence of ventricular premature complexes (VPCs) (P=0.0012), higher plasma creatinine (P=0.0002), lower plasma protein (P=0.029) and great Dane breed (P=0.0003) were negatively associated with survival. Most dogs were treated with angiotensin-converting enzyme inhibitors (93%) or furosemide (86%), and many received digoxin (50%) and/or pimobendan (30%). Thirteen dogs were lost to follow-up. No conclusions could be made in this study on the association between use of drugs and survival. The LVDs-index was the single best variable for assessing the prognosis in this group of dogs with DCM. Other variables that were negatively associated with survival were presence of pulmonary oedema on radiography, presence of VPCs, higher plasma creatinine, lower plasma protein and great Dane breed.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

PubMed

Prunier, J G; Colyn, M; Legendre, X; Nimon, K F; Flamand, M C

2015-01-01

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses. © 2014 John Wiley & Sons Ltd.

Antipyretic Therapy in Critically Ill Patients with Sepsis: An Interaction with Body Temperature

PubMed Central

Zhang, Zhongheng; Chen, Lin; Ni, Hongying

2015-01-01

Background and Objective The effect of antipyretic therapy on mortality in patients with sepsis remains undetermined. The present study aimed to investigate the role of antipyretic therapy in ICU patients with sepsis by using a large clinical database. Methods The multiparameter intelligent monitoring in intensive care II (MIMIC- II) database was employed for the study. Adult patients with sepsis were included for analysis. Antipyretic therapy included antipyretic medication and external cooling. Multivariable model with interaction terms were employed to explore the association of antipyretic therapy and mortality risk. Main Results A total of 15,268 patients fulfilled inclusion criteria and were included in the study. In multivariable model by treating temperature as a continuous variable, there was significant interaction between antipyretic therapy and the maximum temperature (Tmax). While antipyretic therapy had no significant effect on mortality in low temperature quintiles, antipyretic therapy was associated with increased risk of death in the quintile with body temperature >39°C (OR: 1.29, 95% CI: 1.04–1.61). Conclusion Our study shows that there is no beneficial effect on reducing mortality risk with the use of antipyretic therapy in ICU patients with sepsis. External cooling may even be harmful in patients with sepsis. PMID:25822614

Epidemiological characteristics of measles from 2000 to 2014: Results of a measles catch-up vaccination campaign in Xianyang, China.

PubMed

Zhang, Rong-Qiang; Li, Hong-Bing; Li, Feng-Ying; Han, Li-Xin; Xiong, Yong-Min

This study was a cross-sectional case-control study aimed at (1) identifying risk factors contributing to the measles epidemic and (2) evaluating the impacts of measles-containing vaccines (MCVs), with the goal of providing evidence-based recommendations for measles elimination strategies in China. Data on measles cases from 2000 to 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang. The effectiveness of MCVs was evaluated in 357 patients with a vaccination history and 503 healthy randomly selected controls. Patient data were subjected to multivariable logistic regression modeling. From 2005 to 2014, the average incidence of measles in Xianyang was 5.42 cases per 100,000 people. The second MCV dose was highly protective in 8-month-old infants. MCVs in general have been highly protective in 8-month-old infants. Multivariable logistic regression modeling indicated that age (≥2 years vs. <2years), MCV dose 2 vaccination, and MV vaccination were each independently associated with measles case status. In conclusions: A MCV should be administered on time to all age-eligible children, reproductive-age women, and migrant populations, to maximize herd immunity to measles. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Neuropsychological tests for predicting cognitive decline in older adults

PubMed Central

Baerresen, Kimberly M; Miller, Karen J; Hanson, Eric R; Miller, Justin S; Dye, Richelin V; Hartman, Richard E; Vermeersch, David; Small, Gary W

2015-01-01

Summary Aim To determine neuropsychological tests likely to predict cognitive decline. Methods A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing. Results Rey–Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer’s disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD. Conclusion Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline. PMID:26107318

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

Risk factors for maternal night blindness in rural South India

PubMed Central

Katz, Joanne; Tielsch, James M.; Thulasiraj, R. D.; Coles, Christian; Sheeladevi, S.; Yanik, Elizabeth L.; Rahmathullah, Lakshmi

2009-01-01

Purpose This study aimed to identify risk factors associated with maternal night blindness in rural South India. Methods At delivery, women enrolled in a population-based trial of newborn vitamin A supplementation were asked whether they were night blind at any time during the pregnancy. Multivariate logistic regression was used to identify socioeconomic, demographic, and pregnancy related factors associated with maternal night blindness. Results Women reported night blindness in 687 (5.2%) of 13,171 pregnancies. In a multivariate model, having a concrete roof (Odds Ratio (OR): 0.60, 95% Confidence Interval (CI): 0.47, 0.78), religion other than Hindu (OR: 0.46, 95% CI: 0.27, 0.76), maternal literacy (OR: 0.58, 95% CI: 0.49, 0.69), and maternal age from 25 to 29 years (OR: 0.68, 95%CI: 0.50, 0.93) were associated with a lower risk of night blindness in pregnancy. The odds of night blindness were higher for those leasing rather than owning land (OR: 1.78, 95%CI: 1.08, 2.93), parity 6 or more compared to 0 (OR: 2.11, 95% CI: 1.09, 4.08), and with twin pregnancies (OR: 3.23, 95% CI: 1.93, 5.41). Factors not associated with night blindness in the multivariate model were other markers of socioeconomic status such as electricity in the house, radio and television ownership, type of cooking fuel, and household transportation, and number of children under 5 years of age in the household. Conclusions Maternal night blindness was prevalent in this population. Being pregnant with twins and of higher parity put women at higher risk. Maternal literacy and higher socioeconomic status lowered the risk. PMID:19437315

Loss to follow-up in the Australian HIV Observational Database

PubMed Central

McManus, Hamish; Petoumenos, Kathy; Brown, Katherine; Baker, David; Russell, Darren; Read, Tim; Smith, Don; Wray, Lynne; Giles, Michelle; Hoy, Jennifer; Carr, Andrew; Law, Matthew

2015-01-01

Background Loss to follow-up (LTFU) in HIV-positive cohorts is an important surrogate for interrupted clinical care which can potentially influence the assessment of HIV disease status and outcomes. After preliminary evaluation of LTFU rates and patient characteristics, we evaluated the risk of mortality by LTFU status in a high resource setting. Methods Rates of LTFU were measured in the Australian HIV Observational Database for a range of patient characteristics. Multivariate repeated measures regression methods were used to identify determinants of LTFU. Mortality by LTFU status was ascertained using linkage to the National Death Index. Survival following combination antiretroviral therapy initiation was investigated using the Kaplan-Meier (KM) method and Cox proportional hazards models. Results Of 3,413 patients included in this analysis, 1,632 (47.8%) had at least one episode of LTFU after enrolment. Multivariate predictors of LTFU included viral load (VL)>10,000 copies/ml (Rate ratio (RR) 1.63 (95% confidence interval (CI):1.45–1.84) (ref ≤400)), time under follow-up (per year) (RR 1.03 (95% CI: 1.02–1.04)) and prior LTFU (per episode) (RR 1.15 (95% CI: 1.06–1.24)). KM curves for survival were similar by LTFU status (p=0.484). LTFU was not associated with mortality in Cox proportional hazards models (univariate hazard ratio (HR) 0.93 (95% CI: 0.69–1.26) and multivariate HR 1.04 (95% CI: 0.77–1.43)). Conclusions Increased risk of LTFU was identified amongst patients with potentially higher infectiousness. We did not find significant mortality risk associated with LTFU. This is consistent with timely re-engagement with treatment, possibly via high levels of unreported linkage to other health care providers. PMID:25377928

Quality of life in Chronic Pancreatitis is determined by constant pain, disability/unemployment, current smoking and associated co-morbidities

PubMed Central

Machicado, Jorge D.; Amann, Stephen T; Anderson, Michelle A.; Abberbock, Judah; Sherman, Stuart; Conwell, Darwin; Cote, Gregory A.; Singh, Vikesh K.; Lewis, Michele; Alkaade, Samer; Sandhu, Bimaljit S.; Guda, Nalini M.; Muniraj, Thiruvengadam; Tang, Gong; Baillie, John; Brand, Randall; Gardner, Timothy B.; Gelrud, Andres; Forsmark, Christopher E.; Banks, Peter A.; Slivka, Adam; Wilcox, C. Mel; Whitcomb, David C.; Yadav, Dhiraj

2018-01-01

Background Chronic pancreatitis (CP) has a profound independent effect on quality of life (QOL). Our aim was to identify factors that impact the QOL in CP patients. Methods We used data on 1,024 CP patients enrolled in the three NAPS2 studies. Information on demographics, risk factors, co-morbidities, disease phenotype and treatments was obtained from responses to structured questionnaires. Physical (PCS) and mental (MCS) component summary scores generated using responses to the Short Form-12 (SF-12) survey were used to assess QOL at enrollment. Multivariable linear regression models determined independent predictors of QOL. Results Mean PCS and MCS scores were 36.7±11.7 and 42.4±12.2, respectively. Significant (p<0.05) negative impact on PCS scores in multivariable analyses was noted due to constant mild-moderate pain with episodes of severe pain or constant severe pain (10 points), constant mild-moderate pain (5.2), pain-related disability/unemployment (5.1), current smoking (2.9 points) and medical co-morbidities. Significant (p<0.05) negative impact on MCS scores was related to constant pain irrespective of severity (6.8-6.9 points), current smoking (3.9 points) and pain-related disability/unemployment (2.4 points). In women, disability/unemployment resulted in an additional reduction 3.7 point reduction in MCS score. Final multivariable models explained 27% and 18% of the variance in PCS and MCS scores, respectively. Etiology, disease duration, pancreatic morphology, diabetes, exocrine insufficiency and prior endotherapy/pancreatic surgery had no significant independent effect on QOL. Conclusion Constant pain, pain-related disability/unemployment, current smoking, and concurrent co-morbidities significantly affect the QOL in CP. Further research is needed to identify factors impacting QOL not explained by our analyses. PMID:28244497

Alcohol consumption and breast cancer risk by estrogen receptor status: in a pooled analysis of 20 studies

PubMed Central

Jung, Seungyoun; Wang, Molin; Anderson, Kristin; Baglietto, Laura; Bergkvist, Leif; Bernstein, Leslie; van den Brandt, Piet A; Brinton, Louise; Buring, Julie E; Heather Eliassen, A; Falk, Roni; Gapstur, Susan M; Giles, Graham G; Goodman, Gary; Hoffman-Bolton, Judith; Horn-Ross, Pamela L; Inoue, Manami; Kolonel, Laurence N; Krogh, Vittorio; Lof, Marie; Maas, Paige; Miller, Anthony B; Neuhouser, Marian L; Park, Yikyung; Robien, Kim; Rohan, Thomas E; Scarmo, Stephanie; Schouten, Leo J; Sieri, Sabina; Stevens, Victoria L; Tsugane, Schoichiro; Visvanathan, Kala; Wilkens, Lynne R; Wolk, Alicja; Weiderpass, Elisabete; Willett, Walter C; Zeleniuch-Jacquotte, Anne; Zhang, Shumin M; Zhang, Xuehong; Ziegler, Regina G; Smith-Warner, Stephanie A

2016-01-01

Background: Breast cancer aetiology may differ by estrogen receptor (ER) status. Associations of alcohol and folate intakes with risk of breast cancer defined by ER status were examined in pooled analyses of the primary data from 20 cohorts. Methods: During a maximum of 6–18 years of follow-up of 1 089 273 women, 21 624 ER+ and 5113 ER− breast cancers were identified. Study-specific multivariable relative risks (RRs) were calculated using Cox proportional hazards regression models and then combined using a random-effects model. Results: Alcohol consumption was positively associated with risk of ER+ and ER− breast cancer. The pooled multivariable RRs (95% confidence intervals) comparing ≥ 30 g/d with 0 g/day of alcohol consumption were 1.35 (1.23-1.48) for ER+ and 1.28 (1.10-1.49) for ER− breast cancer (Ptrend ≤ 0.001; Pcommon-effects by ER status: 0.57). Associations were similar for alcohol intake from beer, wine and liquor. The associations with alcohol intake did not vary significantly by total (from foods and supplements) folate intake (Pinteraction ≥ 0.26). Dietary (from foods only) and total folate intakes were not associated with risk of overall, ER+ and ER− breast cancer; pooled multivariable RRs ranged from 0.98 to 1.02 comparing extreme quintiles. Following-up US studies through only the period before mandatory folic acid fortification did not change the results. The alcohol and folate associations did not vary by tumour subtypes defined by progesterone receptor status. Conclusions: Alcohol consumption was positively associated with risk of both ER+ and ER− breast cancer, even among women with high folate intake. Folate intake was not associated with breast cancer risk. PMID:26320033

Relations of Insulin Resistance and Glycemic Abnormalities to Cardiovascular Magnetic Resonance Measures of Cardiac Structure and Function: the Framingham Heart Study

PubMed Central

Velagaleti, Raghava S.; Gona, Philimon; Chuang, Michael L.; Salton, Carol J.; Fox, Caroline S.; Blease, Susan J.; Yeon, Susan B.; Manning, Warren J.; O’Donnell, Christopher J.

2011-01-01

Background Data regarding the relationships of diabetes, insulin resistance and sub-clinical hyperinsulinemia/hyperglycemia with cardiac structure and function are conflicting. We sought to apply volumetric cardiovascular magnetic resonance (CMR) in a free-living cohort to potentially clarify these associations. Methods and Results A total of 1603 Framingham Heart Study Offspring participants (age 64±9 years; 55% women) underwent CMR to determine left ventricular mass (LVM), LVM to end-diastolic volume ratio (LVM/LVEDV), relative wall thickness (RWT), ejection fraction (EF), cardiac output (CO) and left atrial size (LAD). Data regarding insulin resistance (homeostasis model, HOMA-IR) and glycemia categories (normal, impaired insulinemia or glycemia, pre-diabetes and diabetes) were determined. In a subgroup (253 men, 290 women) that underwent oral glucose tolerance testing, we related 2-hr insulin and glucose with CMR measures. In both men and women, all age-adjusted CMR measures increased across HOMA-IR quartiles, but multivariable-adjusted trends were significant only for LVM/ht2.7 and LVM/LVEDV. LVM/LVEDV and RWT were higher in participants with pre-diabetes and diabetes (in both sexes) in age-adjusted models, but these associations remained significant after multivariable-adjustment only in men. LVM/LVEDV was significantly associated with 2-hr insulin in men only, and RWT was significantly associated with 2-hr glucose in women only. In multivariable stepwise selection analyses, the inclusion of BMI led to a loss in statistical significance. Conclusions While insulin and glucose indices are associated with abnormalities in cardiac structure, insulin resistance and worsening glycemia are consistently and independently associated with LVM/LVEDV. These data implicate hyperglycemia and insulin resistance in concentric LV remodeling. PMID:20208015

Radiation Therapy Noncompliance and Clinical Outcomes in an Urban Academic Cancer Center

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

Ohri, Nitin; Rapkin, Bruce D.; Guha, Chandan

Purpose: To examine associations between radiation therapy (RT) noncompliance and clinical outcomes. Methods and Materials: We reviewed all patients who completed courses of external beam RT with curative intent in our department from the years 2007 to 2012 for cancers of the head and neck, breast, lung, cervix, uterus, or rectum. Patients who missed 2 or more scheduled RT appointments (excluding planned treatment breaks) were deemed noncompliant. Univariate, multivariable, and propensity-matched analyses were performed to examine associations between RT noncompliance and clinical outcomes. Results: Of 1227 patients, 266 (21.7%) were noncompliant. With median follow-up of 50.9 months, 108 recurrences (8.8%) and 228more » deaths (18.6%) occurred. In univariate analyses, RT noncompliance was associated with increased recurrence risk (5-year cumulative incidence 16% vs 7%, P<.001), inferior recurrence-free survival (5-year actuarial rate 63% vs 79%, P<.001), and inferior overall survival (5-year actuarial rate 72% vs 83%, P<.001). In multivariable analyses that were adjusted for disease site and stage, comorbidity score, gender, ethnicity, race, and socioeconomic status (SES), RT noncompliance was associated with inferior recurrence, recurrence-free survival, and overall survival rates. Propensity score–matched models yielded results nearly identical to those seen in univariate analyses. Low SES was associated with RT noncompliance and was associated with inferior clinical outcomes in univariate analyses, but SES was not associated with inferior outcomes in multivariable models. Conclusion: For cancer patients being treated with curative intent, RT noncompliance is associated with inferior clinical outcomes. The magnitudes of these effects demonstrate that RT noncompliance can serve as a behavioral biomarker to identify high-risk patients who require additional interventions. Treatment compliance may mediate the associations that have been observed linking SES and clinical outcomes.« less

Psychosocial predictors of depression among older African American cancer patients

PubMed Central

Hamilton, Jill B.; Deal, Allison M.; Moore, Angelo D.; Best, Nakia C.; Galbraith, Kayoll V.; Muss, Hyman

2013-01-01

Purpose To determine whether psychosocial factors predict depression among older African American cancer patients. Design/Methods A descriptive correlational study. Setting Outpatient oncology clinic of NCI designated Cancer Center in Southeastern U.S. Sample African American cancer patients aged 50 and over. Methods Fisher’s Exact and Wilcoxon Rank Sum tests were used to evaluate differences between patients who were possibly depressed (Geriatric Depression Scale) or not. Multivariate linear regression statistics were used to identify the psychosocial factors that predicted higher depression scores. Education and gender were included as covariates. Main Variables Religiosity, emotional support, collectivism, perceived stigma and depression. Findings African American cancer patients (n=77) were on average a median age of 58 years (IQR = 55–65), a majority were well-educated, insured, religiously affiliated, and currently in treatment. Participants in the lowest income category, not married, and male gender had higher depression scores. The multivariable model consisting of organized religion, emotional support, collectivism, education, and gender explained 52% (adjusted R2) of the variation in depression scores. Stigma became insignificant in the multivariable model. Conclusions Psychosocial factors are important predictors of depression. For these participants, emotional support and organized religious activities may represent protective factors against depression, while collectivism may increase their risk. Implications Nurses need to be especially aware of the potential psychological strain for patients with collectivist values, experienced stigma, disruptions in church attendance and lack of emotional support. Further, these treatment plans for these patients should ensure that family members are knowledgeable about cancer, its treatment and side effects so they are empowered to meet the needs for support of the African American cancer patient. PMID:23803271

Serum Vitamin D Levels and Markers of Severity of Childhood Asthma in Costa Rica

PubMed Central

Brehm, John M.; Celedón, Juan C.; Soto-Quiros, Manuel E.; Avila, Lydiana; Hunninghake, Gary M.; Forno, Erick; Laskey, Daniel; Sylvia, Jody S.; Hollis, Bruce W.; Weiss, Scott T.; Litonjua, Augusto A.

2009-01-01

Rationale: Maternal vitamin D intake during pregnancy has been inversely associated with asthma symptoms in early childhood. However, no study has examined the relationship between measured vitamin D levels and markers of asthma severity in childhood. Objectives: To determine the relationship between measured vitamin D levels and both markers of asthma severity and allergy in childhood. Methods: We examined the relation between 25-hydroxyvitamin D levels (the major circulating form of vitamin D) and markers of allergy and asthma severity in a cross-sectional study of 616 Costa Rican children between the ages of 6 and 14 years. Linear, logistic, and negative binomial regressions were used for the univariate and multivariate analyses. Measurements and Main Results: Of the 616 children with asthma, 175 (28%) had insufficient levels of vitamin D (<30 ng/ml). In multivariate linear regression models, vitamin D levels were significantly and inversely associated with total IgE and eosinophil count. In multivariate logistic regression models, a log10 unit increase in vitamin D levels was associated with reduced odds of any hospitalization in the previous year (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.004–0.71; P = 0.03), any use of antiinflammatory medications in the previous year (OR, 0.18; 95% CI, 0.05–0.67; P = 0.01), and increased airway responsiveness (a ≤8.58-μmol provocative dose of methacholine producing a 20% fall in baseline FEV1 [OR, 0.15; 95% CI, 0.024–0.97; P = 0.05]). Conclusions: Our results suggest that vitamin D insufficiency is relatively frequent in an equatorial population of children with asthma. In these children, lower vitamin D levels are associated with increased markers of allergy and asthma severity. PMID:19179486

Survival Advantage in Black Versus White Men With CKD: Effect of Estimated GFR and Case Mix

PubMed Central

Kovesdy, Csaba P.; Quarles, L. Darryl; Lott, Evan H.; Lu, Jun Ling; Ma, Jennie Z.; Molnar, Miklos Z.; Kalantar-Zadeh, Kamyar

2013-01-01

Background Black dialysis patients have significantly lower mortality compared to white patients, in contradistinction to the higher mortality seen in blacks in the general population. It is unclear if a similar paradox exists in non–dialysis-dependent CKD, and if it does, what its underlying reasons are. Study Design Historical cohort. Setting & Participants 518,406 white and 52,402 black male US veterans with non-dialysis dependent CKD stages 3–5. Predictor Black race. Outcomes & Measurements We examined overall and CKD stage-specific all-cause mortality using parametric survival models. The effect of sociodemographic characteristics, comorbidities and laboratory characteristics on the observed differences was explored in multivariable models. Results Over a median follow-up of 4.7 years 172,093 patients died (mortality rate, 71.0 [95% CI, 70.6–71.3] per 1000 patient-years). Black race was associated with significantly lower crude mortality (HR, 0.95; 95% CI, 0.94–0.97; p<0.001). The survival advantage was attenuated after adjustment for age (HR, 1.14; 95% CI, 1.12–1.16), but was even magnified after full multivariable adjustment (HR, 0.72; 95% CI, 0.70–0.73; p<0.001). The unadjusted survival advantage of blacks was more prominent in those with more advanced stages of CKD, but CKD stage-specific differences were attenuated by multivariable adjustment. Limitations Exclusively male patients. Conclusions Black patients with CKD have lower mortality compared to white patients. The survival advantage seen in blacks is accentuated in patients with more advanced stages of CKD, which may be explained by changes in case mix and laboratory characteristics occurring during the course of kidney disease. PMID:23369826

The structural equation analysis of childhood abuse, adult stressful life events, and temperaments in major depressive disorders and their influence on refractoriness

PubMed Central

Toda, Hiroyuki; Inoue, Takeshi; Tsunoda, Tomoya; Nakai, Yukiei; Tanichi, Masaaki; Tanaka, Teppei; Hashimoto, Naoki; Nakato, Yasuya; Nakagawa, Shin; Kitaichi, Yuji; Mitsui, Nobuyuki; Boku, Shuken; Tanabe, Hajime; Nibuya, Masashi; Yoshino, Aihide; Kusumi, Ichiro

2015-01-01

Background Previous studies have shown the interaction between heredity and childhood stress or life events on the pathogenesis of a major depressive disorder (MDD). In this study, we tested our hypothesis that childhood abuse, affective temperaments, and adult stressful life events interact and influence the diagnosis of MDD. Patients and methods A total of 170 healthy controls and 98 MDD patients were studied using the following self-administered questionnaire surveys: the Patient Health Questionnaire-9 (PHQ-9), the Life Experiences Survey, the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire, and the Child Abuse and Trauma Scale (CATS). The data were analyzed with univariate analysis, multivariable analysis, and structural equation modeling. Results The neglect scores of the CATS indirectly predicted the diagnosis of MDD through cyclothymic and anxious temperament scores of the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire in the structural equation modeling. Two temperaments – cyclothymic and anxious – directly predicted the diagnosis of MDD. The validity of this result was supported by the results of the stepwise multivariate logistic regression analysis as follows: three factors – neglect, cyclothymic, and anxious temperaments – were significant predictors of MDD. Neglect and the total CATS scores were also predictors of remission vs treatment-resistance in MDD patients independently of depressive symptoms. Limitations The sample size was small for the comparison between the remission and treatment-resistant groups in MDD patients in multivariable analysis. Conclusion This study suggests that childhood abuse, especially neglect, indirectly predicted the diagnosis of MDD through increased affective temperaments. The important role as a mediator of affective temperaments in the effect of childhood abuse on MDD was suggested. PMID:26316754

Association of hand or knee osteoarthritis with diabetes mellitus in a population of Hispanics from Puerto Rico

PubMed Central

Nieves-Plaza, Mariely; Castro-Santana, Lesliane E.; Font, Yvonne M.; Mayor, Angel M.; Vilá, Luis M.

2013-01-01

Background Although a higher prevalence of osteoarthritis (OA) has been reported among diabetes mellitus (DM) patients, inconsistencies and limitations of observational studies have precluded a conclusive association. Objective To evaluate the association of hand or knee OA with DM in a population of Hispanics from Puerto Rico. Methods A cross-sectional study was performed in 202 subjects (100 adult DM patients as per the National Diabetes Data Group Classification, and 102 non-diabetic subjects). OA of hand and knee was ascertained using the American College of Rheumatology classification criteria. Sociodemographic characteristics, health-related behaviors, comorbidities, pharmacotherapy and DM clinical manifestations were determined. Multivariable logistic regression was used to evaluate the association of DM with hand or knee OA, and to evaluate factors associated with hand or knee OA among DM patients. Results The mean (standard deviation, SD) age for DM patients was 51.6 (13.1) years; 64.0% were females. The mean (SD) DM duration was 11.0 (10.4) years. The prevalence of OA in patients with DM and non-diabetics subjects was 49.0% and 26.5%, respectively (p<0.01). In the multivariable analysis, patients with DM had 2.18 the odds of having OA when compared to non-diabetic subjects (95% CI: 1.12–4.24). In a sub-analysis among DM patients, female patients were more likely to have hand or knee OA (OR [95% CI]: 5.06 [1.66–15.66]), whereas patients who did not use insulin alone for DM therapy were more likely to have OA (OR [95% CI]: 4.44 [1.22–16.12]). Conclusion In this population of Hispanics from Puerto Rico, DM patients were more likely to have OA of hands or knees than non-diabetic subjects. This association was retained in multivariable models accounting for established risk factors for OA. Among DM patients, females were at greater risk for OA, whereas the use of insulin was negatively associated. PMID:23319016

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance.

PubMed

Muratov, Eugene; Lewis, Margaret; Fourches, Denis; Tropsha, Alexander; Cox, Wendy C

2017-04-01

Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Back pain prevalence and associated factors in children and adolescents: an epidemiological population study

PubMed Central

Noll, Matias; Candotti, Cláudia Tarragô; da Rosa, Bruna Nichele; Loss, Jefferson Fagundes

2016-01-01

ABSTRACT OBJECTIVE To identify the prevalence of back pain among Brazilian school children and the factors associated with this pain. METHODS All 1,720 schoolchildren from the fifth to the eight grade attending schools from the city of Teutonia, RS, Southern Brazil, were invited to participate in the study. From these, 1,597 children participated. We applied the Back Pain and Body Posture Evaluation Instrument. The dependent variable was back pain, while the independent one were demographic, socioeconomic, behavior and heredity data. The prevalence ratio was estimated by multivariate analysis using the Poisson regression model (α = 0.05). RESULTS The prevalence of back pain in the last three months was 55.7% (n = 802). The multivariate analysis showed that back pain is associated with the variables: sex, parents with back pain, weekly frequency of physical activity, daily time spent watching television, studying in bed, sitting posture to write and use the computer, and way of carrying the backpack. CONCLUSIONS The prevalence of back pain in schoolchildren is high and it is associated with demographic, behavior and heredity aspects. PMID:27305406

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

SU-F-R-07: Radiomics of CT Features and Associations and Correlation with Outcomes Following Lung SBRT

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

Schreibmann, E; Iwinski Sutter, A; Whitaker, D

Objective: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. Methods: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures. Multivariate and univariate models containing CT features were generated to assess associations with clinicopathologic factors.more » Results: On univariate analysis, tumor size (HR 0.54, p=0.045) sumHU (HR 0.31, p=0.044) and short run grey level emphasis STD (HR 0.22, p=0.019) were associated with regional failure-free survival; meanHU (HR 0.30, p=0.035), long run emphasis (HR 0.21, p=0.011) and long run low grey level emphasis (HR 0.14, p=0.005) was associated with distant failure-free survival (DFFS). No features were significant on multivariate modeling however long run low grey level emphasis had a hazard ratio of 0.12 (p=0.061) for DFFS. Adenocarcinoma and squamous cell carcinoma differed with respect to long run emphasis STD (p=0.024), short run low grey level emphasis STD (p<0.001), and long run low grey level emphasis STD (p=0.024). Multivariate modeling of texture features associated with tumor histology was used to estimate histologies of 18 lesions treated without histologic confirmation. Of these, MVA suggested the same histology as a prior metachronous lung malignancy in 3/7 patients. Conclusion: Extracting radiomics features on clinical datasets was feasible with the ITK package with minimal effort to identify pre-treatment quantitative CT features with prognostic factors for distant control after lung SBRT.« less

PTEN Loss as Determined by Clinical-grade Immunohistochemistry Assay Is Associated with Worse Recurrence-free Survival in Prostate Cancer

PubMed Central

Lotan, Tamara L.; Wei, Wei; Morais, Carlos L.; Hawley, Sarah T.; Fazli, Ladan; Hurtado-Coll, Antonio; Troyer, Dean; McKenney, Jesse K.; Simko, Jeffrey; Carroll, Peter R.; Gleave, Martin; Lance, Raymond; Lin, Daniel W.; Nelson, Peter S.; Thompson, Ian M.; True, Lawrence D.; Feng, Ziding; Brooks, James D.

2015-01-01

Background PTEN is the most commonly deleted tumor suppressor gene in primary prostate cancer (PCa) and its loss is associated with poor clinical outcomes and ERG gene rearrangement. Objective We tested whether PTEN loss is associated with shorter recurrence-free survival (RFS) in surgically treated PCa patients with known ERG status. Design, setting, and participants A genetically validated, automated PTEN immunohistochemistry (IHC) protocol was used for 1275 primary prostate tumors from the Canary Foundation retrospective PCa tissue microarray cohort to assess homogeneous (in all tumor tissue sampled) or heterogeneous (in a subset of tumor tissue sampled) PTEN loss. ERG status as determined by a genetically validated IHC assay was available for a subset of 938 tumors. Outcome measurements and statistical analysis Associations between PTEN and ERG status were assessed using Fisher’s exact test. Kaplan-Meier and multivariate weighted Cox proportional models for RFS were constructed. Results and limitations When compared to intact PTEN, homogeneous (hazard ratio [HR] 1.66, p = 0.001) but not heterogeneous (HR 1.24, p = 0.14) PTEN loss was significantly associated with shorter RFS in multivariate models. Among ERG-positive tumors, homogeneous (HR 3.07, p < 0.0001) but not heterogeneous (HR 1.46, p = 0.10) PTEN loss was significantly associated with shorter RFS. Among ERG-negative tumors, PTEN did not reach significance for inclusion in the final multivariate models. The interaction term for PTEN and ERG status with respect to RFS did not reach statistical significance (p = 0.11) for the current sample size. Conclusions These data suggest that PTEN is a useful prognostic biomarker and that there is no statistically significant interaction between PTEN and ERG status for RFS. Patient summary We found that loss of the PTEN tumor suppressor gene in prostate tumors as assessed by tissue staining is correlated with shorter time to prostate cancer recurrence after radical prostatectomy. PMID:27617307

Embolotherapy for Neuroendocrine Tumor Liver Metastases: Prognostic Factors for Hepatic Progression-Free Survival and Overall Survival

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

Chen, James X.; Rose, Steven; White, Sarah B.

PurposeThe purpose of the study was to evaluate prognostic factors for survival outcomes following embolotherapy for neuroendocrine tumor (NET) liver metastases.Materials and MethodsThis was a multicenter retrospective study of 155 patients (60 years mean age, 57 % male) with NET liver metastases from pancreas (n = 71), gut (n = 68), lung (n = 8), or other/unknown (n = 8) primary sites treated with conventional transarterial chemoembolization (TACE, n = 50), transarterial radioembolization (TARE, n = 64), or transarterial embolization (TAE, n = 41) between 2004 and 2015. Patient-, tumor-, and treatment-related factors were evaluated for prognostic effect on hepatic progression-free survival (HPFS) and overall survival (OS) using unadjusted and propensity score-weighted univariate and multivariate Coxmore » proportional hazards models.ResultsMedian HPFS and OS were 18.5 and 125.1 months for G1 (n = 75), 12.2 and 33.9 months for G2 (n = 60), and 4.9 and 9.3 months for G3 tumors (n = 20), respectively (p < 0.05). Tumor burden >50 % hepatic volume demonstrated 5.5- and 26.8-month shorter median HPFS and OS, respectively, versus burden ≤50 % (p < 0.05). There were no significant differences in HPFS or OS between gut or pancreas primaries. In multivariate HPFS analysis, there were no significant differences among embolotherapy modalities. In multivariate OS analysis, TARE had a higher hazard ratio than TACE (unadjusted Cox model: HR 2.1, p = 0.02; propensity score adjusted model: HR 1.8, p = 0.11), while TAE did not differ significantly from TACE.ConclusionHigher tumor grade and tumor burden prognosticated shorter HPFS and OS. TARE had a higher hazard ratio for OS than TACE. There were no significant differences in HPFS among embolotherapy modalities.« less

Serum and Dietary Potassium and Risk of Incident Type 2 Diabetes: The Atherosclerosis Risk in Communities (ARIC) Study

PubMed Central

Chatterjee, Ranee; Yeh, Hsin-Chieh; Shafi, Tariq; Selvin, Elizabeth; Anderson, Cheryl; Pankow, James S.; Miller, Edgar; Brancati, Frederick

2012-01-01

Background Serum potassium levels affect insulin secretion by pancreatic beta-cells, and hypokalemia associated with diuretic use has been associated with dysglycemia. We hypothesized that adults with lower serum potassium levels and lower dietary potassium intake are at higher risk for incident diabetes, independent of diuretic use. Methods We analyzed data from 12,209 participants from the Atherosclerosis Risk in Communities (ARIC) Study, an on-going prospective cohort study beginning in 1986, with 9 years of in-person follow-up and 17 years of telephone follow-up. Using multivariate Cox proportional hazard models, we estimated the relative hazard (RH) of incident diabetes associated with baseline serum potassium levels. Results During 9 years of in-person follow-up, 1475 participants developed incident diabetes. In multivariate analyses, we found an inverse association between serum potassium and risk of incident diabetes. Compared to those with a high-normal serum potassium (5.0-5.5 mEq/l), adults with serum potassium levels of < 4.0, 4.0-<4.5, and 4.5-<5.0, (mEq/L) had adjusted relative hazards (RH) (95% CI) of incident diabetes of 1.64 (1.29-2.08), 1.64 (1.34-2.01), and 1.39 (1.14-1.71) respectively. An increased risk persisted during an additional 8 years of telephone follow-up based on self-report with RHs of 1.2-1.3 for those with a serum potassium less than 5.0 mEq/L. Dietary potassium intake was significantly associated with risk of incident diabetes in unadjusted models but not in multivariate models. Conclusions Serum potassium is an independent predictor of incident diabetes in this cohort. Further study is needed to determine if modification of serum potassium could reduce the subsequent risk of diabetes. PMID:20975023

Association of Discharge Home with Home Health Care and 30-day Readmission after Pancreatectomy

PubMed Central

Sanford, Dominic E; Olsen, Margaret A; Bommarito, Kerry M; Shah, Manish; Fields, Ryan C; Hawkins, William G; Jaques, David P; Linehan, David C

2014-01-01

Background We sought to determine if discharge home with home health care (HHC) is an independent predictor of increased readmission following pancreatectomy. Study Design We examined 30-day readmissions in patients undergoing pancreatectomy using the Healthcare Cost and Utilization Project State Inpatient Database for California from 2009 to 2011. Readmissions were categorized as severe or non-severe using the Modified Accordion Severity Grading System. Multivariable logistic regression models were used to examine the association of discharge home with HHC and 30-day readmission using discharge home without HHC as the reference group. Propensity score matching was used as an additional analysis to compare the rate of 30-day readmission between patients discharged home with HHC to patients discharged home without HHC. Results 3,573 patients underwent pancreatectomy and 752 (21.0%) were readmitted within 30 days of discharge. In a multivariable logistic regression model, discharge home with HHC was an independent predictor of increased 30-day readmission (OR=1.37; 95%CI=1.11-1.69, p=0.004). Using propensity score matching, patients who received HHC had a significantly increased rate of 30-day readmission compared to patients discharged home without HHC (24.3% vs 19.8%, p<0.001). Patients discharged home with HHC had a significantly increased rate of non-severe readmission compared to those discharged home without HHC by univariate comparison (19.2% vs 13.9%, p<0.001), but not severe readmission (6.4% vs 4.7%, p= 0.08). In multivariable logistic regression models, excluding patients discharged to facilities, discharge home with HHC was an independent predictor of increased non-severe readmissions (OR=1.41; 95%CI=1.11-1.79, p=0.005), but not severe readmissions (OR=1.31; 95%CI=0.88-1.93, p=0.18). Conclusions Discharge home with HHC following pancreatectomy is an independent predictor of increased 30-day readmission; specifically, these services are associated with increased non-severe readmissions, but not severe readmissions. PMID:25440026

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

PubMed

Li, Haocheng; Zhang, Yukun; Carroll, Raymond J; Keadle, Sarah Kozey; Sampson, Joshua N; Matthews, Charles E

2017-11-10

A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

Angiotensin-converting Enzyme Inhibitor and Statin Medication Use and Incident Mobility Limitation in Community Older Adults. The Health, Aging and Body Composition Study

PubMed Central

Gray, Shelly L.; Boudreau, Robert M.; Newman, Anne B.; Studenski, Stephanie A.; Shorr, Ronald I; Bauer, Douglas C.; Simonsick, Eleanor M.; Hanlon, Joseph T

2012-01-01

Objective Angiotensin-converting enzyme (ACE) inhibitors and statin medications have been proposed as potential agents to prevent or delay physical disability; yet limited research has evaluated whether such use in older community dwelling adults is associated with a lower risk of incident mobility limitation. Design Longitudinal cohort study Setting Health, Aging and Body Composition (Health ABC) Participants 3055 participants who were well functioning at baseline (e.g., no mobility limitations). Measurements Summated standardized daily doses (low, medium and high) and duration of ACE inhibitor and statin use was computed. Mobility limitation (two consecutive self-reports of having any difficulty walking 1/4 mile or climbing 10 steps without resting) was assessed every 6 months after baseline. Multivariable Cox proportional hazard analyses were conducted adjusting for demographics, health status, and health behaviors. Results At baseline, ACE inhibitors and statins were used by 15.2% and 12.9%, respectively and both increased to over 25% by year 6. Over 6.5 years of follow-up, 49.8% had developed mobility limitation. In separate multivariable models, neither ACE inhibitor (multivariate hazard ratio [HR] 0.95; 95% confidence interval [CI] 0.82–1.09) nor statin use (multivariate HR 1.02; 95% CI 0.87–1.17) was associated with a lower risk for mobility limitation. Similar findings were seen in analyses examining dose- and duration-response relationships and sensitivity analyses restricted to those with hypertension. Conclusions These findings indicate that ACE inhibitors and statins widely prescribed to treat hypertension and hypercholesterolemia, respectively do not lower risk of mobility limitation, an important life quality indicator. PMID:22092102

Alcohol Intake and Risk of Incident Melanoma: A Pooled Analysis of Three Prospective Studies in the U.S

PubMed Central

Rivera, Andrew; Nan, Hongmei; Li, Tricia; Qureshi, Abrar; Cho, Eunyoung

2016-01-01

Background Alcohol consumption is associated with increased risk of numerous cancers, but existing evidence for an association with melanoma is equivocal. No study has evaluated the association with different anatomic locations of melanoma. Methods We used data from three large prospective cohort studies to investigate whether alcohol intake was associated with risk of melanoma. Alcohol intake was assessed repeatedly by food-frequency questionnaires. A Cox proportional hazards model was used to calculate multivariate-adjusted hazard ratios (HRs). Results A total of 1,374 cases of invasive melanoma were documented during 3,855,706 person-years of follow-up. There was an association between higher alcohol intake and incidence of invasive melanoma (pooled multivariate HR 1.14; 95% confidence interval [CI]: 1.00–1.29] per drink/d, p trend = 0.04). Among alcoholic beverages, white wine consumption was associated with an increased risk of melanoma (pooled multivariate HR 1.13 [95% CI: 1.04–1.24] per drink/d, p trend <0.01) after adjusting for other alcoholic beverages. The association between alcohol consumption and melanoma risk was stronger for melanoma in relatively UV-spared sites (trunk) versus more UV-exposed sites (head, neck, or extremities). Compared to non-drinkers, the pooled multivariate-adjusted HRs for ≥20g/d of alcohol were 1.02 (95% CI: 0.64–1.62; P trend =0.25) for melanomas of the head, neck, and extremities and 1.73 (95% CI: 1.25–2.38; P trend =0.02) for melanomas of the trunk. Conclusions Alcohol intake was associated with a modest increase in the risk of melanoma, particularly in UV-protected sites. Impact These findings further support American Cancer Society Guidelines for Cancer Prevention to limit alcohol intake. PMID:27909090

Pretreatment Health Behaviors Predict Survival Among Patients With Head and Neck Squamous Cell Carcinoma

PubMed Central

Duffy, Sonia A.; Ronis, David L.; McLean, Scott; Fowler, Karen E.; Gruber, Stephen B.; Wolf, Gregory T.; Terrell, Jeffrey E.

2009-01-01

Purpose Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. Patients and Methods A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Results Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Conclusion Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival. PMID:19289626

THE AFRICAN DESCENT AND GLAUCOMA EVALUATION STUDY (ADAGES): PREDICTORS OF VISUAL FIELD DAMAGE IN GLAUCOMA SUSPECTS

PubMed Central

Khachatryan, Naira; Medeiros, Felipe A.; Sharpsten, Lucie; Bowd, Christopher; Sample, Pamela A.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Weinreb, Robert N.; Miki, Atsuya; Hammel, Na’ama; Zangwill, Linda M.

2015-01-01

Purpose To evaluate racial differences in the development of visual field (VF) damage in glaucoma suspects. Design Prospective, observational cohort study. Methods Six hundred thirty six eyes from 357 glaucoma suspects with normal VF at baseline were included from the multicenter African Descent and Glaucoma Evaluation Study (ADAGES). Racial differences in the development of VF damage were examined using multivariable Cox Proportional Hazard models. Results Thirty one (25.4%) of 122 African descent participants and 47 (20.0%) of 235 European descent participants developed VF damage (p=0.078). In multivariable analysis, worse baseline VF mean deviation, higher mean arterial pressure during follow up, and a race *mean intraocular pressure (IOP) interaction term were significantly associated with the development of VF damage suggesting that racial differences in the risk of VF damage varied by IOP. At higher mean IOP levels, race was predictive of the development of VF damage even after adjusting for potentially confounding factors. At mean IOPs during follow-up of 22, 24 and 26 mmHg, multivariable hazard ratios (95%CI) for the development of VF damage in African descent compared to European descent subjects were 2.03 (1.15–3.57), 2.71 (1.39–5.29), and 3.61 (1.61–8.08), respectively. However, at lower mean IOP levels (below 22 mmHg) during follow-up, African descent was not predictive of the development of VF damage. Conclusion In this cohort of glaucoma suspects with similar access to treatment, multivariate analysis revealed that at higher mean IOP during follow-up, individuals of African descent were more likely to develop VF damage than individuals of European descent. PMID:25597839

DSM-based problem gambling: increasing the odds of heavy drinking in a national sample of U.S. college athletes?

PubMed

Huang, Jiun-Hau; Jacobs, Durand F; Derevensky, Jeffrey L

2011-03-01

Despite previously found co-occurrence of youth gambling and alcohol use, their relationship has not been systematically explored in a national sample using DSM-based gambling measures and multivariate modeling, adjusted for potential confounders. This study aimed to empirically examine the prevalence patterns and odds of at-least-weekly alcohol use and heavy episodic drinking (HED) in relation to various levels of gambling severity in college athletes. Multivariate logistic regression analyses were performed on data from a national sample of 20,739 U.S. college athletes from the first National Collegiate Athletic Association national survey of gambling and health-risk behaviors. Prevalence of at-least-weekly alcohol use significantly increased as DSM-IV-based gambling severity increased, from non-gambling (24.5%) to non-problem gambling (43.7%) to sub-clinical gambling (58.5%) to problem gambling (67.6%). Multivariate results indicated that all levels of gambling were associated with significantly elevated risk of at-least-weekly HED, from non-problem (OR = 1.25) to sub-clinical (OR = 1.75) to problem gambling (OR = 3.22); the steep increase in the relative risk also suggested a possible quadratic relationship between gambling level and HED risk. Notably, adjusted odds ratios showed problem gambling had the strongest association with at-least-weekly HED, followed by marijuana (OR = 3.08) and cigarette use (OR = 2.64). Gender interactions and differences were also identified and assessed. In conclusion, attention should be paid to college athletes exhibiting gambling problems, especially considering their empirical multivariate associations with high-risk drinking; accordingly, screening for problem gambling is recommended. More research is warranted to elucidate the etiologic mechanisms of these associations. Copyright © 2010 Elsevier Ltd. All rights reserved.

Racial Differences in Circulating Natriuretic Peptide Levels: The Atherosclerosis Risk in Communities Study

PubMed Central

Gupta, Deepak K; Claggett, Brian; Wells, Quinn; Cheng, Susan; Li, Man; Maruthur, Nisa; Selvin, Elizabeth; Coresh, Josef; Konety, Suma; Butler, Kenneth R; Mosley, Thomas; Boerwinkle, Eric; Hoogeveen, Ron; Ballantyne, Christie M; Solomon, Scott D

2015-01-01

Background Natriuretic peptides promote natriuresis, diuresis, and vasodilation. Experimental deficiency of natriuretic peptides leads to hypertension (HTN) and cardiac hypertrophy, conditions more common among African Americans. Hospital-based studies suggest that African Americans may have reduced circulating natriuretic peptides, as compared to Caucasians, but definitive data from community-based cohorts are lacking. Methods and Results We examined plasma N-terminal pro B-type natriuretic peptide (NTproBNP) levels according to race in 9137 Atherosclerosis Risk in Communities (ARIC) Study participants (22% African American) without prevalent cardiovascular disease at visit 4 (1996–1998). Multivariable linear and logistic regression analyses were performed adjusting for clinical covariates. Among African Americans, percent European ancestry was determined from genetic ancestry informative markers and then examined in relation to NTproBNP levels in multivariable linear regression analysis. NTproBNP levels were significantly lower in African Americans (median, 43 pg/mL; interquartile range [IQR], 18, 88) than Caucasians (median, 68 pg/mL; IQR, 36, 124; P<0.0001). In multivariable models, adjusted log NTproBNP levels were 40% lower (95% confidence interval [CI], −43, −36) in African Americans, compared to Caucasians, which was consistent across subgroups of age, gender, HTN, diabetes, insulin resistance, and obesity. African-American race was also significantly associated with having nondetectable NTproBNP (adjusted OR, 5.74; 95% CI, 4.22, 7.80). In multivariable analyses in African Americans, a 10% increase in genetic European ancestry was associated with a 7% (95% CI, 1, 13) increase in adjusted log NTproBNP. Conclusions African Americans have lower levels of plasma NTproBNP than Caucasians, which may be partially owing to genetic variation. Low natriuretic peptide levels in African Americans may contribute to the greater risk for HTN and its sequalae in this population. PMID:25999400

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

PubMed Central

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

2013-01-01

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

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Can Patient Comorbidities Be Included in Clinical Performance Measures for Radiation Oncology?

PubMed Central

Owen, Jean B.; Khalid, Najma; Ho, Alex; Kachnic, Lisa A.; Komaki, Ritsuko; Tao, May Lin; Currey, Adam; Wilson, J. Frank

2014-01-01

Purpose: Patient comorbidities may affect the applicability of performance measures that are inherent in multidisciplinary cancer treatment guidelines. This article describes the distribution of common comorbid conditions by disease site and by patient and facility characteristics in patients who received radiation therapy as part of treatment for cancer of the breast, cervix, lung, prostate, and stomach, and investigates the association of comorbidities with treatment decisions. Materials and Methods: Stratified two-stage cluster sampling provided a random sample of radiation oncology facilities. Eligible patients were randomly sampled from each participating facility for each disease site, and data were abstracted from medical records. The Adult Comorbidity Evaluation Index (ACE-27) was used to measure comorbid conditions and their severity. National estimates were calculated using SUDAAN statistical software. Results: Multivariable logistic regression models predicted the dependent variable “treatment changed or contraindicated due to comorbidities.” The final model showed that ACE-27 was highly associated with change in treatment for patients with severe or moderate index values compared to those with none or mild (P < .001). Two other covariates, age and medical coverage, had no (age) or little (medical coverage) significant contribution to predicting treatment change in the multivariable model. Disease site was associated with treatment change after adjusting for other covariates in the model. Conclusions: ACE-27 is highly predictive of treatment modifications for patients treated for these cancers who receive radiation as part of their care. A standardized tool identifying patients who should be excluded from clinical performance measures allows more accurate use of these measures. PMID:24643573

Determinants of Medical System Delay in the Diagnosis of Colorectal Cancer within the Veteran Affairs Health System

PubMed Central

Fisher, Deborah A.; Zullig, Leah L.; Grambow, Steven C.; Abbott, David H.; Sandler, Robert S.; Fletcher, Robert H.; El-Serag, Hashem B.; Provenzale, Dawn

2010-01-01

Background & Aims The goals of this study were to evaluate determinants of the time in the medical system until a colorectal cancer diagnosis and to explore characteristics associated with stage at diagnosis. Methods We examined medical records and survey data for 468 patients with colorectal cancer at 15 Veterans Affairs medical centers. Patients were classified as screen-detected, bleeding-detected, or other (resulting from the evaluation of another medical concern). Patients who presented emergently with obstruction or perforation were excluded. We used Cox proportional hazards models to determine predictors of time in the medical system until diagnosis. Logistic regression models were used to determine predictors of stage at diagnosis. Results We excluded 21 subjects who presented emergently leaving 447 subjects; the mean age was 67 years and 98% were male, 66% Caucasian, and 43% stage I or II. Diagnosis was by screening for 39%, bleeding symptoms for 27% and other for 34%. The median times to diagnosis were 73–91 days and not significantly different by diagnostic category. In the multivariable model for time-to-diagnosis, older age, having comorbidities, and Atlantic region were associated with a longer time to diagnosis. In the multivariable model for stage-at-diagnosis only diagnostic category was associated with stage; screen-detected category was associated with decreased risk of late stage cancer. Conclusions Our results point to several factors associated with a longer time from the initial clinical event until diagnosis. This increased time in the health care system did not clearly translate into more advanced disease at diagnosis. PMID:20238248

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study

PubMed Central

Hamilton, C. A.; Miller, A.; Casablanca, Y.; Horowitz, N. S.; Rungruang, B.; Krivak, T. C.; Richard, S. D.; Rodriguez, N.; Birrer, M.J.; Backes, F.J.; Geller, M.A.; Quinn, M.; Goodheart, M.J.; Mutch, D.G.; Kavanagh, J.J.; Maxwell, G. L.; Bookman, M. A.

2018-01-01

Objective To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Methods Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10 years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). Results The analysis dataset included 3,010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. Conclusions The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. PMID:29195926

Occupational outcomes of adult childhood cancer survivors: A report from the Childhood Cancer Survivor Study

PubMed Central

Kirchhoff, Anne C.; Krull, Kevin R.; Ness, Kirsten K.; Park, Elyse R.; Oeffinger, Kevin C.; Hudson, Melissa M.; Stovall, Marilyn; Robison, Leslie L.; Wickizer, Thomas; Leisenring, Wendy

2010-01-01

Background We examined whether survivors from the Childhood Cancer Survivor Study were less likely to be in higher skill occupations than a sibling comparison and whether certain survivors were at higher risk. Methods We created three mutually-exclusive occupational categories for participants aged ≥25 years: Managerial/Professional and Non-Physical and Physical Service/Blue Collar. We examined currently employed survivors (N=4845) and siblings (N=1727) in multivariable generalized linear models to evaluate the likelihood of being in the three occupational categories. Among all participants, we used multinomial logistic regression to examine the likelihood of these outcomes in comparison to being unemployed (survivors N=6671; siblings N=2129). Multivariable linear models were used to assess survivor occupational differences by cancer and treatment variables. Personal income was compared by occupation. Results Employed survivors were less often in higher skilled Managerial/Professional occupations (Relative Risk=0.93, 95% Confidence Interval 0.89–0.98) than siblings. Survivors who were Black, were diagnosed at a younger age, or had high-dose cranial radiation were less likely to hold Professional occupations than other survivors. In multinomial models, female survivors’ likelihood of being in full-time Professional occupations (27%) was lower than male survivors (42%) and female (41%) and male (50%) siblings. Survivors’ personal income was lower than siblings within each of the three occupational categories in models adjusted for sociodemographic variables. Conclusions Adult childhood cancer survivors are employed in lower skill jobs than siblings. Survivors with certain treatment histories are at higher risk and may require vocational assistance throughout adulthood. PMID:21246530

Chemotherapy effectiveness and mortality prediction in surgically treated osteosarcoma dogs: A validation study.

PubMed

Schmidt, A F; Nielen, M; Withrow, S J; Selmic, L E; Burton, J H; Klungel, O H; Groenwold, R H H; Kirpensteijn, J

2016-03-01

Canine osteosarcoma is the most common bone cancer, and an important cause of mortality and morbidity, in large purebred dogs. Previously we constructed two multivariable models to predict a dog's 5-month or 1-year mortality risk after surgical treatment for osteosarcoma. According to the 5-month model, dogs with a relatively low risk of 5-month mortality benefited most from additional chemotherapy treatment. In the present study, we externally validated these results using an independent cohort study of 794 dogs. External performance of our prediction models showed some disagreement between observed and predicted risk, mean difference: -0.11 (95% confidence interval [95% CI]-0.29; 0.08) for 5-month risk and 0.25 (95%CI 0.10; 0.40) for 1-year mortality risk. After updating the intercept, agreement improved: -0.0004 (95%CI-0.16; 0.16) and -0.002 (95%CI-0.15; 0.15). The chemotherapy by predicted mortality risk interaction (P-value=0.01) showed that the chemotherapy compared to no chemotherapy effectiveness was modified by 5-month mortality risk: dogs with a relatively lower risk of mortality benefited most from additional chemotherapy. Chemotherapy effectiveness on 1-year mortality was not significantly modified by predicted risk (P-value=0.28). In conclusion, this external validation study confirmed that our multivariable risk prediction models can predict a patient's mortality risk and that dogs with a relatively lower risk of 5-month mortality seem to benefit most from chemotherapy. Copyright © 2016 Elsevier B.V. All rights reserved.

The development and validation of a multivariable model to predict whether patients referred for total knee replacement are suitable surgical candidates at the time of initial consultation

PubMed Central

Churchill, Laura; Malian, Samuel J.; Chesworth, Bert M.; Bryant, Dianne; MacDonald, Steven J.; Marsh, Jacquelyn D.; Giffin, J. Robert

2016-01-01

Background In previous studies, 50%–70% of patients referred to orthopedic surgeons for total knee replacement (TKR) were not surgical candidates at the time of initial assessment. The purpose of our study was to identify and cross-validate patient self-reported predictors of suitability for TKR and to determine the clinical utility of a predictive model to guide the timing and appropriateness of referral to a surgeon. Methods We assessed pre-consultation patient data as well as the surgeon’s findings and post-consultation recommendations. We used multivariate logistic regression to detect self-reported items that could identify suitable surgical candidates. Results Patients’ willingness to undergo surgery, higher rating of pain, greater physical function, previous intra-articular injections and patient age were the factors predictive of patients being offered and electing to undergo TKR. Conclusion The application of the model developed in our study would effectively reduce the proportion of nonsurgical referrals by 25%, while identifying the vast majority of surgical candidates (> 90%). Using patient-reported information, we can correctly predict the outcome of specialist consultation for TKR in 70% of cases. To reduce long waits for first consultation with a surgeon, it may be possible to use these items to educate and guide referring clinicians and patients to understand when specialist consultation is the next step in managing the patient with severe osteoarthritis of the knee. PMID:28234616

Predictors of Depression in Youth With Crohn Disease

PubMed Central

Clark, Jeffrey G.; Srinath, Arvind I.; Youk, Ada O.; Kirshner, Margaret A.; McCarthy, F. Nicole; Keljo, David J.; Bousvaros, Athos; DeMaso, David R.; Szigethy, Eva M.

2014-01-01

Objective The aim of the study was to determine whether infliximab use and other potential predictors are associated with decreased prevalence and severity of depression in pediatric patients with Crohn disease (CD). Methods A total of 550 (n = 550) youth ages 9 to 17 years with biopsy-confirmed CD were consecutively recruited as part of a multicenter randomized controlled trial. Out of the 550, 499 patients met study criteria and were included in the analysis. At recruitment, each subject and a parent completed the Children’s Depression Inventory (CDI). A child or parent CDI score ≥ 12 was used to denote clinically significant depressive symptoms (CSDS). Child and parent CDI scores were summed to form total CDI (CDIT). Infliximab use, demographic information, steroid use, laboratory values, and Pediatric Crohn’s Disease Activity Index (PCDAI) were collected as the potential predictors of depression. Univariate regression models were constructed to determine the relations among predictors, CSDS, and CDIT. Stepwise multivariate regression models were constructed to predict the relation between infliximab use and depression while controlling for other predictors of depression. Results Infliximab use was not associated with a decreased proportion of CSDS and CDIT after adjusting for multiple comparisons. CSDS and CDIT were positively associated with PCDAI, erythrocyte sedimentation rate, and steroid dose (P<0.01) and negatively associated with socioeconomic status (SES) (P<0.001). In multivariate models, PCDAI and SES were the strongest predictors of depression. Conclusions Disease activity and SES are significant predictors of depression in youth with Crohn disease. PMID:24343281

Phobic Anxiety and Plasma Levels of Global Oxidative Stress in Women

PubMed Central

Hagan, Kaitlin A.; Wu, Tianying; Rimm, Eric B.; Eliassen, A. Heather; Okereke, Olivia I.

2015-01-01

Background and Objectives Psychological distress has been hypothesized to be associated with adverse biologic states such as higher oxidative stress and inflammation. Yet, little is known about associations between a common form of distress – phobic anxiety – and global oxidative stress. Thus, we related phobic anxiety to plasma fluorescent oxidation products (FlOPs), a global oxidative stress marker. Methods We conducted a cross-sectional analysis among 1,325 women (aged 43-70 years) from the Nurses’ Health Study. Phobic anxiety was measured using the Crown-Crisp Index (CCI). Adjusted least-squares mean log-transformed FlOPs were calculated across phobic categories. Logistic regression models were used to calculate odds ratios (OR) comparing the highest CCI category (≥6 points) vs. lower scores, across FlOPs quartiles. Results No association was found between phobic anxiety categories and mean FlOP levels in multivariable adjusted linear models. Similarly, in multivariable logistic regression models there were no associations between FlOPs quartiles and likelihood of being in the highest phobic category. Comparing women in the highest vs. lowest FlOPs quartiles: FlOP_360: OR=0.68 (95% CI: 0.40-1.15); FlOP_320: OR=0.99 (95% CI: 0.61-1.61); FlOP_400: OR=0.92 (95% CI: 0.52, 1.63). Conclusions No cross-sectional association was found between phobic anxiety and a plasma measure of global oxidative stress in this sample of middle-aged and older women. PMID:26635425

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation.

PubMed

Fenske, Timothy S; Ahn, Kwang W; Graff, Tara M; DiGilio, Alyssa; Bashir, Qaiser; Kamble, Rammurti T; Ayala, Ernesto; Bacher, Ulrike; Brammer, Jonathan E; Cairo, Mitchell; Chen, Andy; Chen, Yi-Bin; Chhabra, Saurabh; D'Souza, Anita; Farooq, Umar; Freytes, Cesar; Ganguly, Siddhartha; Hertzberg, Mark; Inwards, David; Jaglowski, Samantha; Kharfan-Dabaja, Mohamed A; Lazarus, Hillard M; Nathan, Sunita; Pawarode, Attaphol; Perales, Miguel-Angel; Reddy, Nishitha; Seo, Sachiko; Sureda, Anna; Smith, Sonali M; Hamadani, Mehdi

2016-07-01

For diffuse large B-cell lymphoma (DLBCL) patients progressing after autologous haematopoietic cell transplantation (autoHCT), allogeneic HCT (alloHCT) is often considered, although limited information is available to guide patient selection. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 503 patients who underwent alloHCT after disease progression/relapse following a prior autoHCT. The 3-year probabilities of non-relapse mortality, progression/relapse, progression-free survival (PFS) and overall survival (OS) were 30, 38, 31 and 37% respectively. Factors associated with inferior PFS on multivariate analysis included Karnofsky performance status (KPS) <80, chemoresistance, autoHCT to alloHCT interval <1-year and myeloablative conditioning. Factors associated with worse OS on multivariate analysis included KPS<80, chemoresistance and myeloablative conditioning. Three adverse prognostic factors were used to construct a prognostic model for PFS, including KPS<80 (4 points), autoHCT to alloHCT interval <1-year (2 points) and chemoresistant disease at alloHCT (5 points). This CIBMTR prognostic model classified patients into four groups: low-risk (0 points), intermediate-risk (2-5 points), high-risk (6-9 points) or very high-risk (11 points), predicting 3-year PFS of 40, 32, 11 and 6%, respectively, with 3-year OS probabilities of 43, 39, 19 and 11% respectively. In conclusion, the CIBMTR prognostic model identifies a subgroup of DLBCL patients experiencing long-term survival with alloHCT after a failed prior autoHCT. © 2016 John Wiley & Sons Ltd.

Outcomes of Extremely Low Birth Weight Infants with Acidosis at Birth

PubMed Central

Randolph, David A.; Nolen, Tracy L.; Ambalavanan, Namasivayam; Carlo, Waldemar A.; Peralta-Carcelen, Myriam; Das, Abhik; Bell, Edward F.; Davis, Alexis S.; Laptook, Abbot R.; Stoll, Barbara J.; Shankaran, Seetha; Higgins, Rosemary D.

2014-01-01

OBJECTIVES To test the hypothesis that acidosis at birth is associated with the combined primary outcome of death or neurodevelopmental impairment (NDI) in extremely low birth weight (ELBW) infants, and to develop a predictive model of death/NDI exploring perinatal acidosis as a predictor variable. STUDY DESIGN The study population consisted of ELBW infants born between 2002-2007 at NICHD Neonatal Research Network hospitals. Infants with cord blood gas data and documentation of either mortality prior to discharge or 18-22 month neurodevelopmental outcomes were included. Multiple logistic regression analysis was used to determine the contribution of perinatal acidosis, defined as a cord blood gas with a pH<7 or base excess (BE)<-12, to death/NDI in ELBW infants. In addition, a multivariable model predicting death/NDI was developed. RESULTS 3979 patients were identified of whom 249 had a cord gas pH<7 or BE<-12 mEq/L. 2124 patients (53%) had the primary outcome of death/NDI. After adjustment for confounding variables, pH<7 and BE<-12 mEq/L were each significantly associated with death/NDI (OR=2.5[1.6,4.2]; and OR=1.5[1.1,2.0], respectively). However, inclusion of pH or BE did not improve the ability of the multivariable model to predict death/NDI. CONCLUSIONS Perinatal acidosis is significantly associated with death/NDI in ELBW infants. Perinatal acidosis is infrequent in ELBW infants, however, and other factors are more important in predicting death/NDI. PMID:24554564

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

Predicting Condom Use Using the Information-Motivation-Behavioral Skills (IMB) Model: A Multivariate Latent Growth Curve Analysis

PubMed Central

Senn, Theresa E.; Scott-Sheldon, Lori A. J.; Vanable, Peter A.; Carey, Michael P.

2011-01-01

Background The Information-Motivation-Behavioral Skills (IMB) model often guides sexual risk reduction programs even though no studies have examined covariation in the theory’s constructs in a dynamic fashion with longitudinal data. Purpose Using new developments in latent growth modeling, we explore how changes in information, motivation, and behavioral skills over 9 months relate to changes in condom use among STD clinic patients. Methods Participants (N = 1281, 50% female, 66% African American) completed measures of IMB constructs at three time points. We used parallel process latent growth modeling to examine associations among intercepts and slopes of IMB constructs. Results Initial levels of motivation, behavioral skills, and condom use were all positively associated, with behavioral skills partially mediating associations between motivation and condom use. Changes over time in behavioral skills positively related to changes in condom use. Conclusions Results support the key role of behavioral skills in sexual risk reduction, suggesting these skills should be targeted in HIV prevention interventions. PMID:21638196

A Log Logistic Survival Model Applied to Hypobaric Decompression Sickness

NASA Technical Reports Server (NTRS)

Conkin, Johnny

2001-01-01

Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars.

Determinants of Viraemia Copy-Years in People with HIV/AIDS Following Initiation of Antiretroviral Therapy

PubMed Central

Wright, Stephen T; Hoy, Jennifer; Mulhall, Brian; O’Connor, Catherine C; Petoumenos, Kathy; Read, Timothy; Smith, Don; Woolley, Ian; Boyd, Mark A

2014-01-01

Background Recent studies suggest higher cumulative HIV viraemia exposure measured as viraemia copy-years (VCY) is associated with increased all-cause mortality. The objectives of this study are (a) report the association between VCY and all-cause mortality, and (b) assess associations between common patient characteristics and VCY. Methods Analyses were based on patients recruited to the Australian HIV Observational Database (AHOD) who had received ≥ 24 weeks of antiretroviral therapy (ART). We established VCY after 1, 3, 5 and 10 years of ART by calculating the area under the plasma viral load time-series. We used survival methods to determine the association between high VCY and all-cause mortality. We used multivariable mixed-effect models to determine predictors of VCY. We compared a baseline information model with a time-updated model to evaluate discrimination of patients with high VCY. Results Of the 3021 AHOD participants that initiated ART, 2073(69%), 1667(55%), 1267(42%) and 638(21%) were eligible for analysis at 1, 3, 5, 10 years of ART respectively. Multivariable adjusted hazard ratio (HR) association between all-cause mortality and high VCY was statistically significant, HR 1.52(1.09, 2.13), p-value=0.01. Predicting high VCY after one-year of ART for a time-updated model compared to a baseline information only model, the area under the sensitivity/specificity curve (AUC) was 0.92 vs. 0.84; and at 10 years of ART, AUC: 0.87 vs. 0.61 respectively. Conclusion A high cumulative measure of viral load after initiating ART is associated with increased risk of all-cause mortality. Identifying patients with high VCY is improved by incorporating time-updated information. PMID:24463783

Pretreatment Nomogram to Predict the Risk of Acute Urinary Retention After I-125 Prostate Brachytherapy

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

Roeloffzen, Ellen M., E-mail: e.m.a.roeloffzen@umcutrecht.nl; Vulpen, Marco van; Battermann, Jan J.

Purpose: Acute urinary retention (AUR) after iodine-125 (I-125) prostate brachytherapy negatively influences long-term quality of life and therefore should be prevented. We aimed to develop a nomogram to preoperatively predict the risk of AUR. Methods: Using the preoperative data of 714 consecutive patients who underwent I-125 prostate brachytherapy between 2005 and 2008 at our department, we modeled the probability of AUR. Multivariate logistic regression analysis was used to assess the predictive ability of a set of pretreatment predictors and the additional value of a new risk factor (the extent of prostate protrusion into the bladder). The performance of the finalmore » model was assessed with calibration and discrimination measures. Results: Of the 714 patients, 57 patients (8.0%) developed AUR after implantation. Multivariate analysis showed that the combination of prostate volume, IPSS score, neoadjuvant hormonal treatment and the extent of prostate protrusion contribute to the prediction of AUR. The discriminative value (receiver operator characteristic area, ROC) of the basic model (including prostate volume, International Prostate Symptom Score, and neoadjuvant hormonal treatment) to predict the development of AUR was 0.70. The addition of prostate protrusion significantly increased the discriminative power of the model (ROC 0.82). Calibration of this final model was good. The nomogram showed that among patients with a low sum score (<18 points), the risk of AUR was only 0%-5%. However, in patients with a high sum score (>35 points), the risk of AUR was more than 20%. Conclusion: This nomogram is a useful tool for physicians to predict the risk of AUR after I-125 prostate brachytherapy. The nomogram can aid in individualized treatment decision-making and patient counseling.« less

Biological and Behavioral Risks for Incident Chlamydia trachomatis Infection in a Prospective Cohort

PubMed Central

Hwang, Loris Y.; Ma, Yifei; Moscicki, Anna-Barbara

2014-01-01

Objective To identify biological and behavioral risks for incident Chlamydia trachomatis among a prospective cohort of young women followed frequently. Methods Our cohort of 629 women from two outpatient sites was seen every 4 months (October 2000 through April 2012) for behavioral interviews and infection testing. C trachomatis was tested annually, and anytime patients reported symptoms or possible exposure using commercial nucleic acid amplification tests. Analyses excluded baseline prevalent C trachomatis infections. Risk factors for incident C trachomatis were assessed using Cox proportional hazards models. Significant risks (p<0.10) from bivariate models were entered in a multivariate model, adjusted for four covariates chosen a priori (age, race or ethnicity, condom use, study site). Backwards step-wise elimination produced a final parsimonious model retaining significant variables (p<0.05) and the four adjustment variables. Results The 629 women attended 9,594 total visits. Median follow-up time was 6.9 years (interquartile range 3.2-9.8), during which 97 (15%) women had incident C trachomatis . In the final multivariate model, incident C trachomatis was independently associated with HPV at the preceding visit (p<0.01), smoking (p=0.02), and weekly use of substances besides alcohol and marijuana (p<0.01) since prior visit. Among 207 women with available colpophotographs (1,742 visits), cervical ectopy was not a significant risk factor (p range=0.16-0.39 for ectopy as continuous and ordinal variables). Conclusion Novel risks for C trachomatis include preceding HPV, smoking, and substance use, which may reflect both biological and behavioral mechanisms of risk, such as immune modulation, higher-risk sexual networks, or both. Improved understanding of the biological bases for C trachomatis risk would inform our strategies for C trachomatis control. PMID:25437724

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

RAS mutations predict radiologic and pathologic response in patients treated with chemotherapy prior to resection of colorectal liver metastases

PubMed Central

Mise, Yoshihiro; Kopetz, Scott; Loyer, Evelyne M.; Andreou, Andreas; Cooper, Amanda B.; Kaur, Harmeet; Aloia, Thomas A.; Maru, Dipen M.; Vauthey, Jean-Nicolas

2014-01-01

Purpose RAS mutations have been reported to be a potential prognostic factor in patients with colorectal liver metastases (CLM). However, the impact of RAS mutations on response to chemotherapy remains unclear. We sought to determine the association between RAS mutations and response to preoperative chemotherapy and their impact on survival in patients undergoing curative resection of CLM. Methods RAS mutational status was assessed and its relation to morphologic response and pathologic response was investigated in 184 patients meeting inclusion criteria. Predictors of survival were assessed. The prognostic impact of RAS mutational status was then analyzed using two different multivariate models including either radiologic morphologic response (model 1) or pathologic response (model 2). Results Optimal morphologic response and major pathologic response were more common in patients with wild-type RAS (32.9% and 58.9%, respectively) than in patients with RAS mutations (10.5% and 36.8%; P =.006 and .015, respectively). Multivariate analysis confirmed that wild-type RAS was a strong predictor of optimal morphologic response (odds ratio [OR], 4.38; 95% CI, 1.45-13.2) and major pathologic response (OR,2.79; 95% CI, 1.29-6.04). RAS mutations were independently correlated with both overall survival and recurrence free-survival (hazard ratios, 3.25 and 2.02, respectively, in model 1, and 3.19 and 2.23, respectively, in model 2). Subanalysis revealed that RAS mutational status clearly stratified prognosis in patients with inadequate response to preoperative chemotherapy. Conclusion RAS mutational status can be used to complement the current prognostic indicators for patients undergoing curative resection of CLM after preoperative modern chemotherapy. PMID:25227306

Risk Factors for the Development of Heterotopic Ossification in Seriously Burned Adults: A NIDRR Burn Model System Database Analysis

PubMed Central

Levi, Benjamin; Jayakumar, Prakash; Giladi, Avi; Jupiter, Jesse B.; Ring, David C.; Kowalske, Karen; Gibran, Nicole S.; Herndon, David; Schneider, Jeffrey C.; Ryan, Colleen M.

2015-01-01

Purpose Heterotopic ossification (HO) is a debilitating complication of burn injury; however, incidence and risk factors are poorly understood. In this study we utilize a multicenter database of adults with burn injuries to identify and analyze clinical factors that predict HO formation. Methods Data from 6 high-volume burn centers, in the Burn Injury Model System Database, were analyzed. Univariate logistic regression models were used for model selection. Cluster-adjusted multivariate logistic regression was then used to evaluate the relationship between clinical and demographic data and the development of HO. Results Of 2,979 patients in the database with information on HO that addressed risk factors for development of HO, 98 (3.5%) developed HO. Of these 98 patients, 97 had arm burns, and 96 had arm grafts. Controlling for age and sex in a multivariate model, patients with >30% total body surface area (TBSA) burn had 11.5x higher odds of developing HO (p<0.001), and those with arm burns that required skin grafting had 96.4x higher odds of developing HO (p=0.04). For each additional time a patient went to the operating room, odds of HO increased 30% (OR 1.32, p<0.001), and each additional ventilator day increase odds 3.5% (OR 1.035, p<0.001). Joint contracture, inhalation injury, and bone exposure did not significantly increase odds of HO. Conclusion Risk factors for HO development include >30% TBSA burn, arm burns, arm grafts, ventilator days, and number of trips to the operating room. Future studies can use these results to identify highest-risk patients to guide deployment of prophylactic and experimental treatments. PMID:26496115

Pinhole occulter experiment

NASA Technical Reports Server (NTRS)

Ring, Jeff; Pflug, John

1987-01-01

Viewgraphs and charts from a briefing summarize the accomplishments, results, conclusions, and recommendations of a feasibility study using the Pinhole Occulter Facility (POF). Accomplishments for 1986 include: (1) improved IPS Gimbal Model; (2) improved Crew Motion Disturbance Model; (3) use of existing shuttle on-orbit simulation to study the effects of orbiter attitude deadband size on POF performance; (4) increased understanding of maximum performance expected from current actuator/sensor set; (5) use of TREETOPS nonlinear time domain program to obtain system dynamics describing the complex multibody flexible structures; (6) use of HONEY-X design tool to design and evaluate multivariable compensator for stability, robustness, and performance; (7) application of state-of-the-art compensator design methodology Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR); and (8) examination of tolerance required on knowledge of the POF boom flexible mode frequencies to insure stability, using structure uncertainty analysis.

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Multivariate meta-analysis: potential and promise.

PubMed

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

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Various forms of indexing HDMR for modelling multivariate classification problems

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

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Social Disparities in Women’s Health Service Utilization in the United States: A Population-Based Analysis

PubMed Central

Johnson, Timothy; Dalton, Vanessa

2013-01-01

Purpose Poor and disparate reproductive health outcomes in the United States may be related to inadequate and differential women’s health care utilization. We investigated trends in and determinants of adult U.S. women’s health service use, 2006–2010. Methods We analyzed population data from 7,897 women aged 25–44yrs in the National Survey of Family Growth from 2006 to 2010 using multivariable logistic regression. Results Women’s health service utilization in the past year was reported by 74% of the sample. Among non-fertile, sexually active women, 47% used contraceptive services; fewer used pregnancy (21%) and STI (14%) services. In multivariable models, the odds of service use were greater among older, poor, unemployed women and women with less educational attainment than younger and socioeconomically advantaged women. Black women had greater odds of using pregnancy, STI and gynecological exam services than White women (ORs 1.4–1.6). Lack of insurance was associated with service use in all models (ORs 0.4–0.8). Conclusion While age-related differences in women’s health service use may reflect fertility transitions, social disparities mirror reproductive inequalities among U.S. women. Research on women’s health service utilization and outcomes across the reproductive life course and forthcoming sociopolitical climates is needed. PMID:24332620

Racial and Ethnic Differences in Patient Navigation: Results from the Patient Navigation Research Program

PubMed Central

Ko, Naomi Y; Snyder, Frederick R; Raich, Peter C; Paskett, Electra D.; Dudley, Donald; Lee, Ji-Hyun; Levine, Paul H.; Freund, Karen M

2016-01-01

Purpose Patient navigation was developed to address barriers to timely care and reduce cancer disparities. This study explores navigation and racial and ethnic differences in time to diagnostic resolution of a cancer screening abnormality. Patients and Methods We conducted an analysis of the multi-site Patient Navigation Research Program. Participants with an abnormal cancer screening test were allocated to either navigation or control. Unadjusted median time to resolution was calculated for each racial and ethnic group by navigation and control. Multivariable Cox proportional hazards models were fit, adjusting for sex, age, cancer abnormality type, and health insurance, stratifying by center of care. Results Among a sample of 7,514 participants, 29% were Non-Hispanic White, 43% Hispanic, and 28% Black. In the control group Blacks had a longer median time to diagnostic resolution (108 days) than Non-Hispanic Whites (65 days) or Hispanics (68 days) (p< .0001). In the navigated groups, Blacks had a reduction in median time to diagnostic resolution (97 days) (p <.0001). In the multivariable models, among controls, Black race was associated with increased delay to diagnostic resolution (HR=0.77; 95% CI: 0.69, 0.84) compared to the Non-Hispanic Whites, which was reduced in the navigated arm (HR=0.85; 95% CI: 0.77, 0.94). Conclusion Patient navigation had its greatest impact for Black patients who had the greatest delays in care. PMID:27227342

Gendered Social Learning, Nonfamily Institutions, and Attitudes About Recourse After Partner Violence

PubMed Central

Krause, Kathleen H.

2015-01-01

Objective To provide the first study in Vietnam of how gendered social learning about violence and exposure to non-family institutions influence women’s attitudes about a wife’s recourse after physical IPV. Method A probability sample of 532 married women, ages 18–50 years, was surveyed in July–August, 2012 in Mỹ Hào district. We fit a multivariate linear regression model to estimate correlates of favoring recourse in six situations using a validated attitudinal scale. We split attitudes towards recourse into three subscales (disfavor silence, favor informal recourse, favor formal recourse) and fit one multivariate ordinal logistic regression model for each behavior to estimate correlates of favoring recourse. Results On average, women favored recourse in 2.8 situations. Women who were older and had witnessed physical IPV in childhood had less favorable attitudes about recourse. Women who were hit as children, had completed more schooling, worked outside agriculture, and had sought recourse after IPV had more favorable attitudes about recourse. Conclusions Normative change among women may require efforts to curb family violence, counsel those exposed to violence in childhood, and enhance women’s opportunities for higher schooling and non-agricultural wage work. The state and organizations working on IPV might overcome pockets of unfavorable public opinion by enforcing accountability for IPV rather than seeking to alter ideas about recourse among women. PMID:28392967

Simultaneous Determination of Metamizole, Thiamin and Pyridoxin Using UV-Spectroscopy in Combination with Multivariate Calibration

PubMed Central

Chotimah, Chusnul; Sudjadi; Riyanto, Sugeng; Rohman, Abdul

2015-01-01

Purpose: Analysis of drugs in multicomponent system officially is carried out using chromatographic technique, however, this technique is too laborious and involving sophisticated instrument. Therefore, UV-VIS spectrophotometry coupled with multivariate calibration of partial least square (PLS) for quantitative analysis of metamizole, thiamin and pyridoxin is developed in the presence of cyanocobalamine without any separation step. Methods: The calibration and validation samples are prepared. The calibration model is prepared by developing a series of sample mixture consisting these drugs in certain proportion. Cross validation of calibration sample using leave one out technique is used to identify the smaller set of components that provide the greatest predictive ability. The evaluation of calibration model was based on the coefficient of determination (R2) and root mean square error of calibration (RMSEC). Results: The results showed that the coefficient of determination (R2) for the relationship between actual values and predicted values for all studied drugs was higher than 0.99 indicating good accuracy. The RMSEC values obtained were relatively low, indicating good precision. The accuracy and presision results of developed method showed no significant difference compared to those obtained by official method of HPLC. Conclusion: The developed method (UV-VIS spectrophotometry in combination with PLS) was succesfully used for analysis of metamizole, thiamin and pyridoxin in tablet dosage form. PMID:26819934

Academic performance, educational aspiration and birth outcomes among adolescent mothers: a national longitudinal study

PubMed Central

2014-01-01

Background Maternal educational attainment has been associated with birth outcomes among adult mothers. However, limited research explores whether academic performance and educational aspiration influence birth outcomes among adolescent mothers. Methods Data from Waves I and IV of the National Longitudinal Study of Adolescent Health (Add Health) were used. Adolescent girls whose first pregnancy occurred after Wave I, during their adolescence, and ended with a singleton live birth were included. Adolescents’ grade point average (GPA), experience of ever skipping a grade and ever repeating a grade, and their aspiration to attend college were examined as predictors of birth outcomes (birthweight and gestational age; n = 763). Univariate statistics, bivariate analyses and multivariable models were run stratified on race using survey procedures. Results Among Black adolescents, those who ever skipped a grade had higher offspring’s birthweight. Among non-Black adolescents, ever skipping a grade and higher educational aspiration were associated with higher offspring’s birthweight; ever skipping a grade was also associated with higher gestational age. GPA was not statistically significantly associated with either birth outcome. The addition of smoking during pregnancy and prenatal care visit into the multivariable models did not change these associations. Conclusions Some indicators of higher academic performance and aspiration are associated with better birth outcomes among adolescents. Investing in improving educational opportunities may improve birth outcomes among teenage mothers. PMID:24422664

Determinants of Adiponectin Levels in Patients with Chronic Systolic Heart Failure

PubMed Central

Biolo, Andreia; Shibata, Rei; Ouchi, Noriyuki; Kihara, Shinji; Sonoda, Mina; Walsh, Kenneth; Sam, Flora

2010-01-01

Adiponectin, an adipocytokine, is secreted by adipocytes and mediates anti-hypertrophic and anti-inflammatory effects in the heart. Plasma concentrations of adiponectin are decreased in obesity, insulin resistance and obesity-associated conditions such as hypertension and coronary heart disease. However, a paradoxical increase in adiponectin levels is observed in human systolic heart failure (HF). We sought to investigate the determinants of adiponectin levels in patients with chronic systolic HF. Total adiponectin levels were measured in 99 patients with stable HF and left ventricular (LV) ejection fraction (EF) <40%. Determinants of adiponectin levels by univariate analysis were included in a multivariate linear regression model. At baseline patients were 62% black, 63% male, mean age of 60±13 years, LVEF of 21±9% and a body mass index (BMI) of 30.6±6.7kg/m2. Mean adiponectin levels were 15.8±15µg/ml. Beta-blocker use, BMI, and blood urea nitrogen (BUN) were significant determinants of adiponectin levels by multivariate analysis. LV mass, structure, and LVEF were not related to adiponectin levels by multivariate analysis. Interestingly, the effect of beta-blocker therapy was most marked in non-obese patients with BMI < 30kg/m2. In conclusion, in chronic systolic HF patients, beta-blocker therapy is correlated with lower adiponectin levels, especially in non-obese patients. This relation should be taken into account when studying the complex role of adiponectin in chronic systolic HF. PMID:20381668

Analysis of risk factors for central venous port failure in cancer patients

PubMed Central

Hsieh, Ching-Chuan; Weng, Hsu-Huei; Huang, Wen-Shih; Wang, Wen-Ke; Kao, Chiung-Lun; Lu, Ming-Shian; Wang, Chia-Siu

2009-01-01

AIM: To analyze the risk factors for central port failure in cancer patients administered chemotherapy, using univariate and multivariate analyses. METHODS: A total of 1348 totally implantable venous access devices (TIVADs) were implanted into 1280 cancer patients in this cohort study. A Cox proportional hazard model was applied to analyze risk factors for failure of TIVADs. Log-rank test was used to compare actuarial survival rates. Infection, thrombosis, and surgical complication rates (χ2 test or Fisher’s exact test) were compared in relation to the risk factors. RESULTS: Increasing age, male gender and open-ended catheter use were significant risk factors reducing survival of TIVADs as determined by univariate and multivariate analyses. Hematogenous malignancy decreased the survival time of TIVADs; this reduction was not statistically significant by univariate analysis [hazard ratio (HR) = 1.336, 95% CI: 0.966-1.849, P = 0.080)]. However, it became a significant risk factor by multivariate analysis (HR = 1.499, 95% CI: 1.079-2.083, P = 0.016) when correlated with variables of age, sex and catheter type. Close-ended (Groshong) catheters had a lower thrombosis rate than open-ended catheters (2.5% vs 5%, P = 0.015). Hematogenous malignancy had higher infection rates than solid malignancy (10.5% vs 2.5%, P < 0.001). CONCLUSION: Increasing age, male gender, open-ended catheters and hematogenous malignancy were risk factors for TIVAD failure. Close-ended catheters had lower thrombosis rates and hematogenous malignancy had higher infection rates. PMID:19787834

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

Background The primary cell wall of fruits and vegetables is a structure mainly composed of polysaccharides (pectins, hemicelluloses, cellulose). Polysaccharides are assembled into a network and linked together. It is thought that the percentage of components and of plant cell wall has an important influence on mechanical properties of fruits and vegetables. Results In this study the Raman microspectroscopy technique was introduced to the visualization of the distribution of polysaccharides in cell wall of fruit. The methodology of the sample preparation, the measurement using Raman microscope and multivariate image analysis are discussed. Single band imaging (for preliminary analysis) and multivariate image analysis methods (principal component analysis and multivariate curve resolution) were used for the identification and localization of the components in the primary cell wall. Conclusions Raman microspectroscopy supported by multivariate image analysis methods is useful in distinguishing cellulose and pectins in the cell wall in tomatoes. It presents how the localization of biopolymers was possible with minimally prepared samples. PMID:24917885

A new subgrid-scale representation of hydrometeor fields using a multivariate PDF

DOE PAGES

Griffin, Brian M.; Larson, Vincent E.

2016-06-03

The subgrid-scale representation of hydrometeor fields is important for calculating microphysical process rates. In order to represent subgrid-scale variability, the Cloud Layers Unified By Binormals (CLUBB) parameterization uses a multivariate probability density function (PDF). In addition to vertical velocity, temperature, and moisture fields, the PDF includes hydrometeor fields. Previously, hydrometeor fields were assumed to follow a multivariate single lognormal distribution. Now, in order to better represent the distribution of hydrometeors, two new multivariate PDFs are formulated and introduced.The new PDFs represent hydrometeors using either a delta-lognormal or a delta-double-lognormal shape. The two new PDF distributions, plus the previous single lognormalmore » shape, are compared to histograms of data taken from large-eddy simulations (LESs) of a precipitating cumulus case, a drizzling stratocumulus case, and a deep convective case. In conclusion, the warm microphysical process rates produced by the different hydrometeor PDFs are compared to the same process rates produced by the LES.« less

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Multivariate Bayesian modeling of known and unknown causes of events--an application to biosurveillance.

PubMed

Shen, Yanna; Cooper, Gregory F

2012-09-01

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Predicting Intention Perform Breast Self-Examination: Application of the Theory of Reasoned Action

PubMed Central

Dewi, Triana Kesuma; Zein, Rizqy Amelia

2017-01-01

Objective: The present study aimed to examine the applicability of the theory of reasoned action to explain intention to perform breast self-examination (BSE). Methods: A questionnaire was constructed to collect data. The hypothesis was tested in two steps. First, to assess the strength of the correlation among the constructs of theory of reasoned action (TRA), Pearson’s product moment correlations were applied. Second, multivariate relationships among the constructs were examined by performing hierarchical multiple linear regression analysis. Result: The findings supported the TRA model, explaining 45.8% of the variance in the students’ BSE intention, which was significantly correlated with attitude (r = 0.609, p = 0.000) and subjective norms (r = 0.420, p =0 .000). Conclusion: TRA could be a suitable model to predict BSE intentions. Participants who believed that doing BSE regularly is beneficial for early diagnosis of breast cancer and also believed that their significant referents think that doing BSE would significantly detect breast cancer earlier, were more likely to intend to perform BSE regularly. Therefore, the research findings supported the conclusion that promoting the importance of BSE at the community/social level would enhance individuals to perform BSE routinely. PMID:29172263

Predicting Intention Perform Breast Self-Examination: Application of the Theory of Reasoned Action

PubMed

Dewi, Triana Kesuma; Zein, Rizqy Amelia

2017-11-26

Objective: The present study aimed to examine the applicability of the theory of reasoned action to explain intention to perform breast self-examination (BSE). Methods: A questionnaire was constructed to collect data. The hypothesis was tested in two steps. First, to assess the strength of the correlation among the constructs of theory of reasoned action (TRA), Pearson’s product moment correlations were applied. Second, multivariate relationships among the constructs were examined by performing hierarchical multiple linear regression analysis. Result: The findings supported the TRA model, explaining 45.8% of the variance in the students’ BSE intention, which was significantly correlated with attitude (r = 0.609, p = 0.000) and subjective norms (r = 0.420, p =0 .000). Conclusion: TRA could be a suitable model to predict BSE intentions . Participants who believed that doing BSE regularly is beneficial for early diagnosis of breast cancer and also believed that their significant referents think that doing BSE would significantly detect breast cancer earlier, were more likely to intend to perform BSE regularly. Therefore, the research findings supported the conclusion that promoting the importance of BSE at the community/social level would enhance individuals to perform BSE routinely. Creative Commons Attribution License

An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model

ERIC Educational Resources Information Center

Sengul, Sare; Yildiz, Sevda Goktepe

2016-01-01

The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Possible Protective Effect of Hydroxychloroquine on Retarding the Occurrence of Integument Damage in Lupus: Data from LUMINA, a Multiethnic Cohort

PubMed Central

Pons-Estel, Guillermo J.; Alarcón, Graciela S.; González, Luis A.; Zhang, Jie; Vilá, Luis M.; Reveille, John D.; McGwin, Gerald

2010-01-01

Objective To determine the features predictive of time-to-integument damage in patients with systemic lupus erythematosus (SLE) from a multiethnic cohort (LUMINA). Methods SLE LUMINA patients (n=580), age ≥16 years, disease duration ≤5 years at baseline (T0), of African American, Hispanic and Caucasian ethnicity were studied. Integument damage was defined per the SLICC damage index (scarring alopecia, extensive skin scarring and skin ulcers lasting at least six months); factors associated with time-to-its occurrence were examined by Cox proportional univariable and multivariable (main model) hazards regression analyses. Two alternative models were also examined; in model 1 all patients, regardless of when integument damage occurred (n=94), were included; in model 2 a time-varying approach (GEE) was employed. Results Thirty-nine (6.7%) of 580 patients developed integument damage over a mean (SD) total disease duration of 5.9 (3.7) years and were included in the main multivariable regression model. After adjusting for discoid rash, nailfold infarcts, photosensitivity and Raynaud’s phenomenon (significant in the univariable analyses), disease activity over time [Hazard ratio (HR)=1.17; 95% Confidence interval (CI) 1.09–1.26)] was associated with a shorter time-to-integument damage whereas hydroxychloroquine use (HR=0.23, 95% CI 0.12–0.47) and Texan-Hispanic (HR=0.35; 95% CI 0.14–0.87) and Caucasian ethnicities (HR=0.37; 95% CI 0.14–0.99) were associated with a longer time. Results of the alternative models were consistent with those of the main model albeit in model 2 the association with hydroxychloroquine was not significant. Conclusions Our data indicate that hydroxychloroquine use is possibly associated with a delay in integument damage development in patients with SLE. PMID:20391486

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

PubMed Central

Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

2015-01-01

The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

The relationship between self-reported history of endodontic therapy and coronary heart disease in the Atherosclerosis Risk in Communities Study

PubMed Central

Caplan, Daniel J.; Pankow, James S.; Cai, Jianwen; Offenbacher, Steven; Beck, James D.

2009-01-01

Background Results from numerous studies have suggested links between periodontal disease and coronary heart disease (CHD), but endodontic disease has not been studied extensively in this regard. Methods The authors evaluated the relationship between self-reported history of endodontic therapy (ET) and prevalent CHD in the Atherosclerosis Risk in Communities (ARIC) Study, aprospective epidemiologic study sponsored by the National Heart, Lung, and Blood Institute. The authors used multivariable logistic regres-sionto analyze data obtained from oral health questionnaires, medical evaluations and clinical dental examinations. Results Of 6,651 participants analyzed, 50.4 percent reported never having had ET; 21.5 percent reported having had ET one time; and 28.0 percent reported having had ET two or more times. Final multivariable regression models indicated that among participants with 25 or more teeth, those reporting having had ET two or more times had 1.62 (95 percent confidence interval [CI], 1.04–2.53) times the odds of prevalent CHD compared with those reporting never having had ET. Among participants with 24 or fewer teeth, no significant differences in CHD prevalence were observed among groups regardless of their history of ET. Conclusions Among participants with 25 or more teeth, those with a greater self-reported history of ET were more likely to have CHD than were those reporting no history of ET. Clinical Implications More accurate epidemiologic quantification of endodontic infection and inflammation is required before definitive conclusions can be made about potential relationships between endodontic disease and CHD. PMID:19654253

Practice Characteristics Associated with Patient-Specific Receipt of Dental Diagnostic Radiographs

PubMed Central

Gilbert, Gregg H; Weems, Richard A; Litaker, Mark S; Shelton, Brent J

2006-01-01

Objective To quantify the role of practice characteristics in patient-specific receipt of dental diagnostic radiographic services. Data Source/Study Setting Florida Dental Care Study (FDCS). Study Design The FDCS was a 48-month prospective observational cohort study of community-dwelling adults. Participants' dentists were asked to complete a questionnaire about their practice characteristics. Data Collection/Extraction Methods In-person interviews and clinical examinations were conducted at baseline, 24, and 48 months, with 6-monthly telephone interviews in between. A single multivariate (four radiographic service outcomes) multivariable (multiple explanatory covariates) logistic regression was used to model service receipts. Principal Findings These practice characteristics were significantly associated with patient-specific receipt of radiographic services: number of different practices attended during follow-up; dentist's rating of how busy the practice was; typical waiting time for a new patient examination; practice size; percentage of patients that the dentist reported as interested in details about the condition of their mouths; percentage of African American patients in the practice; percentage of patients in the practice who do not have dental insurance; and dentist's agreement with a statement regarding whether patients should be dismissed from the practice. Effects had differential magnitudes and directions of effect, depending upon radiograph type. Conclusions Practice characteristics were significantly associated with patient-specific receipt of services. These effects were independent of patient-specific disease level and patient-specific sociodemographic characteristics, suggesting that practitioners do influence receipt of these diagnostic services. These findings are consistent with the conclusion that practitioners act in response to a mix of patients' interests, economic self-interests, and their own treatment preferences. PMID:16987308

Prediction equations of forced oscillation technique: the insidious role of collinearity.

PubMed

Narchi, Hassib; AlBlooshi, Afaf

2018-03-27

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Determining the Relationship Between Moral Waivers and Marine Corps Unsuitability Attrition

DTIC Science & Technology

2008-03-01

observed characteristics. However, econometric research indicates that the magnitude of interaction effects estimated via probit or logit models may...1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service separations. 15. NUMBER OF...files from fiscal years 1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

PubMed

Marro, M; Nieva, C; Sanz-Pamplona, R; Sierra, A

2014-09-01

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy. Copyright © 2014 Elsevier B.V. All rights reserved.

Coffee consumption modifies risk of estrogen-receptor negative breast cancer

PubMed Central

2011-01-01

Introduction Breast cancer is a complex disease and may be sub-divided into hormone-responsive (estrogen receptor (ER) positive) and non-hormone-responsive subtypes (ER-negative). Some evidence suggests that heterogeneity exists in the associations between coffee consumption and breast cancer risk, according to different estrogen receptor subtypes. We assessed the association between coffee consumption and postmenopausal breast cancer risk in a large population-based study (2,818 cases and 3,111 controls), overall, and stratified by ER tumour subtypes. Methods Odds ratios (OR) and corresponding 95% confidence intervals (CI) were estimated using the multivariate logistic regression models fitted to examine breast cancer risk in a stratified case-control analysis. Heterogeneity among ER subtypes was evaluated in a case-only analysis, by fitting binary logistic regression models, treating ER status as a dependent variable, with coffee consumption included as a covariate. Results In the Swedish study, coffee consumption was associated with a modest decrease in overall breast cancer risk in the age-adjusted model (OR> 5 cups/day compared to OR≤ 1 cup/day: 0.80, 95% CI: 0.64, 0.99, P trend = 0.028). In the stratified case-control analyses, a significant reduction in the risk of ER-negative breast cancer was observed in heavy coffee drinkers (OR> 5 cups/day compared to OR≤ 1 cup/day : 0.43, 95% CI: 0.25, 0.72, P trend = 0.0003) in a multivariate-adjusted model. The breast cancer risk reduction associated with higher coffee consumption was significantly higher for ER-negative compared to ER-positive tumours (P heterogeneity (age-adjusted) = 0.004). Conclusions A high daily intake of coffee was found to be associated with a statistically significant decrease in ER-negative breast cancer among postmenopausal women. PMID:21569535

Uric acid and endothelial function in elderly community-dwelling subjects.

PubMed

Ticinesi, Andrea; Lauretani, Fulvio; Ceda, Gian Paolo; Ruggiero, Carmelinda; Ferrucci, Luigi; Aloe, Rosalia; Larsson, Anders; Cederholm, Tommy; Lind, Lars; Meschi, Tiziana; Maggio, Marcello

2017-03-01

The role of serum uric acid (SUA), an inflammatory agent and potential mediator of cardiovascular diseases, in endothelial function (EF) has been tested only in middle-aged subjects affected by specific diseases. Our aim was to assess the relationship between SUA and measures of EF in a cohort of elderly community-dwellers. This study involved 424 males and 426 females aged 70years from the Prospective Study of the Vasculature in Uppsala Seniors (PIVUS), having complete data on SUA and EF assessed by flow-mediated vasodilation (FMD) and by intra-arterial infusion of acetylcholine (endothelium-dependent vasodilation, EDV) and sodium nitroprusside (endothelium-independent vasodilation, EIDV). Univariate and multivariate regression models obtained by backward selection from initial fully-adjusted models were built to assess the relationship between SUA and measures of EF in both genders. Cardiovascular risk factors, serum hormonal and metabolic mediators, and body composition were considered as potential confounders. In the univariate model, SUA was inversely associated in both genders with log(EDV) (β±SE males -0.39±0.17, p=0.03; females -0.57±0.19, p=0.003) and log(EIDV) (males -0.23±0.12, p=0.05; females -0.49±0.15, p=0.002), but not with log(FMD). After adjustment for BMI, only the association between SUA and log(EIDV) in females persisted, though attenuated (-0.32±0.16, p=0.049), and was no longer significant in the fully-adjusted multivariate model including waist/hip ratio. In conclusion, in older subjects, especially women, SUA is associated with EF not independently of a list of confounders including BMI and trunk fat mass, suggesting a role as surrogate metabolic marker rather than an active player in EF. Copyright © 2017 Elsevier Inc. All rights reserved.

Uric acid and endothelial function in elderly community-dwelling subjects

PubMed Central

Ticinesi, Andrea; Lauretani, Fulvio; Ceda, Gian Paolo; Ruggiero, Carmelinda; Ferrucci, Luigi; Aloe, Rosalia; Larsson, Anders; Cederholm, Tommy; Lind, Lars; Meschi, Tiziana; Maggio, Marcello

2017-01-01

The role of serum uric acid (SUA), an inflammatory agent and potential mediator of cardiovascular diseases, in endothelial function (EF) has been tested only in middle-aged subjects affected by specific diseases. Our aim was to assess the relationship between SUA and measures of EF in a cohort of elderly community-dwellers. This study involved 424 males and 426 females aged 70 years from the Prospective Study of the Vasculature in Uppsala Seniors (PIVUS), having complete data on SUA and EF assessed by flow-mediated vasodilation (FMD) and by intra-arterial infusion of acetylcholine (endothelium-dependent vasodilation, EDV) and sodium nitroprusside (endothelium-independent vasodilation, EIDV). Univariate and multivariate regression models obtained by backward selection from initial fully-adjusted models were built to assess the relationship between SUA and measures of EF in both genders. Cardiovascular risk factors, serum hormonal and metabolic mediators, and body composition were considered as potential confounders. In the univariate model, SUA was inversely associated in both genders with log(EDV) (β ± SE males −0.39 ± 0.17, p = 0.03; females −0.57 ± 0.19, p = 0.003) and log(EIDV) (males −0.23 ± 0.12, p = 0.05; females −0.49 ± 0.15, p = 0.002), but not with log(FMD). After adjustment for BMI, only the association between SUA and log(EIDV) in females persisted, though attenuated (−0.32 ± 0.16, p = 0.049), and was no longer significant in the fully-adjusted multivariate model including waist/hip ratio. In conclusion, in older subjects, especially women, SUA is associated with EF not independently of a list of confounders including BMI and trunk fat mass, suggesting a role as surrogate metabolic marker rather than an active player in EF. PMID:28057563

DEPRESSION AND INCIDENT ALZHEIMER’S DISEASE: THE IMPACT OF DEPRESSION SEVERITY

PubMed Central

Gracia-García, Patricia; de-la-Cámara, Concepción; Santabárbara, Javier; Lopez-Anton, Raúl; Quintanilla, Miguel Angel; Ventura, Tirso; Marcos, Guillermo; Campayo, Antonio; Saz, Pedro; Lyketsos, Constantine; Lobo, Antonio

2014-01-01

Objective We test the hypothesis that clinically significant depression, severe depression in particular, increases the risk of Alzheimer’s Disease (AD). Design A longitudinal, three-wave epidemiological enquiry was implemented in a sample of individuals aged ≥55 years (n = 4,803) followed-up at 2.5 years and 4.5 years. Setting Population-based cohort drawn from the ZARADEMP Project, in Zaragoza, Spain. Participants Cognitively intact individuals at baseline (n = 3,864). Main outcome measures Depression was assessed by a standardized diagnostic interview (Geriatric Mental State, GMS-AGECAT). A panel of research psychiatrist diagnosed AD according to DSM-IV criteria. Fine and Gray multivariate regression model was used in the analysis, accounting for mortality. Results At baseline, clinically significant depression was diagnosed in 452 participants (11.7%). Among the depressed, 16.4% had severe depression. Seventy incident cases of AD were found at follow-up. Compared with non-depressed individuals, the incidence rate of AD was significantly higher in the depressed (incidence rate ratio, IRR = 1.91 (95%CI: 1.04–3.51) and particularly in the severely depressed (IRR = 3.59 (95%CI: 1.30–9.94). A consistent, significant association was observed between severe depression at baseline and incident AD in the multivariate model (hazard ratio, HR = 4.30 (95%CI: 1.39–13.33). Untreated depression was associated with incident AD in the unadjusted model, although in the final model this association was attenuated and non-significant. Conclusions Severe depression increases the risk of AD, even after controlling for the competing risk of death. This finding may stimulate studies about the effect of treating depression in relation to the risk of AD. PMID:23791538

Left Ventricular Structure and Risk of Cardiovascular Events: A Framingham Heart Study Cardiac Magnetic Resonance Study

PubMed Central

Tsao, Connie W; Gona, Philimon N; Salton, Carol J; Chuang, Michael L; Levy, Daniel; Manning, Warren J; O’Donnell, Christopher J

2015-01-01

Background Elevated left ventricular mass index (LVMI) and concentric left ventricular (LV) remodeling are related to adverse cardiovascular disease (CVD) events. The predictive utility of LV concentric remodeling and LV mass in the prediction of CVD events is not well characterized. Methods and Results Framingham Heart Study Offspring Cohort members without prevalent CVD (n=1715, 50% men, aged 65±9 years) underwent cardiovascular magnetic resonance for LVMI and geometry (2002–2006) and were prospectively followed for incident CVD (myocardial infarction, coronary insufficiency, heart failure, stroke) or CVD death. Over 13 808 person-years of follow-up (median 8.4, range 0.0 to 10.5 years), 85 CVD events occurred. In multivariable-adjusted proportional hazards regression models, each 10-g/m2 increment in LVMI and each 0.1 unit in relative wall thickness was associated with 33% and 59% increased risk for CVD, respectively (P=0.004 and P=0.009, respectively). The association between LV mass/LV end-diastolic volume and incident CVD was borderline significant (P=0.053). Multivariable-adjusted risk reclassification models showed a modest improvement in CVD risk prediction with the incorporation of cardiovascular magnetic resonance LVMI and measures of LV concentricity (C-statistic 0.71 [95% CI 0.65 to 0.78] for the model with traditional risk factors only, improved to 0.74 [95% CI 0.68 to 0.80] for the risk factor model additionally including LVMI and relative wall thickness). Conclusions Among adults free of prevalent CVD in the community, greater LVMI and LV concentric hypertrophy are associated with a marked increase in adverse incident CVD events. The potential benefit of aggressive primary prevention to modify LV mass and geometry in these adults requires further investigation. PMID:26374295

The Association between Triglyceride/High-Density Lipoprotein Cholesterol Ratio and All-Cause Mortality in Acute Coronary Syndrome after Coronary Revascularization

PubMed Central

Wan, Ke; Zhao, Jianxun; Huang, Hao; Zhang, Qing; Chen, Xi; Zeng, Zhi; Zhang, Li; Chen, Yucheng

2015-01-01

Aims High triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C) are cardiovascular risk factors. A positive correlation between elevated TG/HDL-C ratio and all-cause mortality and cardiovascular events exists in women. However, utility of TG to HDL-C ratio for prediction is unknown among acute coronary syndrome (ACS). Methods Fasting lipid profiles, detailed demographic data, and clinical data were obtained at baseline from 416 patients with ACS after coronary revascularization. Subjects were stratified into three levels of TG/HDL-C. We constructed multivariate Cox-proportional hazard models for all-cause mortality over a median follow-up of 3 years using log TG to HDL-C ratio as a predictor variable and analyzing traditional cardiovascular risk factors. We constructed a logistic regression model for major adverse cardiovascular events (MACEs) to prove that the TG/HDL-C ratio is a risk factor. Results The subject’s mean age was 64 ± 11 years; 54.5% were hypertensive, 21.8% diabetic, and 61.0% current or prior smokers. TG/HDL-C ratio ranged from 0.27 to 14.33. During the follow-up period, there were 43 deaths. In multivariate Cox models after adjusting for age, smoking, hypertension, diabetes, and severity of angiographic coronary disease, patients in the highest tertile of ACS had a 5.32-fold increased risk of mortality compared with the lowest tertile. After adjusting for conventional coronary heart disease risk factors by the logistic regression model, the TG/HDL-C ratio was associated with MACEs. Conclusion The TG to HDL-C ratio is a powerful independent predictor of all-cause mortality and is a risk factor of cardiovascular events. PMID:25880982

Evaluation and simplification of the occupational slip, trip and fall risk-assessment test

PubMed Central

NAKAMURA, Takehiro; OYAMA, Ichiro; FUJINO, Yoshihisa; KUBO, Tatsuhiko; KADOWAKI, Koji; KUNIMOTO, Masamizu; ODOI, Haruka; TABATA, Hidetoshi; MATSUDA, Shinya

2016-01-01

Objective: The purpose of this investigation is to evaluate the efficacy of the occupational slip, trip and fall (STF) risk assessment test developed by the Japan Industrial Safety and Health Association (JISHA). We further intended to simplify the test to improve efficiency. Methods: A previous cohort study was performed using 540 employees aged ≥50 years who took the JISHA’s STF risk assessment test. We conducted multivariate analysis using these previous results as baseline values and answers to questionnaire items or score on physical fitness tests as variables. The screening efficiency of each model was evaluated based on the obtained receiver operating characteristic (ROC) curve. Results: The area under the ROC obtained in multivariate analysis was 0.79 when using all items. Six of the 25 questionnaire items were selected for stepwise analysis, giving an area under the ROC curve of 0.77. Conclusion: Based on the results of follow-up performed one year after the initial examination, we successfully determined the usefulness of the STF risk assessment test. Administering a questionnaire alone is sufficient for screening subjects at risk of STF during the subsequent one-year period. PMID:27021057

Bicycle Use and Cyclist Safety Following Boston’s Bicycle Infrastructure Expansion, 2009–2012

PubMed Central

Angriman, Federico; Bellows, Alexandra L.; Taylor, Kathryn

2016-01-01

Objectives. To evaluate changes in bicycle use and cyclist safety in Boston, Massachusetts, following the rapid expansion of its bicycle infrastructure between 2007 and 2014. Methods. We measured bicycle lane mileage, a surrogate for bicycle infrastructure expansion, and quantified total estimated number of commuters. In addition, we calculated the number of reported bicycle accidents from 2009 to 2012. Bicycle accident and injury trends over time were assessed via generalized linear models. Multivariable logistic regression was used to examine factors associated with bicycle injuries. Results. Boston increased its total bicycle lane mileage from 0.034 miles in 2007 to 92.2 miles in 2014 (P < .001). The percentage of bicycle commuters increased from 0.9% in 2005 to 2.4% in 2014 (P = .002) and the total percentage of bicycle accidents involving injuries diminished significantly, from 82.7% in 2009 to 74.6% in 2012. The multivariable logistic regression analysis showed that for every 1-year increase in time from 2009 to 2012, there was a 14% reduction in the odds of being injured in an accident. Conclusions. The expansion of Boston’s bicycle infrastructure was associated with increases in both bicycle use and cyclist safety. PMID:27736203

Morphological and Hemodynamic Discriminators for Rupture Status in Posterior Communicating Artery Aneurysms

PubMed Central

Karmonik, Christof; Fang, Yibin; Xu, Jinyu; Yu, Ying; Cao, Wei; Liu, Jianmin; Huang, Qinghai

2016-01-01

Background and Purpose The conflicting findings of previous morphological and hemodynamic studies on intracranial aneurysm rupture may be caused by the relatively small sample sizes and the variation in location of the patient-specific aneurysm models. We aimed to determine the discriminators for aneurysm rupture status by focusing on only posterior communicating artery (PCoA) aneurysms. Materials and Methods In 129 PCoA aneurysms (85 ruptured, 44 unruptured), clinical, morphological and hemodynamic characteristics were compared between the ruptured and unruptured cases. Multivariate logistic regression analysis was performed to determine the discriminators for rupture status of PCoA aneurysms. Results While univariate analyses showed that the size of aneurysm dome, aspect ratio (AR), size ratio (SR), dome-to-neck ratio (DN), inflow angle (IA), normalized wall shear stress (NWSS) and percentage of low wall shear stress area (LSA) were significantly associated with PCoA aneurysm rupture status. With multivariate analyses, significance was only retained for higher IA (OR = 1.539, p < 0.001) and LSA (OR = 1.393, p = 0.041). Conclusions Hemodynamics and morphology were related to rupture status of intracranial aneurysms. Higher IA and LSA were identified as discriminators for rupture status of PCoA aneurysms. PMID:26910518

The High Prevalence of Incarceration History Among Black Men Who Have Sex With Men in the United States: Associations and Implications

PubMed Central

Magnus, Manya; Kuo, Irene; Wang, Lei; Liu, Ting-Yuan; Mayer, Kenneth H.

2014-01-01

Objectives. We examined lifetime incarceration history and its association with key characteristics among 1553 Black men who have sex with men (BMSM) recruited in 6 US cities. Methods. We conducted bivariate analyses of data collected from the HIV Prevention Trials Network 061 study from July 2009 through December 2011 to examine the relationship between incarceration history and demographic and psychosocial variables predating incarceration and multivariate logistic regression analyses to explore the associations between incarceration history and demographic and psychosocial variables found to be significant. We then used multivariate logistic regression models to explore the independent association between incarceration history and 6 outcome variables. Results. After adjusting for confounders, we found that increasing age, transgender identity, heterosexual or straight identity, history of childhood violence, and childhood sexual experience were significantly associated with incarceration history. A history of incarceration was also independently associated with any alcohol and drug use in the past 6 months. Conclusions. The findings highlight an elevated lifetime incarceration history among a geographically diverse sample of BMSM and the need to adequately assess the impact of incarceration among BMSM in the United States. PMID:24432948

Social support for women of reproductive age and its predictors: a population-based study

PubMed Central

2012-01-01

Background Social support is an exchange of resources between at least two individuals perceived by the provider or recipient to be intended to promote the health of the recipient. Social support is a major determinant of health. The objective of this study was to determine the perceived social support and its associated sociodemographic factors among women of reproductive age. Methods This was a population-based cross-sectional study with multistage random cluster sampling of 1359 women of reproductive age. Data were collected using questionnaires on sociodemographic factors and perceived social support (PRQ85-Part 2). The relationship between the dependent variable (perceived social support) and the independent variables (sociodemographic characteristics) was analyzed using the multivariable linear regression model. Results The mean score of social support was 134.3 ± 17.9. Women scored highest in the “worth” dimension and lowest in the “social integration” dimension. Multivariable linear regression analysis indicated that the variables of education, spouse’s occupation, Sufficiency of income for expenses and primary support source were significantly related to the perceived social support. Conclusion Sociodemographic factors affect social support and could be considered in planning interventions to improve social support for Iranian women. PMID:22988834

Pharmacy Student Attitudes and Willingness to Engage in Care with People Living with HIV/AIDS

PubMed Central

Furtek, Kari J.; Malladi, Ruthvik; Ng, Eric; Zhou, Maria

2016-01-01

Objective. To describe the extent to which pharmacy students hold negative attitudes toward people living with HIV/AIDS (PLWHA) and to determine whether background variables, student knowledge, and professional attitudes may affect willingness to care for PLWHA. Methods. An online survey tool was developed and administered to 150 pharmacy students in their third professional year. Descriptive and stepwise multivariate regressions were performed. Results. While descriptive results showed a majority of respondents had favorable professional attitudes towards caring for PLWHA, most pharmacy students expressed discomfort with specific attitudes about being in close physical contact and receiving selected services from PLWHA. Multivariate results revealed that: (1) being a minority predicted greater knowledge; (2) having received prior HIV instruction and greater HIV knowledge predicted more positive professional attitudes caring for PLWHA; (3) being more socially liberal, having more positive professional attitudes caring for PLWHA, and having greater empathy towards PLWHA predicted student willingness to provide services. Conclusion. Future educational interventions specifically targeted toward socially conservative whites may impact greater student willingness to care for PLWHA. Additional research should also explore the generalizability of the present findings and modeling to pharmacy students in other regions of the country. PMID:27170816

Quality of Acute Care for Patients with Urinary Stones in the United States

PubMed Central

Scales, Charles D.; Bergman, Jonathan; Carter, Stacey; Jack, Gregory; Saigal, Christopher S.; Litwin, Mark S.

2015-01-01

Objective To describe guideline adherence for patients with suspected upper tract stones. Methods We performed a cross-sectional analysis of visits recorded by the National Hospital Ambulatory Medical Care Survey (ED component) in 2007–2010 (most recent data). We assessed adherence to clinical guidelines for diagnostic laboratory testing, imaging, and pharmacologic therapy. Multivariable regression models controlled for important covariates. Results An estimated 4,956,444 ED visits for patients with suspected kidney stones occurred during the study period. Guideline adherence was highest for diagnostic imaging, with 3,122,229 (63%) visits providing optimal imaging. Complete guideline-based laboratory testing occurred in only 2 of every 5 visits. Pharmacologic therapy to facilitate stone passage was prescribed during only 17% of eligible visits. In multivariable analysis of guideline adherence, we found little variation by patient, provider or facility characteristics. Conclusions Guideline-recommended care was absent from a substantial proportion of acute care visits for patients with suspected kidney stones. These failures of care delivery likely increase costs and temporary disability. Targeted interventions to improve guideline adherence should be designed and evaluated to improve care for patients with symptomatic kidney stones. PMID:26335495

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

Sole, Claudio V., E-mail: csole@iram.cl; School of Medicine, Complutense University, Madrid; Calvo, Felipe A.

Purpose: To assess long-term outcomes and toxicity of intraoperative electron-beam radiation therapy (IOERT) in the management of pediatric patients with Ewing sarcomas (EWS) and rhabdomyosarcomas (RMS). Methods and Materials: Seventy-one sarcoma (EWS n=37, 52%; RMS n=34, 48%) patients underwent IOERT for primary (n=46, 65%) or locally recurrent sarcomas (n=25, 35%) from May 1983 to November 2012. Local control (LC), overall survival (OS), and disease-free survival were estimated using Kaplan-Meier methods. For survival outcomes, potential associations were assessed in univariate and multivariate analyses using the Cox proportional hazards model. Results: After a median follow-up of 72 months (range, 4-310 months), 10-year LC, disease-freemore » survival, and OS was 74%, 57%, and 68%, respectively. In multivariate analysis after adjustment for other covariates, disease status (P=.04 and P=.05) and R1 margin status (P<.01 and P=.04) remained significantly associated with LC and OS. Nine patients (13%) reported severe chronic toxicity events (all grade 3). Conclusions: A multimodal IOERT-containing approach is a well-tolerated component of treatment for pediatric EWS and RMS patients, allowing reduction or substitution of external beam radiation exposure while maintaining high local control rates.« less

Predictive factors of clinical response in steroid-refractory ulcerative colitis treated with granulocyte-monocyte apheresis

PubMed Central

D'Ovidio, Valeria; Meo, Donatella; Viscido, Angelo; Bresci, Giampaolo; Vernia, Piero; Caprilli, Renzo

2011-01-01

AIM: To identify factors predicting the clinical response of ulcerative colitis patients to granulocyte-monocyte apheresis (GMA). METHODS: Sixty-nine ulcerative colitis patients (39 F, 30 M) dependent upon/refractory to steroids were treated with GMA. Steroid dependency, clinical activity index (CAI), C reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), values at baseline, use of immunosuppressant, duration of disease, and age and extent of disease were considered for statistical analysis as predictive factors of clinical response. Univariate and multivariate logistic regression models were used. RESULTS: In the univariate analysis, CAI (P = 0.039) and ESR (P = 0.017) levels at baseline were singled out as predictive of clinical remission. In the multivariate analysis steroid dependency [Odds ratio (OR) = 0.390, 95% Confidence interval (CI): 0.176-0.865, Wald 5.361, P = 0.0160] and low CAI levels at baseline (4 < CAI < 7) (OR = 0.770, 95% CI: 0.425-1.394, Wald 3.747, P = 0.028) proved to be effective as factors predicting clinical response. CONCLUSION: GMA may be a valid therapeutic option for steroid-dependent ulcerative colitis patients with mild-moderate disease and its clinical efficacy seems to persist for 12 mo. PMID:21528055

Association Between Perceived Discrimination and Racial/Ethnic Disparities in Problem Behaviors Among Preadolescent Youths

PubMed Central

Elliott, Marc N.; Kanouse, David E.; Klein, David J.; Davies, Susan L.; Cuccaro, Paula M.; Banspach, Stephen W.; Peskin, Melissa F.; Schuster, Mark A.

2013-01-01

Objectives. We examined the contribution of perceived racial/ethnic discrimination to disparities in problem behaviors among preadolescent Black, Latino, and White youths. Methods. We used cross-sectional data from Healthy Passages, a 3-community study of 5119 fifth graders and their parents from August 2004 through September 2006 in Birmingham, Alabama; Los Angeles County, California; and Houston, Texas. We used multivariate regressions to examine the relationships of perceived racial/ethnic discrimination and race/ethnicity to problem behaviors. We used values from these regressions to calculate the percentage of disparities in problem behaviors associated with the discrimination effect. Results. In multivariate models, perceived discrimination was associated with greater problem behaviors among Black and Latino youths. Compared with Whites, Blacks were significantly more likely to report problem behaviors, whereas Latinos were significantly less likely (a “reverse disparity”). When we set Blacks’ and Latinos’ discrimination experiences to zero, the adjusted disparity between Blacks and Whites was reduced by an estimated one third to two thirds; the reverse adjusted disparity favoring Latinos widened by about one fifth to one half. Conclusions. Eliminating discrimination could considerably reduce mental health issues, including problem behaviors, among Black and Latino youths. PMID:23597387

Remodeling characteristics and collagen distribution in synthetic mesh materials explanted from human subjects after abdominal wall reconstruction: an analysis of remodeling characteristics by patient risk factors and surgical site classifications

PubMed Central

Cavallo, Jaime A.; Roma, Andres A.; Jasielec, Mateusz S.; Ousley, Jenny; Creamer, Jennifer; Pichert, Matthew D.; Baalman, Sara; Frisella, Margaret M.; Matthews, Brent D.

2014-01-01

Background The purpose of this study was to evaluate the associations between patient characteristics or surgical site classifications and the histologic remodeling scores of synthetic meshes biopsied from their abdominal wall repair sites in the first attempt to generate a multivariable risk prediction model of non-constructive remodeling. Methods Biopsies of the synthetic meshes were obtained from the abdominal wall repair sites of 51 patients during a subsequent abdominal re-exploration. Biopsies were stained with hematoxylin and eosin, and evaluated according to a semi-quantitative scoring system for remodeling characteristics (cell infiltration, cell types, extracellular matrix deposition, inflammation, fibrous encapsulation, and neovascularization) and a mean composite score (CR). Biopsies were also stained with Sirius Red and Fast Green, and analyzed to determine the collagen I:III ratio. Based on univariate analyses between subject clinical characteristics or surgical site classification and the histologic remodeling scores, cohort variables were selected for multivariable regression models using a threshold p value of ≤0.200. Results The model selection process for the extracellular matrix score yielded two variables: subject age at time of mesh implantation, and mesh classification (c-statistic = 0.842). For CR score, the model selection process yielded two variables: subject age at time of mesh implantation and mesh classification (r2 = 0.464). The model selection process for the collagen III area yielded a model with two variables: subject body mass index at time of mesh explantation and pack-year history (r2 = 0.244). Conclusion Host characteristics and surgical site assessments may predict degree of remodeling for synthetic meshes used to reinforce abdominal wall repair sites. These preliminary results constitute the first steps in generating a risk prediction model that predicts the patients and clinical circumstances for which non-constructive remodeling of an abdominal wall repair site with synthetic mesh reinforcement is most likely to occur. PMID:24442681

An Incident Cohort Study Comparing Survival on Home Hemodialysis and Peritoneal Dialysis (Australia and New Zealand Dialysis and Transplantation Registry)

PubMed Central

Nadeau-Fredette, Annie-Claire; Hawley, Carmel M.; Pascoe, Elaine M.; Chan, Christopher T.; Clayton, Philip A.; Polkinghorne, Kevan R.; Boudville, Neil; Leblanc, Martine

2015-01-01

Background and objectives Home dialysis is often recognized as a first-choice therapy for patients initiating dialysis. However, studies comparing clinical outcomes between peritoneal dialysis and home hemodialysis have been very limited. Design, setting, participants, & measurements This Australia and New Zealand Dialysis and Transplantation Registry study assessed all Australian and New Zealand adult patients receiving home dialysis on day 90 after initiation of RRT between 2000 and 2012. The primary outcome was overall survival. The secondary outcomes were on-treatment survival, patient and technique survival, and death-censored technique survival. All results were adjusted with three prespecified models: multivariable Cox proportional hazards model (main model), propensity score quintile–stratified model, and propensity score–matched model. Results The study included 10,710 patients on incident peritoneal dialysis and 706 patients on incident home hemodialysis. Treatment with home hemodialysis was associated with better patient survival than treatment with peritoneal dialysis (5-year survival: 85% versus 44%, respectively; log-rank P<0.001). Using multivariable Cox proportional hazards analysis, home hemodialysis was associated with superior patient survival (hazard ratio for overall death, 0.47; 95% confidence interval, 0.38 to 0.59) as well as better on-treatment survival (hazard ratio for on-treatment death, 0.34; 95% confidence interval, 0.26 to 0.45), composite patient and technique survival (hazard ratio for death or technique failure, 0.34; 95% confidence interval, 0.29 to 0.40), and death-censored technique survival (hazard ratio for technique failure, 0.34; 95% confidence interval, 0.28 to 0.41). Similar results were obtained with the propensity score models as well as sensitivity analyses using competing risks models and different definitions for technique failure and lag period after modality switch, during which events were attributed to the initial modality. Conclusions Home hemodialysis was associated with superior patient and technique survival compared with peritoneal dialysis. PMID:26068181

Night shift work and incident diabetes among U.S. black women

PubMed Central

Vimalananda, Varsha G.; Palmer, Julie R.; Gerlovin, Hanna; Wise, Lauren A.; Rosenzweig, James L.; Rosenberg, Lynn; Narváez, Edward A. Ruiz

2015-01-01

Aims To assess shift work in relation to incident type 2 diabetes among African American women. Methods In the Black Women's Health Study (BWHS), an ongoing prospective cohort study, we followed 28,041 participants for incident diabetes during 2005-2013. They answered questions in 2005 about having worked the night shift. We estimated hazard ratios (HR) and 95% confidence intervals (CI) for incident diabetes using Cox proportional hazards models. The basic multivariable model included age, time period, family history of diabetes, education, and neighborhood SES. In further models, we controlled for lifestyle factors and body mass index (BMI). Results Over the 8 years of follow-up, there were 1,786 incident diabetes cases. Relative to never having worked the night shift, HRs (95% CI) of diabetes were 1.17 (1.04, 1.31) for 1-2 years of night shift work, 1.23 (1.06, 1.41) for 3-9 years, and 1.42 (1.19, 1.70) for ≥ 10 years (P-trend < 0.0001). The monotonic positive association between night shift work and type 2 diabetes remained after multivariable adjustment (P-trend = 0.02). The association did not vary by obesity status, but was stronger in women aged < 50 years. Conclusions Long durations of shift work were associated with an increased risk of type 2 diabetes. The association was only partially explained by lifestyle factors and BMI. A better understanding of the mechanisms by which shift work may affect risk of diabetes is needed in view of the high prevalence of shift work among U.S. workers. PMID:25586362

Cytogenetic Prognostication Within Medulloblastoma Subgroups

PubMed Central

Shih, David J.H.; Northcott, Paul A.; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M.; Garzia, Livia; Peacock, John; Mack, Stephen C.; Wu, Xiaochong; Rolider, Adi; Morrissy, A. Sorana; Cavalli, Florence M.G.; Jones, David T.W.; Zitterbart, Karel; Faria, Claudia C.; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A.; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G.; Liau, Linda M.; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K.; Thompson, Reid C.; Bailey, Simon; Lindsey, Janet C.; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M.C.; Scherer, Stephen W.; Phillips, Joanna J.; Gupta, Nalin; Fan, Xing; Muraszko, Karin M.; Vibhakar, Rajeev; Eberhart, Charles G.; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J.; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F.; Weiss, William A.; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R.; Rubin, Joshua B.; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M.; Gajjar, Amar; Packer, Roger J.; Rutkowski, Stefan; Pomeroy, Scott L.; French, Pim J.; Kloosterhof, Nanne K.; Kros, Johan M.; Van Meir, Erwin G.; Clifford, Steven C.; Bourdeaut, Franck; Delattre, Olivier; Doz, François F.; Hawkins, Cynthia E.; Malkin, David; Grajkowska, Wieslawa A.; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T.; Pfister, Stefan M.; Taylor, Michael D.

2014-01-01

Purpose Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Patients and Methods Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Results Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Conclusion Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials. PMID:24493713

Leukocyte Telomere Length in HIV-Infected and HIV-Exposed Uninfected Children: Shorter Telomeres with Uncontrolled HIV Viremia

PubMed Central

Côté, Hélène C. F.; Soudeyns, Hugo; Thorne, Anona; Alimenti, Ariane; Lamarre, Valérie; Maan, Evelyn J.; Sattha, Beheroze; Singer, Joel; Lapointe, Normand; Money, Deborah M.; Forbes, John

2012-01-01

Objectives Nucleoside reverse transcriptase inhibitors (NRTIs) used in HIV antiretroviral therapy can inhibit human telomerase reverse transcriptase. We therefore investigated whether in utero or childhood exposure to NRTIs affects leukocyte telomere length (LTL), a marker of cellular aging. Methods In this cross-sectional CARMA cohort study, we investigated factors associated with LTL in HIV -1-infected (HIV+) children (n = 94), HIV-1-exposed uninfected (HEU) children who were exposed to antiretroviral therapy (ART) perinatally (n = 177), and HIV-unexposed uninfected (HIV−) control children (n = 104) aged 0–19 years. Univariate followed by multivariate linear regression models were used to examine relationships of explanatory variables with LTL for: a) all subjects, b) HIV+/HEU children only, and c) HIV+ children only. Results After adjusting for age and gender, there was no difference in LTL between the 3 groups, when considering children of all ages together. In multivariate models, older age and male gender were associated with shorter LTL. For the HIV+ group alone, having a detectable HIV viral load was also strongly associated with shorter LTL (p = 0.007). Conclusions In this large study, group rates of LTL attrition were similar for HIV+, HEU and HIV− children. No associations between children’s LTL and their perinatal ART exposure or HIV status were seen in linear regression models. However, the association between having a detectable HIV viral load and shorter LTL suggests that uncontrolled HIV viremia rather than duration of ART exposure may be associated with acceleration of blood telomere attrition. PMID:22815702

Stratification of Recanalization for Patients with Endovascular Treatment of Intracranial Aneurysms

PubMed Central

Ogilvy, Christopher S.; Chua, Michelle H.; Fusco, Matthew R.; Reddy, Arra S.; Thomas, Ajith J.

2015-01-01

Background With increasing utilization of endovascular techniques in the treatment of both ruptured and unruptured intracranial aneurysms, the issue of obliteration efficacy has become increasingly important. Objective Our goal was to systematically develop a comprehensive model for predicting retreatment with various types of endovascular treatment. Methods We retrospectively reviewed medical records that were prospectively collected for 305 patients who received endovascular treatment for intracranial aneurysms from 2007 to 2013. Multivariable logistic regression was performed on candidate predictors identified by univariable screening analysis to detect independent predictors of retreatment. A composite risk score was constructed based on the proportional contribution of independent predictors in the multivariable model. Results Size (>10 mm), aneurysm rupture, stent assistance, and post-treatment degree of aneurysm occlusion were independently associated with retreatment while intraluminal thrombosis and flow diversion demonstrated a trend towards retreatment. The Aneurysm Recanalization Stratification Scale was constructed by assigning the following weights to statistically and clinically significant predictors. Aneurysm-specific factors: Size (>10 mm), 2 points; rupture, 2 points; presence of thrombus, 2 points. Treatment-related factors: Stent assistance, -1 point; flow diversion, -2 points; Raymond Roy 2 occlusion, 1 point; Raymond Roy 3 occlusion, 2 points. This scale demonstrated good discrimination with a C-statistic of 0.799. Conclusion Surgical decision-making and patient-centered informed consent require comprehensive and accessible information on treatment efficacy. We have constructed the Aneurysm Recanalization Stratification Scale to enhance this decision-making process. This is the first comprehensive model that has been developed to quantitatively predict the risk of retreatment following endovascular therapy. PMID:25621984

The Role of Positive Alcohol Expectancies in Underage Binge Drinking Among College Students

PubMed Central

McBride, Nicole M.; Barrett, Blake; Moore, Kathleen A.; Schonfeld, Lawrence

2014-01-01

Objective This study explored associations between positive alcohol expectancies, demographics, as well as academic status and binge drinking among underage college students. Participants A sample of 1,553 underage college students at three public universities and one college in the southeast who completed the Core Alcohol and Drug Survey in the spring 2013 semester. Methods A series of bivariate analyses and logistic regression models were used to examine associations between demographic and academic status variables as well as positive alcohol expectancies with self-reported binge drinking. Positive alcohol expectancies were examined in multivariable models via two factors derived from principal component analyses. Results Students who endorsed higher agreement of these two emergent factors (Sociability; Sexuality) were more likely to report an occurrence of binge drinking in the past two weeks. Conclusions Study results document associations between positive alcohol expectancies and binge drinking among underage students; implications for prevention and treatment are discussed. PMID:24678848

A General Multivariate Latent Growth Model with Applications to Student Achievement

ERIC Educational Resources Information Center

Bianconcini, Silvia; Cagnone, Silvia

2012-01-01

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

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…

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

Virus replication as a phenotypic version of polynucleotide evolution.

PubMed

Antoneli, Fernando; Bosco, Francisco; Castro, Diogo; Janini, Luiz Mario

2013-04-01

In this paper, we revisit and adapt to viral evolution an approach based on the theory of branching process advanced by Demetrius et al. (Bull. Math. Biol. 46:239-262, 1985), in their study of polynucleotide evolution. By taking into account beneficial effects, we obtain a non-trivial multivariate generalization of their single-type branching process model. Perturbative techniques allows us to obtain analytical asymptotic expressions for the main global parameters of the model, which lead to the following rigorous results: (i) a new criterion for "no sure extinction", (ii) a generalization and proof, for this particular class of models, of the lethal mutagenesis criterion proposed by Bull et al. (J. Virol. 18:2930-2939, 2007), (iii) a new proposal for the notion of relaxation time with a quantitative prescription for its evaluation, (iv) the quantitative description of the evolution of the expected values in four distinct "stages": extinction threshold, lethal mutagenesis, stationary "equilibrium", and transient. Finally, based on these quantitative results, we are able to draw some qualitative conclusions.

[Determinants of health care utilization in Costa Rica].

PubMed

Morera Salas, Melvin; Aparicio Llanos, Amada

2010-01-01

To analyze the determinants of health care utilization (visits to the doctor) in Costa Rica using an econometric approach. Data were drawn from the National Survey of Health for Costa Rica 2006. We modeled the Grossman approach to the demand for health services by using a standard negative binomial regression, and used a hurdle model for the principal-agent specification. The factors determining healthcare utilization were level of education, self-assessed health, number of declared chronic diseases and geographic region of residence. The number of outpatient visits to the doctor depends on the proxies for medical need, but we found no multivariate association between the use of outpatient visits and income or insurance status. This result suggests that there is no problem with access in the public - almost universal - Costa Rican health system. No conclusive results were obtained on the influence of the physician on the frequency of use of health care services, as postulated by the principal-agent model. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

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

PubMed

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

2015-12-30

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

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss.

PubMed

Rosen, Sophia; Davidov, Ori

2012-07-20

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome. The multivariate mixed-effects model is a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, it is known that hearing thresholds, at every frequency, increase with age. Moreover, this age-related threshold elevation is monotone in frequency, that is, the higher the frequency, the higher, on average, is the rate of threshold elevation. This means that there is a natural ordering among the different frequencies in the rate of hearing loss. In practice, this amounts to imposing a set of constraints on the different frequencies' regression coefficients modeling the mean effect of time and age at entry to the study on hearing thresholds. The aforementioned constraints should be accounted for in the analysis. The result is a multivariate longitudinal model with restricted parameters. We propose estimation and testing procedures for such models. We show that ignoring the constraints may lead to misleading inferences regarding the direction and the magnitude of various effects. Moreover, simulations show that incorporating the constraints substantially improves the mean squared error of the estimates and the power of the tests. We used this methodology to analyze a real hearing loss study. Copyright © 2012 John Wiley & Sons, Ltd.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

Suicide ideation among college students: A multivariate analysis

PubMed Central

Arria, Amelia M.; O’Grady, Kevin E.; Caldeira, Kimberly M.; Vincent, Kathryn B.; Wilcox, Holly C.; Wish, Eric D.

2009-01-01

Objectives To develop a multi-dimensional model that might explain college suicide ideation. Methods Face-to-face interviews were conducted with 1,249 first-year college students. Results An estimated 6% wt of first-year students at this university had current suicide ideation. Depressive symptoms, low social support, affective dysregulation, and father-child conflict were each independently associated with suicide ideation. Only 40%wt of individuals with suicide ideation were classified as depressed according to standard criteria. In the group who reported low levels of depressive symptoms, low social support and affective dysregulation were important predictors of suicide ideation. Alcohol use disorder was also independently associated with suicide ideation, while parental conflict was not. Conclusions Results highlight potential targets for early intervention among college students. PMID:19590997

Age-Specific Prostate Specific Antigen Cutoffs for Guiding Biopsy Decision in Chinese Population

PubMed Central

Xu, Jianfeng; Jiang, Haowen; Ding, Qiang

2013-01-01

Background Age-specific prostate specific antigen (PSA) cutoffs for prostate biopsy have been widely used in the USA and European countries. However, the application of age-specific PSA remains poorly understood in China. Methods Between 2003 and 2012, 1,848 men over the age of 40, underwent prostate biopsy for prostate cancer (PCa) at Huashan Hospital, Shanghai, China. Clinical information and blood samples were collected prior to biopsy for each patient. Men were divided into three age groups (≤60, 61 to 80, and >80) for analyses. Digital rectal examination (DRE), transrectal ultrasound (prostate volume and nodule), total PSA (tPSA), and free PSA (fPSA) were also included in the analyses. Logistic regression was used to build the multi-variate model. Results Serum tPSA levels were age-dependent (P = 0.008), while %fPSA (P = 0.051) and PSAD (P = 0.284) were age-independent. At a specificity of 80%, the sensitivities for predicting PCa were 83%, 71% and 68% with tPSA cutoff values of 19.0 ng/mL (age≤60),21.0 ng/mL (age 61–80), and 23.0 ng/mL (age≥81). Also, sensitivities at the same tPSA levels were able to reach relatively high levels (70%–88%) for predicting high-grade PCa. Area (AUC) under the receive operating curves (ROCs) of tPSA, %fPSA, PSAD and multi-variate model were different in age groups. When predicting PCa, the AUC of tPSA, %fPSA, PSAD and multi-variate model were 0.90, 0.57, 0.93 and 0.87 respectively in men ≤60 yr; 0.82, 0.70, 0.88 and 0.86 respectively in men 61–80 yr; 0.79, 0.78, 0.87 and 0.88 respectively in men>80 yr. When predicting Gleason Score ≥7 or 8 PCa, there were no significant differences between AUCs of each variable. Conclusion Age-specific PSA cutoff values for prostate biopsy should be considered in the Chinese population. Indications for prostate biopsies (tPSA, %fPSA and PSAD) should be considered based on age in the Chinese population. PMID:23825670

How do patient characteristics influence informal payments for inpatient and outpatient health care in Albania: Results of logit and OLS models using Albanian LSMS 2005

PubMed Central

2011-01-01

Background Informal payments for health care are common in most former communist countries. This paper explores the demand side of these payments in Albania. By using data from the Living Standard Measurement Survey 2005 we control for individual determinants of informal payments in inpatient and outpatient health care. We use these results to explain the main factors contributing to the occurrence and extent of informal payments in Albania. Methods Using multivariate methods (logit and OLS) we test three models to explain informal payments: the cultural, economic and governance model. The results of logit models are presented here as odds ratios (OR) and results from OLS models as regression coefficients (RC). Results Our findings suggest differences in determinants of informal payments in inpatient and outpatient care. Generally our results show that informal payments are dependent on certain characteristics of patients, including age, area of residence, education, health status and health insurance. However, they are less dependent on income, suggesting homogeneity of payments across income categories. Conclusions We have found more evidence for the validity of governance and economic models than for the cultural model. PMID:21605459

Folate Deficiency, Atopy, and Severe Asthma Exacerbations in Puerto Rican Children

PubMed Central

Blatter, Joshua; Brehm, John M.; Sordillo, Joanne; Forno, Erick; Boutaoui, Nadia; Acosta-Pérez, Edna; Alvarez, María; Colón-Semidey, Angel; Weiss, Scott T.; Litonjua, Augusto A.; Canino, Glorisa

2016-01-01

Background: Little is known about folate and atopy or severe asthma exacerbations. We examined whether folate deficiency is associated with number of positive skin tests to allergens or severe asthma exacerbations in a high-risk population and further assessed whether such association is explained or modified by vitamin D status. Methods: Cross-sectional study of 582 children aged 6 to 14 years with (n = 304) and without (n = 278) asthma in San Juan, Puerto Rico. Folate deficiency was defined as plasma folate less than or equal to 20 ng/ml. Our outcomes were the number of positive skin tests to allergens (range, 0–15) in all children and (in children with asthma) one or more severe exacerbations in the previous year. Logistic and negative binomial regression models were used for the multivariate analysis. All multivariate models were adjusted for age, sex, household income, residential proximity to a major road, and (for atopy) case/control status; those for severe exacerbations were also adjusted for use of inhaled corticosteroids and vitamin D insufficiency (a plasma 25[OH]D < 30 ng/ml). Measurements and Main Results: In a multivariate analysis, folate deficiency was significantly associated with an increased degree of atopy and 2.2 times increased odds of at least one severe asthma exacerbation (95% confidence interval for odds ratio, 1.1–4.6). Compared with children who had normal levels of both folate and vitamin D, those with both folate deficiency and vitamin D insufficiency had nearly eightfold increased odds of one or more severe asthma exacerbation (95% confidence interval for adjusted odds ratio, 2.7–21.6). Conclusions: Folate deficiency is associated with increased degree of atopy and severe asthma exacerbations in school-aged Puerto Ricans. Vitamin D insufficiency may further increase detrimental effects of folate deficiency on severe asthma exacerbations. PMID:26561879

Predictors of anemia in women of reproductive age: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project.

PubMed

Wirth, James P; Woodruff, Bradley A; Engle-Stone, Reina; Namaste, Sorrel Ml; Temple, Victor J; Petry, Nicolai; Macdonald, Barbara; Suchdev, Parminder S; Rohner, Fabian; Aaron, Grant J

2017-07-01

Background: Anemia in women of reproductive age (WRA) (age range: 15-49 y) remains a public health problem globally, and reducing anemia in women by 50% by 2025 is a goal of the World Health Assembly. Objective: We assessed the associations between anemia and multiple proximal risk factors (e.g., iron and vitamin A deficiencies, inflammation, malaria, and body mass index) and distal risk factors (e.g., education status, household sanitation and hygiene, and urban or rural residence) in nonpregnant WRA. Design: Cross-sectional, nationally representative data from 10 surveys ( n = 27,018) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project were analyzed individually and pooled by the infection burden and risk in the country. We examined the severity of anemia and measured the bivariate associations between anemia and factors at the country level and by infection burden, which we classified with the use of the national prevalences of malaria, HIV, schistosomiasis, sanitation, and water-quality indicators. Pooled multivariate logistic regression models were constructed for each infection-burden category to identify independent determinants of anemia (hemoglobin concertation <120 g/L). Results: Anemia prevalence was ∼40% in countries with a high infection burden and 12% and 7% in countries with moderate and low infection burdens, respectively. Iron deficiency was consistently associated with anemia in multivariate models, but the proportion of anemic women who were iron deficient was considerably lower in the high-infection group (35%) than in the moderate- and low-infection groups (65% and 71%, respectively). In the multivariate analysis, inflammation, vitamin A insufficiency, socioeconomic status, and age were also significantly associated with anemia, but malaria and vitamin B-12 and folate deficiencies were not. Conclusions: The contribution of iron deficiency to anemia varies according to a country's infection burden. Anemia-reduction programs for WRA can be improved by considering the underlying infection burden of the population and by assessing the overlap of micronutrient deficiencies and anemia.

Clinical correlates of hypoglycaemia over 4 years in people with type 2 diabetes starting insulin: An analysis from the CREDIT study

PubMed Central

Calvi‐Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick

2018-01-01

Aim To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. Materials and methods CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. Results The proportions of participants reporting ≥1 non‐severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Conclusions Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. PMID:29205734

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

ERIC Educational Resources Information Center

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

ERIC Educational Resources Information Center

Gisburne, Jaclyn M.; Fairchild, Patricia J.

2004-01-01

This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

Assessing Reliability of Student Ratings of Advisor: A Comparison of Univariate and Multivariate Generalizability Approaches.

ERIC Educational Resources Information Center

Sun, Anji; Valiga, Michael J.

In this study, the reliability of the American College Testing (ACT) Program's "Survey of Academic Advising" (SAA) was examined using both univariate and multivariate generalizability theory approaches. The primary purpose of the study was to compare the results of three generalizability theory models (a random univariate model, a mixed…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5

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

Hannah, Walter M.; Maloney, Eric D.; Pritchard, Michael S.

Hindcast simulations of MJO events during the dynamics of the MJO (DYNAMO) field campaign are conducted with two models, one with conventional parameterization (CAM5) and a comparable model that utilizes superparameterization (SP–CAM). SP–CAM is shown to produce a qualitatively better reproduction of the fluctuations of precipitation and low–level zonal wind associated with the first two DYNAMO MJO events compared to CAM5. Interestingly, skill metrics using the real–time multivariate MJO index (RMM) suggest the opposite conclusion that CAM5 has more skill than SP–CAM. This inconsistency can be explained by a systematic increase of RMM amplitude with lead time, which results frommore » a drift of the large–scale wind field in SP–CAM that projects strongly onto the RMM index. CAM5 hindcasts exhibit a contraction of the moisture distribution, in which extreme wet and dry conditions become less frequent with lead time. SP–CAM hindcasts better reproduce the observed moisture distribution, but also have stronger drift patterns of moisture budget terms, such as an increase in drying by meridional advection in SP–CAM. This advection tendency in SP–CAM appears to be associated with enhanced off–equatorial synoptic eddy activity with lead time. In conclusion, systematic drift moisture tendencies in SP–CAM are of similar magnitude to intraseasonal moisture tendencies, and therefore are important for understanding MJO prediction skill.« less

Do Intermediate Radiation Doses Contribute to Late Rectal Toxicity? An Analysis of Data From Radiation Therapy Oncology Group Protocol 94-06

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

Tucker, Susan L., E-mail: sltucker@mdanderson.org; Dong, Lei; Michalski, Jeff M.

2012-10-01

Purpose: To investigate whether the volumes of rectum exposed to intermediate doses, from 30 to 50 Gy, contribute to the risk of Grade {>=}2 late rectal toxicity among patients with prostate cancer receiving radiotherapy. Methods and Materials: Data from 1009 patients treated on Radiation Therapy Oncology Group protocol 94-06 were analyzed using three approaches. First, the contribution of intermediate doses to a previously published fit of the Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) model was determined. Next, the extent to which intermediate doses provide additional risk information, after taking the LKB model into account, was investigated. Third, the proportionmore » of rectum receiving doses higher than a threshold, VDose, was computed for doses ranging from 5 to 85 Gy, and a multivariate Cox proportional hazards model was used to determine which of these parameters were significantly associated with time to Grade {>=}2 late rectal toxicity. Results: Doses <60 Gy had no detectable impact on the fit of the LKB model, as expected on the basis of the small estimate of the volume parameter (n = 0.077). Furthermore, there was no detectable difference in late rectal toxicity among cohorts with similar risk estimates from the LKB model but with different volumes of rectum exposed to intermediate doses. The multivariate Cox proportional hazards model selected V75 as the only value of VDose significantly associated with late rectal toxicity. Conclusions: There is no evidence from these data that intermediate doses influence the risk of Grade {>=}2 late rectal toxicity. Instead, the critical doses for this endpoint seem to be {>=}75 Gy. It is hypothesized that cases of Grade {>=}2 late rectal toxicity occurring among patients with V75 less than approximately 12% may be due to a 'background' level of risk, likely due mainly to biological factors.« less

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

Li, B; Fujita, A; Buch, K

Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIsmore » over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.« less

Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

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

Louie, Alexander V., E-mail: Dr.alexlouie@gmail.com; Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario; Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts

Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogrammore » for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.« less

Why the Long Face? The Mechanics of Mandibular Symphysis Proportions in Crocodiles

PubMed Central

Walmsley, Christopher W.; Smits, Peter D.; Quayle, Michelle R.; McCurry, Matthew R.; Richards, Heather S.; Oldfield, Christopher C.; Wroe, Stephen; Clausen, Phillip D.; McHenry, Colin R.

2013-01-01

Background Crocodilians exhibit a spectrum of rostral shape from long snouted (longirostrine), through to short snouted (brevirostrine) morphologies. The proportional length of the mandibular symphysis correlates consistently with rostral shape, forming as much as 50% of the mandible’s length in longirostrine forms, but 10% in brevirostrine crocodilians. Here we analyse the structural consequences of an elongate mandibular symphysis in relation to feeding behaviours. Methods/Principal Findings Simple beam and high resolution Finite Element (FE) models of seven species of crocodile were analysed under loads simulating biting, shaking and twisting. Using beam theory, we statistically compared multiple hypotheses of which morphological variables should control the biomechanical response. Brevi- and mesorostrine morphologies were found to consistently outperform longirostrine types when subject to equivalent biting, shaking and twisting loads. The best predictors of performance for biting and twisting loads in FE models were overall length and symphyseal length respectively; for shaking loads symphyseal length and a multivariate measurement of shape (PC1– which is strongly but not exclusively correlated with symphyseal length) were equally good predictors. Linear measurements were better predictors than multivariate measurements of shape in biting and twisting loads. For both biting and shaking loads but not for twisting, simple beam models agree with best performance predictors in FE models. Conclusions/Significance Combining beam and FE modelling allows a priori hypotheses about the importance of morphological traits on biomechanics to be statistically tested. Short mandibular symphyses perform well under loads used for feeding upon large prey, but elongate symphyses incur high strains under equivalent loads, underlining the structural constraints to prey size in the longirostrine morphotype. The biomechanics of the crocodilian mandible are largely consistent with beam theory and can be predicted from simple morphological measurements, suggesting that crocodilians are a useful model for investigating the palaeobiomechanics of other aquatic tetrapods. PMID:23342027

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8

PubMed Central

Ferreira, Fábio S.; Pereira, João M.S.; Duarte, João V.; Castelo-Branco, Miguel

2017-01-01

Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Objective: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). Method: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately – using standard univariate VBM - and simultaneously, with multivariate analyses. Results: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. Conclusion: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities. PMID:28761571

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Environmental Influences on Daily Emergency Admissions in Sickle-Cell Disease Patients

PubMed Central

Mekontso Dessap, Armand; Contou, Damien; Dandine-Roulland, Claire; Hemery, François; Habibi, Anoosha; Charles-Nelson, Anaïs; Galacteros, Frederic; Brun-Buisson, Christian; Maitre, Bernard; Katsahian, Sandrine

2014-01-01

Abstract Previous reports have suggested a role for weather conditions and air pollution on the variability of sickle cell disease (SCD) severity, but large-scale comprehensive epidemiological studies are lacking. In order to evaluate the influence of air pollution and climatic factors on emergency hospital admissions (EHA) in SCD patients, we conducted an 8-year observational retrospective study in 22 French university hospitals in Paris conurbation, using distributed lag non-linear models, a methodology able to flexibly describe simultaneously non-linear and delayed associations, with a multivariable approach. During the 2922 days of the study, there were 17,710 EHA, with a mean daily number of 6.1 ± 2.8. Most environmental factors were significantly correlated to each other. The risk of EHA was significantly associated with higher values of nitrogen dioxide, atmospheric particulate matters, and daily mean wind speed; and with lower values of carbon monoxide, ozone, sulfur dioxide, daily temperature (minimal, maximal, mean, and range), day-to-day mean temperature change, daily bright sunshine, and occurrence of storm. There was a lag effect for 12 of 15 environmental factors influencing hospitalization rate. Multivariate analysis identified carbon monoxide, day-to-day temperature change, and mean wind speed, along with calendar factors (weekend, summer season, and year) as independent factors associated with EHA. In conclusion, most weather conditions and air pollutants assessed were correlated to each other and influenced the rate of EHA in SCD patients. In multivariate analysis, lower carbon monoxide concentrations, day-to-day mean temperature drop and higher wind speed were associated with increased risk of EHA. PMID:25546672

Environmental influences on daily emergency admissions in sickle-cell disease patients.

PubMed

Mekontso Dessap, Armand; Contou, Damien; Dandine-Roulland, Claire; Hemery, François; Habibi, Anoosha; Charles-Nelson, Anaïs; Galacteros, Frederic; Brun-Buisson, Christian; Maitre, Bernard; Katsahian, Sandrine

2014-12-01

Previous reports have suggested a role for weather conditions and air pollution on the variability of sickle cell disease (SCD) severity, but large-scale comprehensive epidemiological studies are lacking. In order to evaluate the influence of air pollution and climatic factors on emergency hospital admissions (EHA) in SCD patients, we conducted an 8-year observational retrospective study in 22 French university hospitals in Paris conurbation, using distributed lag non-linear models, a methodology able to flexibly describe simultaneously non-linear and delayed associations, with a multivariable approach. During the 2922 days of the study, there were 17,710 EHA, with a mean daily number of 6.1 ± 2.8. Most environmental factors were significantly correlated to each other. The risk of EHA was significantly associated with higher values of nitrogen dioxide, atmospheric particulate matters, and daily mean wind speed; and with lower values of carbon monoxide, ozone, sulfur dioxide, daily temperature (minimal, maximal, mean, and range), day-to-day mean temperature change, daily bright sunshine, and occurrence of storm. There was a lag effect for 12 of 15 environmental factors influencing hospitalization rate. Multivariate analysis identified carbon monoxide, day-to-day temperature change, and mean wind speed, along with calendar factors (weekend, summer season, and year) as independent factors associated with EHA. In conclusion, most weather conditions and air pollutants assessed were correlated to each other and influenced the rate of EHA in SCD patients. In multivariate analysis, lower carbon monoxide concentrations, day-to-day mean temperature drop and higher wind speed were associated with increased risk of EHA.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Inter-hospital variations in caesarean sections. A risk adjusted comparison in the Valencia public hospitals

PubMed Central

Librero, J.; Peiro, S.; Calderon, S. M.

2000-01-01

BACKGROUND—The aim of this study was to describe the variability in caesarean rates in the public hospitals in the Valencia Region, Spain, and to analyse the association between caesarean sections and clinical and extra-clinical factors.
METHODS—Analysis of data contained in the Minimum Basic Data Set (MBDS) compiled for all births in 11 public hospitals in Valencia during 1994-1995 (n=36 819). Bivariate and multivariate analyses were used to evaluate the association between caesarean section rates and specific risk factors. The multivariate model was used to construct predictions about caesarean rates for each hospital, for comparison with rates observed.
RESULTS—Caesarean rates were 17.6% (inter-hospital range: 14.7% to 25.0%), with ample variability between hospitals in the diagnosis of maternal-fetal risk factors (particularly dystocia and fetal distress), and the indication for caesarean in the presence of these factors. Multivariate analysis showed that maternal-fetal risk factors correlated strongly with caesarean section, although extra-clinical factors, such as the day of the week, also correlated positively. After adjusting for the risk factors, the inter-hospital variation in caesarean rates persisted.
CONCLUSIONS—Although certain limitations (imprecision of some diagnoses and information biases in the MBDS) make it impossible to establish unequivocal conclusions, results show a high degree of variability among hospitals when opting for caesarean section. This variability cannot be justified by differences in obstetric risks.


Keywords: hospital utilisation; medical practice variation; caesarean section; administrative databases PMID:10890876

Fibroblast Growth Factor 2-A Predictor of Outcome for Patients Irradiated for Stage II-III Non-Small-Cell Lung Cancer

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

Rades, Dirk, E-mail: Rades.Dirk@gmx.net; Setter, Cornelia; Dahl, Olav

2012-01-01

Purpose: The prognostic value of the tumor cell expression of the fibroblast growth factor 2 (FGF-2) in patients with non-small-cell lung cancer (NSCLC) is unclear. The present study investigated the effect of tumor cell expression of FGF-2 on the outcome of 60 patients irradiated for Stage II-III NSCLC. Methods and Materials: The effect of FGF-2 expression and 13 additional factors on locoregional control (LRC), metastasis-free survival (MFS), and overall survival (OS) were retrospectively evaluated. These additional factors included age, gender, Karnofsky performance status, histologic type, histologic grade, T and N category, American Joint Committee on Cancer stage, surgery, chemotherapy, pack-years,more » smoking during radiotherapy, and hemoglobin during radiotherapy. Locoregional failure was identified by endoscopy or computed tomography. Univariate analyses were performed with the Kaplan-Meier method and the Wilcoxon test and multivariate analyses with the Cox proportional hazard model. Results: On univariate analysis, improved LRC was associated with surgery (p = .017), greater hemoglobin levels (p = .036), and FGF-2 negativity (p <.001). On multivariate analysis of LRC, surgery (relative risk [RR], 2.44; p = .037), and FGF-2 expression (RR, 5.06; p <.001) maintained significance. On univariate analysis, improved MFS was associated with squamous cell carcinoma (p = .020), greater hemoglobin levels (p = .007), and FGF-2 negativity (p = .001). On multivariate analysis of MFS, the hemoglobin levels (RR, 2.65; p = .019) and FGF-2 expression (RR, 3.05; p = .004) were significant. On univariate analysis, improved OS was associated with a lower N category (p = .048), greater hemoglobin levels (p <.001), and FGF-2 negativity (p <.001). On multivariate analysis of OS, greater hemoglobin levels (RR, 4.62; p = .002) and FGF-2 expression (RR, 3.25; p = .002) maintained significance. Conclusions: Tumor cell expression of FGF-2 appeared to be an independent negative predictor of LRC, MFS, and OS.« less

Predicting Failure of Glyburide Therapy in Gestational Diabetes

PubMed Central

Harper, Lorie M.; Glover, Angelica V.; Biggio, Joseph R.; Tita, Alan

2016-01-01

Objective We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Study Design Retrospective cohort of all singletons with GDM treated with glyburide 2007–2013. Glyburide failure was defined as reaching glyburide 20 mg/day and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Results Of 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ≤26 weeks, 1-hour GCT ≥228 mg/dL, 3-hour GTT 1-hour value ≥221 mg/dL, ≥7 post-prandial blood sugars >120 mg/dL in the week glyburide started, and ≥1 blood sugar >200 mg/dL. The model accurately classified 81% of subjects. Conclusions Women with GDM who will require insulin can be identified at initiation of pharmacologic therapy. PMID:26796130

Toward the Multivariate Modeling of Achievement, Aptitude, and Personality.

ERIC Educational Resources Information Center

Foshay, Wellesley R.; Misanchuk, Earl R.

1981-01-01

A multivariate investigation of the dynamics of cumulative achievement studied the influence of course grades, personality traits, environmental variables, and previous performance. The latter was the best single predictor of performance. (CJ)

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Community Attitudes About Discussing Sexual Health: Assessing Public Opinion of Local STD Prevention Campaigns

PubMed Central

Thomas, Rosalind; Bekan Homawoo, Brigitte; McClamroch, Kristi; Wise, Benjamin; Coles, F. Bruce

2013-01-01

Objectives We assessed public views about the acceptability of and need for sexually transmitted disease (STD) and sexual health-related educational messaging in local campaigns. Methods A 28-item state-added module was included in the 2008 New York Behavioral Risk Factor Surveillance System survey (n=3,751). Respondents rated acceptability of venues/dissemination channels and messaging and agreement with attitudinal/need statements. Additional data were analyzed from a separate state survey with individual county samples (n=36,257). We conducted univariate, bivariate, and multivariable modeling analyses. Results Each venue was acceptable to more than three-quarters of respondents (range: 79% for billboards to 95% for teaching STD prevention in high school). All message areas were acceptable to at least 85% of respondents (acceptability rating range: 85% to 97%). More than 70% agreed that there is a need for more open discussion about STDs. Bivariate analyses identified areas where messaging tailored to specific subgroups may be helpful (e.g., 26% of white people, 44% of African Americans, and 45% of Hispanic people agreed with the statement, “I need ideas about how to talk to my partner about protection from STDs”). Little geographic variation was seen. Results of multivariable modeling on opposition showed limited interaction effects. Conclusion These data provide key information about current community norms and reflect the public's approval for hearing and seeing more about sexual health and STDs in a range of public forums. PMID:23450887

Longitudinal follow-up of nutritional status and its influencing factors in adults undergoing allogeneic hematopoietic cell transplantation.

PubMed

Urbain, P; Birlinger, J; Lambert, C; Finke, J; Bertz, H; Biesalski, H-K

2013-03-01

There are few longitudinal data on nutritional status and body composition of patients undergoing allogeneic hematopoietic cell transplantation (alloHCT). We assessed nutritional status of 105 patients before alloHCT and its course during the early post-transplant period to day +30 and day +100 via weight history, body mass index (BMI) normalized for gender and age, Subjective Global Assessment, phase angle normalized for gender, age, and BMI, and fat-free and body fat masses. Furthermore, we present a multivariate regression model investigating the impact of factors on body weight. At admission, 23.8% reported significant weight losses (>5%) in the previous 6 months, and we noted 31.5% with abnormal age- and sex-adjusted BMI values (10th, 90th percentiles). BMI decreased significantly (P<0.0001) in both periods by 11% in total, meaning a weight loss of 8.6±5.7 kg. Simultaneously, the patients experienced significant losses (P<0.0001) of both fat-free and body fat masses. Multivariate regression model revealed clinically relevant acute GVHD (parameter estimate 1.43; P=0.02) and moderate/severe anorexia (parameter estimate 1.07; P=0.058) as independent factors influencing early weight loss. In conclusion, our results show a significant deterioration in nutritional status during the early post-transplant period. Predominant alloHCT-associated complications such as anorexia and acute GVHD became evident as significant factors influencing nutritional status.

Decreased Odds of Injection Risk Behavior Associated With Direct Versus Indirect Use of Syringe Exchange: Evidence From Two California Cities.

PubMed

Behrends, Czarina N; Li, Chin-Shang; Gibson, David R

2017-07-29

While there is substantial evidence that syringe exchange programs (SEPs) are effective in preventing HIV among people who inject drugs (PWID), nearly all the evidence comes from PWID who obtain syringes from an SEP directly. Much less is known about the benefits of secondary exchange to PWID who get syringes indirectly from friends or acquaintances who visit an SEP for them. We evaluated the effectiveness of direct versus indirect syringe exchange in reducing HIV-related high-risk injecting behavior among PWID in two separate studies conducted in Sacramento and San Jose, California, cities with quite different syringe exchange models. In both studies associations between direct and indirect syringe exchange and self-reported risk behavior were examined with multivariable logistic regression models. Study 1 assessed effects of a "satellite" home-delivery syringe exchange in Sacramento, while Study 2 evaluated a conventional fixed-site exchange in San Jose. Multivariable analyses revealed 95% and 69% reductions, respectively, in high-risk injection associated with direct use of the SEPs in Sacramento and San Jose, and a 46% reduction associated with indirect use of the SEP in Sacramento. Conclusions/Importance: The very large effect of direct SEP use in Sacramento was likely due in part to home delivery of sterile syringes. While more modest effects were associated with indirect use, such use nevertheless is valuable in reducing the risk of HIV transmission of PWID who are unable or unwilling to visit a syringe exchange.

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Health Care Workplace Discrimination and Physician Turnover

PubMed Central

Nunez-Smith, Marcella; Pilgrim, Nanlesta; Wynia, Matthew; Desai, Mayur M.; Bright, Cedric; Krumholz, Harlan M.; Bradley, Elizabeth H.

2013-01-01

Objective To examine the association between physician race/ethnicity, workplace discrimination, and physician job turnover. Methods Cross-sectional, national survey conducted in 2006–2007 of practicing physicians [n = 529] randomly identified via the American Medical Association Masterfile and The National Medical Association membership roster. We assessed the relationships between career racial/ethnic discrimination at work and several career-related dependent variables, including 2 measures of physician turnover, career satisfaction, and contemplation of career change. We used standard frequency analyses, odds ratios and χ2 statistics, and multivariate logistic regression modeling to evaluate these associations. Results Physicians who self-identified as nonmajority were significantly more likely to have left at least 1 job because of workplace discrimination (black, 29%; Asian, 24%; other race, 21%; Hispanic/Latino, 20%; white, 9%). In multivariate models, having experienced racial/ethnic discrimination at work was associated with high job turnover [adjusted odes ratio, 2.7; 95% CI, 1.4–4.9]. Among physicians who experienced work-place discrimination, only 45% of physicians were satisfied with their careers (vs 88% among those who had not experienced workplace discrimination, p value < .01], and 40% were con-templating a career change (vs 10% among those who had not experienced workplace discrimination, p value < .001). Conclusion Workplace discrimination is associated with physician job turnover, career dissatisfaction, and contemplation of career change. These findings underscore the importance of monitoring for workplace discrimination and responding when opportunities for intervention and retention still exist. PMID:20070016

Forgotten marriages? Measuring the reliability of marriage histories

PubMed Central

Chae, Sophia

2016-01-01

BACKGROUND Marriage histories are a valuable data source for investigating nuptiality. While researchers typically acknowledge the problems associated with their use, it is unknown to what extent these problems occur and how marriage analyses are affected. OBJECTIVE This paper seeks to investigate the quality of marriage histories by measuring levels of misreporting, examining the characteristics associated with misreporting, and assessing whether misreporting biases marriage indicators. METHODS Using data from the Malawi Longitudinal Study of Families and Health (MLSFH), I compare marriage histories reported by the same respondents at two different points in time. I investigate whether respondents consistently report their spouses (by name), status of marriage, and dates of marriage. I use multivariate regression models to investigate the characteristics associated with misreporting. Finally, I examine whether misreporting marriages and marriage dates affects marriage indicators. RESULTS Results indicate that 28.3% of men and 17.9% of women omitted at least one marriage in one of the survey waves. Multivariate regression models show that misreporting is not random: marriage, individual, interviewer, and survey characteristics are associated with marriage omission and marriage date inconsistencies. Misreporting also affects marriage indicators. CONCLUSIONS This is the first study of its kind to examine the reliability of marriage histories collected in the context of Sub-Saharan Africa. Although marriage histories are frequently used to study marriage dynamics, until now no knowledge has existed on the degree of misreporting. Misreporting in marriage histories is shown to be non-negligent and could potentially affect analyses. PMID:27152090

Factors Impacting Growth in Infants with Single Ventricle Physiology: A Report from Pediatric Heart Network Infant Single Ventricle Trial

PubMed Central

Williams, Richard V.; Zak, Victor; Ravishankar, Chitra; Altmann, Karen; Anderson, Jeffrey; Atz, Andrew M.; Dunbar-Masterson, Carolyn; Ghanayem, Nancy; Lambert, Linda; Lurito, Karen; Medoff-Cooper, Barbara; Margossian, Renee; Pemberton, Victoria L.; Russell, Jennifer; Stylianou, Mario; Hsu, Daphne

2011-01-01

Objectives To describe growth patterns in infants with single ventricle physiology and determine factors influencing growth. Study design Data from 230 subjects enrolled in the Pediatric Heart Network Infant Single Ventricle Enalapril Trial were used to assess factors influencing change in weight-for-age z-score (Δz) from study enrollment (0.7 ± 0.4 months) to pre-superior cavopulmonary connection (SCPC) (5.1 ± 1.8 months, period 1), and pre-SCPC to final study visit (14.1 ± 0.9 months, period 2). Predictor variables included patient characteristics, feeding regimen, clinical center, and medical factors during neonatal (period 1) and SCPC hospitalizations (period 2). Univariate regression analysis was performed, followed by backward stepwise regression and bootstrapping reliability to inform a final multivariable model. Results Weights were available for 197/230 subjects for period 1 and 173/197 for period 2. For period 1, greater gestational age, younger age at study enrollment, tube feeding at neonatal discharge, and clinical center were associated with a greater negative Δz (poorer growth) in multivariable modeling (adjusted R2 = 0.39, p < 0.001). For period 2, younger age at SCPC and greater daily caloric intake were associated with greater positive Δz (better growth) (R2 = 0.10, p = 0.002). Conclusions Aggressive nutritional support and earlier SCPC are modifiable factors associated with a favorable change in weight-for-age z-score. PMID:21784436

A Multivariate Twin Study of the DSM-IV Criteria for Antisocial Personality Disorder

PubMed Central

Kendler, Kenneth S.; Aggen, Steven H.; Patrick, Christopher J.

2012-01-01

BACKGROUND Many assessment instruments for psychopathy are multidimensional, suggesting that distinguishable factors are needed to effectively capture variation in this personality domain. However, no prior study has examined the factor structure of the DSM-IV criteria for antisocial personality disorder (ASPD). METHODS Self-report questionnaire items reflecting all A criteria for DSM-IV ASPD were available from 4,291 twins (including both members of 1,647 pairs) from the Virginia Adult Study of Psychiatric and Substance Use Disorders. Exploratory factor analysis and twin model fitting were performed using, respectively, Mplus and Mx. RESULTS Phenotypic factor analysis produced evidence for 2 correlated factors: aggressive-disregard and disinhibition. The best-fitting multivariate twin model included two genetic and one unique environmental common factor, along with criteria-specific genetic and environmental effects. The two genetic factors closely resembled the phenotypic factors and varied in their prediction of a range of relevant criterion variables. Scores on the genetic aggressive-disregard factor score were more strongly associated with risk for conduct disorder, early and heavy alcohol use, and low educational status, whereas scores on the genetic disinhibition factor score were more strongly associated with younger age, novelty seeking, and major depression. CONCLUSION From a genetic perspective, the DSM-IV criteria for ASPD do not reflect a single dimension of liability but rather are influenced by two dimensions of genetic risk reflecting aggressive-disregard and disinhibition. The phenotypic structure of the ASPD criteria results largely from genetic and not from environmental influences. PMID:21762879

Survival in Alzheimer disease

PubMed Central

Helzner, E P.; Scarmeas, N; Cosentino, S; Tang, M X.; Schupf, N; Stern, Y

2008-01-01

Objective: To describe factors associated with survival in Alzheimer disease (AD) in a multiethnic, population-based longitudinal study. Methods: AD cases were identified in the Washington Heights Inwood Columbia Aging Project, a longitudinal, community-based study of cognitive aging in Northern Manhattan. The sample comprised 323 participants who were initially dementia-free but developed AD during study follow-up (incident cases). Participants were followed for an average of 4.1 (up to 12.6) years. Possible factors associated with shorter lifespan were assessed using Cox proportional hazards models with attained age as the time to event (time from birth to death or last follow-up). In subanalyses, median postdiagnosis survival durations were estimated using postdiagnosis study follow-up as the timescale. Results: The mortality rate was 10.7 per 100 person-years. Mortality rates were higher among those diagnosed at older ages, and among Hispanics compared to non-Hispanic whites. The median lifespan of the entire sample was 92.2 years (95% CI: 90.3, 94.1). In a multivariable-adjusted Cox model, history of diabetes and history of hypertension were independently associated with a shorter lifespan. No differences in lifespan were seen by race/ethnicity after multivariable adjustment. The median postdiagnosis survival duration was 3.7 years among non-Hispanic whites, 4.8 years among African Americans, and 7.6 years among Hispanics. Conclusion: Factors influencing survival in Alzheimer disease include race/ethnicity and comorbid diabetes and hypertension. GLOSSARY AD = Alzheimer disease; NDI = National Death Index; WHICAP = Washington Heights Inwood Columbia Aging Project. PMID:18981370

Determinants of workplace injury among Thai Cohort Study participants

PubMed Central

Berecki-Gisolf, Janneke; Tawatsupa, Benjawan; McClure, Roderick; Seubsman, Sam-ang; Sleigh, Adrian

2013-01-01

Objectives To explore individual determinants of workplace injury among Thai workers. Design Cross-sectional analysis of a large national cohort. Setting Thailand. Participants Thai Cohort Study participants who responded to the 2009 follow-up survey were included if they reported doing paid work or being self-employed (n=51 751). Outcome measures Self-reported injury incidence over the past 12 months was calculated. Multivariate logistic regression models were used to test associations between individual determinants and self-reported workplace injury. Results Workplace injuries were reported by 1317 study participants (2.5%); the incidence was 34 (95% CI 32 to 36)/1000 worker-years for men, and 18 (17–20) for women. Among men working ≥41 h and earning <10 000 Baht, the injury rate was four times higher compared with men working <11 h and earning ≥20 001 Baht; differences in injury rates were less pronounced in women. Multivariate modelling showed that working ≥49 h/week (23%) and working for ≤10 000 Bath/month (37%) were associated with workplace injury. The increase in injury risk with increased working hours did not exceed the risk expected from increased exposure. Conclusions Reductions in occupational injury rates could be achieved by limiting working hours to 48/week. Particularly for Thai low wage earners and those with longer workdays, there is a need for effective injury preventive programmes. PMID:23869104

Bayesian transformation cure frailty models with multivariate failure time data.

PubMed

Yin, Guosheng

2008-12-10

We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.

Modeling the Risk of Radiation-Induced Acute Esophagitis for Combined Washington University and RTOG Trial 93-11 Lung Cancer Patients

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

Huang, Ellen X.; Bradley, Jeffrey D.; El Naqa, Issam

2012-04-01

Purpose: To construct a maximally predictive model of the risk of severe acute esophagitis (AE) for patients who receive definitive radiation therapy (RT) for non-small-cell lung cancer. Methods and Materials: The dataset includes Washington University and RTOG 93-11 clinical trial data (events/patients: 120/374, WUSTL = 101/237, RTOG9311 = 19/137). Statistical model building was performed based on dosimetric and clinical parameters (patient age, sex, weight loss, pretreatment chemotherapy, concurrent chemotherapy, fraction size). A wide range of dose-volume parameters were extracted from dearchived treatment plans, including Dx, Vx, MOHx (mean of hottest x% volume), MOCx (mean of coldest x% volume), and gEUDmore » (generalized equivalent uniform dose) values. Results: The most significant single parameters for predicting acute esophagitis (RTOG Grade 2 or greater) were MOH85, mean esophagus dose (MED), and V30. A superior-inferior weighted dose-center position was derived but not found to be significant. Fraction size was found to be significant on univariate logistic analysis (Spearman R = 0.421, p < 0.00001) but not multivariate logistic modeling. Cross-validation model building was used to determine that an optimal model size needed only two parameters (MOH85 and concurrent chemotherapy, robustly selected on bootstrap model-rebuilding). Mean esophagus dose (MED) is preferred instead of MOH85, as it gives nearly the same statistical performance and is easier to compute. AE risk is given as a logistic function of (0.0688 Asterisk-Operator MED+1.50 Asterisk-Operator ConChemo-3.13), where MED is in Gy and ConChemo is either 1 (yes) if concurrent chemotherapy was given, or 0 (no). This model correlates to the observed risk of AE with a Spearman coefficient of 0.629 (p < 0.000001). Conclusions: Multivariate statistical model building with cross-validation suggests that a two-variable logistic model based on mean dose and the use of concurrent chemotherapy robustly predicts acute esophagitis risk in combined-data WUSTL and RTOG 93-11 trial datasets.« less

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

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

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Clostridium Difficile Infection Due to Pneumonia Treatment: Mortality Risk Models.

PubMed

Chmielewska, M; Zycinska, K; Lenartowicz, B; Hadzik-Błaszczyk, M; Cieplak, M; Kur, Z; Wardyn, K A

2017-01-01

One of the most common gastrointestinal infection after the antibiotic treatment of community or nosocomial pneumonia is caused by the anaerobic spore Clostridium difficile (C. difficile). The aim of this study was to retrospectively assess mortality due to C. difficile infection (CDI) in patients treated for pneumonia. We identified 94 cases of post-pneumonia CDI out of the 217 patients with CDI. The mortality issue was addressed by creating a mortality risk models using logistic regression and multivariate fractional polynomial analysis. The patients' demographics, clinical features, and laboratory results were taken into consideration. To estimate the influence of the preceding respiratory infection, a pneumonia severity scale was included in the analysis. The analysis showed two statistically significant and clinically relevant mortality models. The model with the highest prognostic strength entailed age, leukocyte count, serum creatinine and urea concentration, hematocrit, coexisting neoplasia or chronic obstructive pulmonary disease. In conclusion, we report on two prognostic models, based on clinically relevant factors, which can be of help in predicting mortality risk in C. difficile infection, secondary to the antibiotic treatment of pneumonia. These models could be useful in preventive tailoring of individual therapy.

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

PubMed

Yang, James J; Williams, L Keoki; Buu, Anne

2017-08-24

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Cross-country transferability of multi-variable damage models

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

2017-04-01

Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

PubMed Central

Poisson, Laila M.; Gutman, David; Scarpace, Lisa; Hwang, Scott N.; Holder, Chad A.; Wintermark, Max; Rao, Arvind; Colen, Rivka R.; Kirby, Justin; Freymann, John; Jaffe, C. Carl; Mikkelsen, Tom; Flanders, Adam

2014-01-01

Purpose To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Materials and Methods An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material–enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Results Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49–1.79 years). Conclusion Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features. © RSNA, 2014 Online supplemental material is available for this article. PMID:24646147

A Combined Approach of Infrared Spectroscopy and Multivariate Analysis for the Simultaneous Determination of Sugars and Fructans in Strawberry Juices During Storage.

PubMed

Cassani, Lucía; Santos, Mauricio; Gerbino, Esteban; Del Rosario Moreira, María; Gómez-Zavaglia, Andrea

2018-03-01

In this work, a Fourier transform mid-infrared spectroscopy (FTIR)-based method was developed for simultaneously quantifying simple sugars and exogenously added fructooligosaccharides (FOS) in strawberry juices preserved for up to 14 d using nonthermal techniques (geraniol and vanillin+ultrasound). The main spectral differences were observed in the 1200 to 900 cm -1 region. The presence of FOS was identified by the typical bands at 1134, 1034, and 935 cm -1 . During storage, a significant decrease of sucrose was concomitant to an increase of glucose and fructose in juices stored without any previous preservation treatment, as determined by high-performance liquid chromatography (HPLC). A principal component analysis was performed on the FTIR spectra corresponding to the different treatments. The groups observed explained more than 94% of the variance and were related to changes in the carbohydrate composition during storage. Then, different partial least square models (PLS) were defined to determine the concentrations of glucose, sucrose, fructose, and those of exogenously added FOS with degrees of polymerization within 3 and 5. The carbohydrates' concentrations determined by HPLC were used as reference method. The models were validated with independent sets of data. The mean of predicted values fitted nicely those obtained by HPLC (correlation and R 2  > 0.97), thus supporting the use of the PLS models to monitor the quality of strawberry juices in unknown samples. In conclusion, FTIR spectroscopy appears as an adequate analytical tool to quick assess whether juice formulations meet specifications in terms of authenticity, contamination and/or deterioration. FTIR spectroscopy provided a method potentially transferable to the food industry when associated with the multivariate analysis. The robust 21 PLS models defined in this work provided reliable tools for the rapid monitoring of juices' authenticity and/or deterioration. In this regard, FTIR associated to multivariate analysis enabled the determination of different sugars in a single measurement without the need of pure sugars as standards. This experimental simplicity supports the use of FTIR at the production line, and also contributes to save time in determining carbohydrates' composition and stability, in an environmentally friendly way. © 2017 Institute of Food Technologists®.

Accumulated Delivered Dose Response of Stereotactic Body Radiation Therapy for Liver Metastases

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

Swaminath, Anand; Massey, Christine; Brierley, James D.

2015-11-01

Purpose: To determine whether the accumulated dose using image guided radiation therapy is a stronger predictor of clinical outcomes than the planned dose in stereotactic body radiation therapy (SBRT) for liver metastases. Methods and Materials: From 2003 to 2009, 81 patients with 142 metastases were treated in institutional review board–approved SBRT studies (5-10 fractions). Patients were treated during free breathing (with or without abdominal compression) or with controlled exhale breath-holding. SBRT was planned on a static exhale computed tomography (CT) scan, and the minimum planning target volume dose to 0.5 cm{sup 3} (minPTV) was recorded. The accumulated minimum dose to themore » 0.5 cm{sup 3} gross tumor volume (accGTV) was calculated after performing dose accumulation from exported image guided radiation therapy data sets registered to the planning CT using rigid (2-dimensional MV/kV orthogonal) or deformable (3-dimensional/4-dimensional cone beam CT) image registration. Univariate and multivariate Cox regression models assessed the factors influencing the time to local progression (TTLP). Hazard ratios for accGTV and minPTV were compared using model goodness-of-fit and bootstrapping. Results: Overall, the accGTV dose exceeded the minPTV dose in 98% of the lesions. For 5 to 6 fractions, accGTV doses of >45 Gy were associated with 1-year local control of 86%. On univariate analysis, the cancer subtype (breast), smaller tumor volume, and increased dose were significant predictors for improved TTLP. The dose and volume were uncorrelated; the accGTV dose and minPTV dose were correlated and were tested separately on multivariate models. Breast cancer subtype, accGTV dose (P<.001), and minPTV dose (P=.02) retained significance in the multivariate models. The univariate hazard ratio for TTLP for 5-Gy increases in accGTV versus minPTV was 0.67 versus 0.74 (all patients; 95% confidence interval of difference 0.03-0.14). Goodness-of-fit testing confirmed the accGTV dose as a stronger dose–response predictor than the minPTV dose. Conclusions: The accGTV dose is a better predictor of TTLP than the minPTV dose for liver metastasis SBRT. The use of modern image guided radiation therapy in future analyses of dose–response outcomes should increase the concordance between the planned and delivered doses.« less

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

PubMed Central

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

PubMed

Belay, T K; Dagnachew, B S; Kowalski, Z M; Ådnøy, T

2017-08-01

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

A multivariate mixed model system for wood specific gravity and moisture content of planted loblolly pine stands in the southern United States

Treesearch

Finto Antony; Laurence R. Schimleck; Alex Clark; Richard F. Daniels

2012-01-01

Specific gravity (SG) and moisture content (MC) both have a strong influence on the quantity and quality of wood fiber. We proposed a multivariate mixed model system to model the two properties simultaneously. Disk SG and MC at different height levels were measured from 3 trees in 135 stands across the natural range of loblolly pine and the stand level values were used...

Multivariate curve resolution-alternating least squares and kinetic modeling applied to near-infrared data from curing reactions of epoxy resins: mechanistic approach and estimation of kinetic rate constants.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2006-02-01

This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.

Back Pain Prevalence and Its Associated Factors in Brazilian Athletes from Public High Schools: A Cross-Sectional Study.

PubMed

Noll, Matias; de Avelar, Ivan Silveira; Lehnen, Georgia Cristina; Vieira, Marcus Fraga

2016-01-01

Most studies on the prevalence of back pain have evaluated it in developed countries (Human Development Index--HDI > 0.808), and their conclusions may not hold for developing countries. The aim of this study was to identify the prevalence of back pain in representative Brazilian athletes from public high schools. This cross-sectional study was performed during the state phase of the 2015 Jogos dos Institutos Federais (JIF), or Federal Institutes Games, in Brazil (HDI = 0.744), and it enrolled 251 athletes, 173 males and 78 females (14-20 years old). The dependent variable was back pain, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, exercise-level, anthropometric, strength, behavioral, and postural factors. The prevalence ratio (PR) was calculated using multivariable analysis according to the Poisson regression model (α = 0.05). The prevalence of back pain in the three months prior to the study was 43.7% (n = 104), and 26% of the athletes reported feeling back pain only once. Multivariable analysis showed that back pain was associated with demographic (sex), psychosocial (loneliness and loss of sleep in the previous year), hereditary (ethnicity, parental back pain), strength (lumbar and hand forces), anthropometric (body mass index), behavioral (sleeping time per night, reading and studying in bed, smoking habits in the previous month), and postural (sitting posture while writing, while on a bench, and while using a computer) variables. Participants who recorded higher levels of lumbar and manual forces reported a lower prevalence of back pain (PR < 0.79), whereas feeling lonely in the previous year, obesity, and ethnicity exhibited the highest prevalence ratio (PR > 1.30). In conclusion, there is no association between exercise levels and back pain but there is an association between back pain and non-exercise related variables.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

Synthesis of a control model for a liquid nitrogen cooled, closed circuit, cryogenic nitrogen wind tunnel and its validation

NASA Technical Reports Server (NTRS)

Balakrishna, S.; Goglia, G. L.

1979-01-01

The details of the efforts to synthesize a control-compatible multivariable model of a liquid nitrogen cooled, gaseous nitrogen operated, closed circuit, cryogenic pressure tunnel are presented. The synthesized model was transformed into a real-time cryogenic tunnel simulator, and this model is validated by comparing the model responses to the actual tunnel responses of the 0.3 m transonic cryogenic tunnel, using the quasi-steady-state and the transient responses of the model and the tunnel. The global nature of the simple, explicit, lumped multivariable model of a closed circuit cryogenic tunnel is demonstrated.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

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

A 45-Second Self-Test for Cardiorespiratory Fitness: Heart Rate-Based Estimation in Healthy Individuals

PubMed Central

Bonato, Matteo; Papini, Gabriele; Bosio, Andrea; Mohammed, Rahil A.; Bonomi, Alberto G.; Moore, Jonathan P.; Merati, Giampiero; La Torre, Antonio; Kubis, Hans-Peter

2016-01-01

Cardio-respiratory fitness (CRF) is a widespread essential indicator in Sports Science as well as in Sports Medicine. This study aimed to develop and validate a prediction model for CRF based on a 45 second self-test, which can be conducted anywhere. Criterion validity, test re-test study was set up to accomplish our objectives. Data from 81 healthy volunteers (age: 29 ± 8 years, BMI: 24.0 ± 2.9), 18 of whom females, were used to validate this test against gold standard. Nineteen volunteers repeated this test twice in order to evaluate its repeatability. CRF estimation models were developed using heart rate (HR) features extracted from the resting, exercise, and the recovery phase. The most predictive HR feature was the intercept of the linear equation fitting the HR values during the recovery phase normalized for the height2 (r2 = 0.30). The Ruffier-Dickson Index (RDI), which was originally developed for this squat test, showed a negative significant correlation with CRF (r = -0.40), but explained only 15% of the variability in CRF. A multivariate model based on RDI and sex, age and height increased the explained variability up to 53% with a cross validation (CV) error of 0.532 L ∙ min-1 and substantial repeatability (ICC = 0.91). The best predictive multivariate model made use of the linear intercept of HR at the beginning of the recovery normalized for height2 and age2; this had an adjusted r2 = 0. 59, a CV error of 0.495 L·min-1 and substantial repeatability (ICC = 0.93). It also had a higher agreement in classifying CRF levels (κ = 0.42) than RDI-based model (κ = 0.29). In conclusion, this simple 45 s self-test can be used to estimate and classify CRF in healthy individuals with moderate accuracy and large repeatability when HR recovery features are included. PMID:27959935

A 45-Second Self-Test for Cardiorespiratory Fitness: Heart Rate-Based Estimation in Healthy Individuals.

PubMed

Sartor, Francesco; Bonato, Matteo; Papini, Gabriele; Bosio, Andrea; Mohammed, Rahil A; Bonomi, Alberto G; Moore, Jonathan P; Merati, Giampiero; La Torre, Antonio; Kubis, Hans-Peter

2016-01-01

Cardio-respiratory fitness (CRF) is a widespread essential indicator in Sports Science as well as in Sports Medicine. This study aimed to develop and validate a prediction model for CRF based on a 45 second self-test, which can be conducted anywhere. Criterion validity, test re-test study was set up to accomplish our objectives. Data from 81 healthy volunteers (age: 29 ± 8 years, BMI: 24.0 ± 2.9), 18 of whom females, were used to validate this test against gold standard. Nineteen volunteers repeated this test twice in order to evaluate its repeatability. CRF estimation models were developed using heart rate (HR) features extracted from the resting, exercise, and the recovery phase. The most predictive HR feature was the intercept of the linear equation fitting the HR values during the recovery phase normalized for the height2 (r2 = 0.30). The Ruffier-Dickson Index (RDI), which was originally developed for this squat test, showed a negative significant correlation with CRF (r = -0.40), but explained only 15% of the variability in CRF. A multivariate model based on RDI and sex, age and height increased the explained variability up to 53% with a cross validation (CV) error of 0.532 L ∙ min-1 and substantial repeatability (ICC = 0.91). The best predictive multivariate model made use of the linear intercept of HR at the beginning of the recovery normalized for height2 and age2; this had an adjusted r2 = 0. 59, a CV error of 0.495 L·min-1 and substantial repeatability (ICC = 0.93). It also had a higher agreement in classifying CRF levels (κ = 0.42) than RDI-based model (κ = 0.29). In conclusion, this simple 45 s self-test can be used to estimate and classify CRF in healthy individuals with moderate accuracy and large repeatability when HR recovery features are included.

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

Sanchez-Nieto, Beatriz, E-mail: bsanchez@fis.puc.cl; Goset, Karen C.; Caviedes, Ivan

Purpose: To propose multivariate predictive models for changes in pulmonary function tests ({Delta}PFTs) with respect to preradiotherapy (pre-RT) values in patients undergoing RT for breast cancer and lymphoma. Methods and Materials: A prospective study was designed to measure {Delta}PFTs of patients undergoing RT. Sixty-six patients were included. Spirometry, lung capacity (measured by helium dilution), and diffusing capacity of carbon monoxide tests were used to measure lung function. Two lung definitions were considered: paired lung vs. irradiated lung (IL). Correlation analysis of dosimetric parameters (mean lung dose and the percentage of lung volume receiving more than a threshold dose) and {Delta}PFTsmore » was carried out to find the best dosimetric predictor. Chemotherapy, age, smoking, and the selected dose-volume parameter were considered as single and interaction terms in a multivariate analysis. Stability of results was checked by bootstrapping. Results: Both lung definitions proved to be similar. Modeling was carried out for IL. Acute and late damage showed the highest correlations with volumes irradiated above {approx}20 Gy (maximum R{sup 2} = 0.28) and {approx}40 Gy (maximum R{sup 2} = 0.21), respectively. RT alone induced a minor and transitory restrictive defect (p = 0.013). Doxorubicin-cyclophosphamide-paclitaxel (Taxol), when administered pre-RT, induced a late, large restrictive effect, independent of RT (p = 0.031). Bootstrap values confirmed the results. Conclusions: None of the dose-volume parameters was a perfect predictor of outcome. Thus, different predictor models for {Delta}PFTs were derived for the IL, which incorporated other nondosimetric parameters mainly through interaction terms. Late {Delta}PFTs seem to behave more serially than early ones. Large restrictive defects were demonstrated in patients pretreated with doxorubicin-cyclophosphamide-paclitaxel.« less

Traumatic brain injury in England and Wales: prospective audit of epidemiology, complications and standardised mortality

PubMed Central

Lawrence, T; Bouamra, O; Woodford, M; Lecky, F; Hutchinson, P J

2016-01-01

Objectives To provide a comprehensive assessment of the management of traumatic brain injury (TBI) relating to epidemiology, complications and standardised mortality across specialist units. Design The Trauma Audit and Research Network collects data prospectively on patients suffering trauma across England and Wales. We analysed all data collected on patients with TBI between April 2014 and June 2015. Setting Data were collected on patients presenting to emergency departments across 187 hospitals including 26 with specialist neurosurgical services, incorporating factors previously identified in the Ps14 multivariate logistic regression (Ps14n) model multivariate TBI outcome prediction model. The frequency and timing of secondary transfer to neurosurgical centres was assessed. Results We identified 15 820 patients with TBI presenting to neurosurgical centres directly (6258), transferred from a district hospital to a neurosurgical centre (3682) and remaining in a district general hospital (5880). The commonest mechanisms of injury were falls in the elderly and road traffic collisions in the young, which were more likely to present in coma. In severe TBI (Glasgow Coma Score (GCS) ≤8), the median time from admission to imaging with CT scan is 0.5 hours. Median time to craniotomy from admission is 2.6 hours and median time to intracranial pressure monitoring is 3 hours. The most frequently documented complication of severe TBI is bronchopneumonia in 5% of patients. Risk-adjusted W scores derived from the Ps14n model indicate that no neurosurgical unit fell outside the 3 SD limits on a funnel plot. Conclusions We provide the first comprehensive report of the management of TBI in England and Wales, including data from all neurosurgical units. These data provide transparency and suggests equity of access to high-quality TBI management provided in England and Wales. PMID:27884843

Predictors of Arterial Blood Pressure Control During Deliberate Hypotension with Sodium Nitroprusside in Children

PubMed Central

Spielberg, David R; Barrett, Jeffrey S; Hammer, Gregory B; Drover, David R; Reece, Tammy; Cohane, Carol A; Schulman, Scott R

2014-01-01

Background Sodium nitroprusside (SNP) is used to decrease arterial blood pressure (BP) during certain surgical procedures. There are limited data regarding efficacy of BP control with SNP. There are no data on patient and clinician factors that affect BP control. We evaluated the dose-response relationship of SNP in infants and children undergoing major surgery and performed a quantitative assessment of BP control. Methods One hundred fifty-three subjects at 7 sites received a blinded infusion followed by open-label SNP during operative procedures requiring controlled hypotension. SNP was administered by continuous infusion and titrated to maintain BP control (mean arterial BP [MAP] within ±10% of clinician-defined target). BP was recorded using an arterial catheter. Statistical Process Control methodology was used to quantify BP control. A multivariable model assessed the effects of patient and procedural factors. Results BP was controlled an average 45.4% (SD 23.9%, 95% CI 41.5%-49.18%) of the time. Larger changes in infusion rate were associated with worse BP control (7.99% less control for 1 mcg•kg−•min− increase in average titration size, p=0.0009). A larger difference between a patient's baseline and target MAP predicted worse BP control (0.93% worse control per 1 mmHg increase in MAP difference, p=0.0013). Both effects persisted in multivariable models. Conclusions : SNP was effective in reducing BP. However, BP was within the target range less than half of the time. No clinician or patient factors were predictive of BP control, although two inverse relationships were identified. These relationships require additional study and may be best coupled with exposure-response modeling to propose improved dosing strategies when using SNP for controlled hypotension in the pediatric population. PMID:25099924

Impact of Colic Pain as a Significant Factor for Predicting the Stone Free Rate of One-Session Shock Wave Lithotripsy for Treating Ureter Stones: A Bayesian Logistic Regression Model Analysis

PubMed Central

Chung, Doo Yong; Cho, Kang Su; Lee, Dae Hun; Han, Jang Hee; Kang, Dong Hyuk; Jung, Hae Do; Kown, Jong Kyou; Ham, Won Sik; Choi, Young Deuk; Lee, Joo Yong

2015-01-01

Purpose This study was conducted to evaluate colic pain as a prognostic pretreatment factor that can influence ureter stone clearance and to estimate the probability of stone-free status in shock wave lithotripsy (SWL) patients with a ureter stone. Materials and Methods We retrospectively reviewed the medical records of 1,418 patients who underwent their first SWL between 2005 and 2013. Among these patients, 551 had a ureter stone measuring 4–20 mm and were thus eligible for our analyses. The colic pain as the chief complaint was defined as either subjective flank pain during history taking and physical examination. Propensity-scores for established for colic pain was calculated for each patient using multivariate logistic regression based upon the following covariates: age, maximal stone length (MSL), and mean stone density (MSD). Each factor was evaluated as predictor for stone-free status by Bayesian and non-Bayesian logistic regression model. Results After propensity-score matching, 217 patients were extracted in each group from the total patient cohort. There were no statistical differences in variables used in propensity- score matching. One-session success and stone-free rate were also higher in the painful group (73.7% and 71.0%, respectively) than in the painless group (63.6% and 60.4%, respectively). In multivariate non-Bayesian and Bayesian logistic regression models, a painful stone, shorter MSL, and lower MSD were significant factors for one-session stone-free status in patients who underwent SWL. Conclusions Colic pain in patients with ureter calculi was one of the significant predicting factors including MSL and MSD for one-session stone-free status of SWL. PMID:25902059

A 10-Year Retrospective Review of a Nonrandomized Cohort of 458 Patients Undergoing Radical Radiotherapy or Cystectomy in Yorkshire, UK

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

Munro, Nicholas P., E-mail: nic@munron.plus.co; Sundaram, Subramnian K.; Weston, Philip

2010-05-01

Purpose: We have previously reported on the mortality, morbidity, and 5-year survival of 458 patients who underwent radical radiotherapy or surgery for invasive bladder cancer in Yorkshire from 1993 to 1996. We aim to present the 10-year outcomes of these patients and to reassess factors predicting survival. Methods and Materials: The Northern and Yorkshire Cancer Registry identified 458 patients whose cases were subjected to Kaplan-Meier all-cause survival analyses, and a retrospective casenote analysis was undertaken on 398 (87%) for univariate and multivariate Cox proportional hazards modeling. Additional proportional hazards regression modeling was used to assess the statistical significance of variablesmore » on overall survival. Results: The ratio of radiotherapy to cystectomy was 3:1. There was no significant difference in overall 10-year survival between those who underwent radiotherapy (22%) and radical cystectomy (24%). Univariate analyses suggested that female sex, performance status, hydronephrosis and clinical T stage, were associated with an inferior outcome at 10 years. Patient age, tumor grade, treatment delay, and caseload factors were not significant. Multivariate analysis models were created for 0-2 and 2-10 years after treatment. There were no significant differences in treatment for 0-2 years; however, after 2 years follow-up there was some evidence of increased survival for patients receiving surgery compared with radiotherapy (hazard ratio 0.66, 95% confidence interval: 0.44-1.01, p = 0.06). Conclusions: a 10-year minimum follow-up has rarely been reported after radical treatment for invasive bladder cancer. At 10 years, there was no statistical difference in all-cause survival between surgery and radiotherapy treatment modalities.« less

Cognitive Complaints After Breast Cancer Treatments: Examining the Relationship With Neuropsychological Test Performance

PubMed Central

2013-01-01

Background Cognitive complaints are reported frequently after breast cancer treatments. Their association with neuropsychological (NP) test performance is not well-established. Methods Early-stage, posttreatment breast cancer patients were enrolled in a prospective, longitudinal, cohort study prior to starting endocrine therapy. Evaluation included an NP test battery and self-report questionnaires assessing symptoms, including cognitive complaints. Multivariable regression models assessed associations among cognitive complaints, mood, treatment exposures, and NP test performance. Results One hundred eighty-nine breast cancer patients, aged 21–65 years, completed the evaluation; 23.3% endorsed higher memory complaints and 19.0% reported higher executive function complaints (>1 SD above the mean for healthy control sample). Regression modeling demonstrated a statistically significant association of higher memory complaints with combined chemotherapy and radiation treatments (P = .01), poorer NP verbal memory performance (P = .02), and higher depressive symptoms (P < .001), controlling for age and IQ. For executive functioning complaints, multivariable modeling controlling for age, IQ, and other confounds demonstrated statistically significant associations with better NP visual memory performance (P = .03) and higher depressive symptoms (P < .001), whereas combined chemotherapy and radiation treatment (P = .05) approached statistical significance. Conclusions About one in five post–adjuvant treatment breast cancer patients had elevated memory and/or executive function complaints that were statistically significantly associated with domain-specific NP test performances and depressive symptoms; combined chemotherapy and radiation treatment was also statistically significantly associated with memory complaints. These results and other emerging studies suggest that subjective cognitive complaints in part reflect objective NP performance, although their etiology and biology appear to be multifactorial, motivating further transdisciplinary research. PMID:23606729

Neuroimaging and Neurodevelopmental Outcome in Extremely Preterm Infants

PubMed Central

Barnes, Patrick D.; Bulas, Dorothy; Slovis, Thomas L.; Finer, Neil N.; Wrage, Lisa A.; Das, Abhik; Tyson, Jon E.; Stevenson, David K.; Carlo, Waldemar A.; Walsh, Michele C.; Laptook, Abbot R.; Yoder, Bradley A.; Van Meurs, Krisa P.; Faix, Roger G.; Rich, Wade; Newman, Nancy S.; Cheng, Helen; Heyne, Roy J.; Vohr, Betty R.; Acarregui, Michael J.; Vaucher, Yvonne E.; Pappas, Athina; Peralta-Carcelen, Myriam; Wilson-Costello, Deanne E.; Evans, Patricia W.; Goldstein, Ricki F.; Myers, Gary J.; Poindexter, Brenda B.; McGowan, Elisabeth C.; Adams-Chapman, Ira; Fuller, Janell; Higgins, Rosemary D.

2015-01-01

BACKGROUND: Extremely preterm infants are at risk for neurodevelopmental impairment (NDI). Early cranial ultrasound (CUS) is usual practice, but near-term brain MRI has been reported to better predict outcomes. We prospectively evaluated MRI white matter abnormality (WMA) and cerebellar lesions, and serial CUS adverse findings as predictors of outcomes at 18 to 22 months’ corrected age. METHODS: Early and late CUS, and brain MRI were read by masked central readers, in a large cohort (n = 480) of infants <28 weeks’ gestation surviving to near term in the Neonatal Research Network. Outcomes included NDI or death after neuroimaging, and significant gross motor impairment or death, with NDI defined as cognitive composite score <70, significant gross motor impairment, and severe hearing or visual impairment. Multivariable models evaluated the relative predictive value of neuroimaging while controlling for other factors. RESULTS: Of 480 infants, 15 died and 20 were lost. Increasing severity of WMA and significant cerebellar lesions on MRI were associated with adverse outcomes. Cerebellar lesions were rarely identified by CUS. In full multivariable models, both late CUS and MRI, but not early CUS, remained independently associated with NDI or death (MRI cerebellar lesions: odds ratio, 3.0 [95% confidence interval: 1.3–6.8]; late CUS: odds ratio, 9.8 [95% confidence interval: 2.8–35]), and significant gross motor impairment or death. In models that did not include late CUS, MRI moderate-severe WMA was independently associated with adverse outcomes. CONCLUSIONS: Both late CUS and near-term MRI abnormalities were associated with outcomes, independent of early CUS and other factors, underscoring the relative prognostic value of near-term neuroimaging. PMID:25554820

Retinal nerve fibre layer thinning is associated with drug resistance in epilepsy

PubMed Central

Balestrini, Simona; Clayton, Lisa M S; Bartmann, Ana P; Chinthapalli, Krishna; Novy, Jan; Coppola, Antonietta; Wandschneider, Britta; Stern, William M; Acheson, James; Bell, Gail S; Sander, Josemir W; Sisodiya, Sanjay M

2016-01-01

Objective Retinal nerve fibre layer (RNFL) thickness is related to the axonal anterior visual pathway and is considered a marker of overall white matter ‘integrity’. We hypothesised that RNFL changes would occur in people with epilepsy, independently of vigabatrin exposure, and be related to clinical characteristics of epilepsy. Methods Three hundred people with epilepsy attending specialist clinics and 90 healthy controls were included in this cross-sectional cohort study. RNFL imaging was performed using spectral-domain optical coherence tomography (OCT). Drug resistance was defined as failure of adequate trials of two antiepileptic drugs to achieve sustained seizure freedom. Results The average RNFL thickness and the thickness of each of the 90° quadrants were significantly thinner in people with epilepsy than healthy controls (p<0.001, t test). In a multivariate logistic regression model, drug resistance was the only significant predictor of abnormal RNFL thinning (OR=2.09, 95% CI 1.09 to 4.01, p=0.03). Duration of epilepsy (coefficient −0.16, p=0.004) and presence of intellectual disability (coefficient −4.0, p=0.044) also showed a significant relationship with RNFL thinning in a multivariate linear regression model. Conclusions Our results suggest that people with epilepsy with no previous exposure to vigabatrin have a significantly thinner RNFL than healthy participants. Drug resistance emerged as a significant independent predictor of RNFL borderline attenuation or abnormal thinning in a logistic regression model. As this is easily assessed by OCT, RNFL thickness might be used to better understand the mechanisms underlying drug resistance, and possibly severity. Longitudinal studies are needed to confirm our findings. PMID:25886782

High versus Low-Dose Rate Brachytherapy for Cervical Cancer

PubMed Central

Patankar, Sonali S.; Tergas, Ana I.; Deutsch, Israel; Burke, William M.; Hou, June Y.; Ananth, Cande V.; Huang, Yongmei; Neugut, Alfred I.; Hershman, Dawn L.; Wright, Jason D.

2015-01-01

Objectives Brachytherapy plays an important role in the treatment of cervical cancer. While small trials have shown comparable survival outcomes between high (HDR) and low-dose rate (LDR) brachytherapy, little data is available in the US. We examined the utilization of HDR brachytherapy and analyzed the impact of type of brachytherapy on survival for cervical cancer. Methods Women with stage IB2–IVA cervical cancer treated with primary (external beam and brachytherapy) radiotherapy between 2003–2011 and recorded in the National Cancer Database (NCDB) were analyzed. Generalized linear mixed models and Cox proportional hazards regression were used to examine predictors of HDR brachytherapy use and the association between HDR use and survival. Results A total of 10,564 women including 2681 (25.4%) who received LDR and 7883 (74.6%) that received HDR were identified. Use of HDR increased from 50.2% in 2003 to 83.9% in 2011 (P<0.0001). In a multivariable model, year of diagnosis was the strongest predictor of use of HDR. While patients in the Northeast were more likely to receive HDR therapy, there were no other clinical or socioeconomic characteristics associated with receipt of HDR. In a multivariable Cox model, survival was similar between the HDR and LDR groups (HR=0.93; 95% 0.83–1.03). Similar findings were noted in analyses stratified by stage and histology. Kaplan-Meier analyses demonstrated no difference in survival based on type of brachytherapy for stage IIB (P=0.68), IIIB (P=0.17), or IVA (P=0.16) tumors. Conclusions The use of HDR therapy has increased rapidly. Overall survival is similar for LDR and HDR brachytherapy. PMID:25575481

Leptospirosis in American Samoa – Estimating and Mapping Risk Using Environmental Data

PubMed Central

Lau, Colleen L.; Clements, Archie C. A.; Skelly, Chris; Dobson, Annette J.; Smythe, Lee D.; Weinstein, Philip

2012-01-01

Background The recent emergence of leptospirosis has been linked to many environmental drivers of disease transmission. Accurate epidemiological data are lacking because of under-diagnosis, poor laboratory capacity, and inadequate surveillance. Predictive risk maps have been produced for many diseases to identify high-risk areas for infection and guide allocation of public health resources, and are particularly useful where disease surveillance is poor. To date, no predictive risk maps have been produced for leptospirosis. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk. Methodology and Principal Findings Data on seroprevalence and risk factors were obtained from a recent study of leptospirosis in American Samoa. Data on environmental variables were obtained from local sources, and included rainfall, altitude, vegetation, soil type, and location of backyard piggeries. Multivariable logistic regression was performed to investigate associations between seropositivity and risk factors. Using the multivariable models, seroprevalence at geographic locations was predicted based on environmental variables. Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive. Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house. Models had acceptable goodness of fit, and correctly classified ∼84% of cases. Conclusions and Significance Environmental variables could be used to identify high-risk areas for leptospirosis. Environmental monitoring could potentially be a valuable strategy for leptospirosis control, and allow us to move from disease surveillance to environmental health hazard surveillance as a more cost-effective tool for directing public health interventions. PMID:22666516

Characteristics and Outcomes of Patients with Ewing Sarcoma Over 40 Years of Age at Diagnosis

PubMed Central

Karski, Erin E.; Matthay, Katherine K.; Neuhaus, John M.; Goldsby, Robert E.; DuBois, Steven G.

2012-01-01

Background The peak incidence of Ewing sarcoma (EWS) is in adolescence, with little known about patients who are ≥ 40 years at diagnosis. We describe the clinical characteristics and survival of this rare group. Methods This retrospective cohort study utilized the Surveillance Epidemiology and End Results database. 2780 patients were identified; including 383 patients diagnosed ≥ 40 years. Patient characteristics between age groups were compared using chi-squared tests. Survival from diagnosis to death was estimated via Kaplan-Meier methods, compared with log-rank tests, and modeled using multivariable Cox methods. A competing risks analysis was performed to evaluate death due to cancer. Results Patients ≥ 40 years of age were more likely to have extra-skeletal tumors (66.1% v 31.7%; p<0.001), axial tumors (64.0% v 57.2%; p=0.01), and metastatic disease at diagnosis (35.5% v 30.0%; p=0.04) compared to younger patients. Five-year survival for those age ≥ 40 and age < 40 were 40.6% and 54.3%, respectively (p<0.0001). A Cox multivariable model controlling for differences between groups confirmed inferior survival for older patients (hazard ratio for death of 2.04; 95% CI 1.63 - 2.54; p < 0.0001); though treatment data were unavailable and not controlled for in the model. A competing risks analysis confirmed increased risk of cancer-related death in older patients. Conclusion Patients ≥ 40 years at diagnosis with EWS are more likely to have extra-skeletal tumors, metastatic disease, and axial primary tumors suggesting a difference in tumor biology. Independent of differences in these characteristics, older patients also have a lower survival rate. PMID:22959474

Retinoblastoma and Phosphate and Tensin Homolog Tumor Suppressors: Impact on Ductal Carcinoma In Situ Progression

PubMed Central

2012-01-01

Background A subset of patients with ductal carcinoma in situ (DCIS) will progress to invasive breast cancer. However, there are currently no markers to differentiate women at high risk from those at lower risk of developing invasive disease. Methods The association of two major tumor suppressor genes, retinoblastoma (RB) and phosphatase and tensin homolog (PTEN), with risk of any ipsilateral breast event (IBE) or progression to invasive breast cancer (IBC) was analyzed using data from 236 DCIS patients treated with breast conserving surgery with long-term follow-up. RB and PTEN expression was assessed with immunohistochemistry. The functional effects of RB and/or PTEN loss were modeled in MCF10A cells. Hazard ratios (HRs) were estimated with univariate and multivariable Cox regression models. All statistical tests were two-sided. Results Loss of RB immunoreactivity in DCIS was strongly associated with risk of IBE occurrence (HR = 2.64; 95% confidence interval [CI] = 1.64 to 4.25) and IBC recurrence (HR = 4.66; 95% CI = 2.19 to 9.93). The prognostic power of RB loss remained statistically significant in multivariable analyses. PTEN loss occurred frequently in DCIS but was not associated with recurrence or progression. However, patients with DCIS lesions that were both RB and PTEN deficient were at further increased risk for IBEs (HR = 3.39; 95% CI = 1.92 to 5.99) and IBC recurrence (HR = 6.1, 95% CI = 2.5 to 14.76). Preclinical modeling in MCF10A cells demonstrated that loss of RB and PTEN impacted proliferation, motility, and invasive properties. Conclusions These studies indicate that RB and PTEN together have prognostic utility and could be used to target aggressive treatment for patients with the greatest probability of benefit. PMID:23197489

Development and Validation of an Instrument to Predict Functional Recovery in Tibial Fracture Patients: The Somatic Pre-Occupation and Coping (SPOC) Questionnaire

PubMed Central

Busse, Jason W.; Bhandari, Mohit; Guyatt, Gordon H.; Heels-Ansdell, Diane; Kulkarni, Abhaya V.; Mandel, Scott; Sanders, David; Schemitsch, Emil; Swiontkowski, Marc; Tornetta, Paul; Wai, Eugene; Walter, Stephen D.

2011-01-01

Objective To explore the role of patients’ beliefs in their likelihood of recovery from severe physical trauma. Methods We developed and validated an instrument designed to capture the impact of patients’ beliefs on functional recovery from injury; the Somatic Pre-occupation and Coping (SPOC) questionnaire. At 6-weeks post-surgical fixation, we administered the SPOC questionnaire to 359 consecutive patients with operatively managed tibial shaft fractures. We constructed multivariable regression models to explore the association between SPOC scores and functional outcome at 1-year, as measured by return to work and short form-36 (SF-36) physical component summary (PCS) and mental component summary (MCS) scores. Results In our adjusted multivariable regression models that included pre-injury SF-36 scores, SPOC scores at 6-weeks post-surgery accounted for 18% of the variation in SF-36 PCS scores and 18% of SF-36 MCS scores at 1-year. In both models, 6-week SPOC scores were a far more powerful predictor of functional recovery than age, gender, fracture type, smoking status, or the presence of multi-trauma. Our adjusted analysis found that for each 14 point increment in SPOC score at 6-weeks (14 chosen on the basis of half a standard deviation of the mean SPOC score) the odds of returning to work at 1-year decreased by 40% (odds ratio = 0.60; 95% CI = 0.50 to 0.73). Conclusion The SPOC questionnaire is a valid measurement of illness beliefs in tibial fracture patients and is highly predictive of their long-term functional recovery. Future research should explore if these results extend to other trauma populations and if modification of unhelpful illness beliefs is feasible and would result in improved functional outcomes. PMID:22011635

Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study.

PubMed

Cox, L A; Ricci, P F

2005-04-01

Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.

Clostridium difficile Associated Risk of Death Score (CARDS): A novel severity score to predict mortality among hospitalized patients with Clostridium difficile infection

PubMed Central

Kassam, Zain; Fabersunne, Camila Cribb; Smith, Mark B.; Alm, Eric J.; Kaplan, Gilaad G.; Nguyen, Geoffrey C.; Ananthakrishnan, Ashwin N.

2016-01-01

Background Clostridium difficile infection (CDI) is public health threat and associated with significant mortality. However, there is a paucity of objectively derived CDI severity scoring systems to predict mortality. Aims To develop a novel CDI risk score to predict mortality entitled: Clostridium difficile Associated Risk of Death Score (CARDS). Methods We obtained data from the United States 2011 Nationwide Inpatient Sample (NIS) database. All CDI-associated hospitalizations were identified using discharge codes (ICD-9-CM, 008.45). Multivariate logistic regression was utilized to identify independent predictors of mortality. CARDS was calculated by assigning a numeric weight to each parameter based on their odds ratio in the final logistic model. Predictive properties of model discrimination were assessed using the c-statistic and validated in an independent sample using the 2010 NIS database. Results We identified 77,776 hospitalizations, yielding an estimate of 374,747 cases with an associated diagnosis of CDI in the United States, 8% of whom died in the hospital. The 8 severity score predictors were identified on multivariate analysis: age, cardiopulmonary disease, malignancy, diabetes, inflammatory bowel disease, acute renal failure, liver disease and ICU admission, with weights ranging from −1 (for diabetes) to 5 (for ICU admission). The overall risk score in the cohort ranged from 0 to 18. Mortality increased significantly as CARDS increased. CDI-associated mortality was 1.2% with a CARDS of 0 compared to 100% with CARDS of 18. The model performed equally well in our validation cohort. Conclusion CARDS is a promising simple severity score to predict mortality among those hospitalized with CDI. PMID:26849527

Treatment Adherence and Its Impact on Disease-Free Survival in the Breast International Group 1-98 Trial of Tamoxifen and Letrozole, Alone and in Sequence

PubMed Central

Giobbie-Hurder, Anita; Coates, Alan S.; Price, Karen N.; Ejlertsen, Bent; Debled, Marc; Gelber, Richard D.; Goldhirsch, Aron; Smith, Ian; Rabaglio, Manuela; Forbes, John F.; Neven, Patrick; Láng, István; Colleoni, Marco; Thürlimann, Beat

2016-01-01

Purpose To investigate adherence to endocrine treatment and its relationship with disease-free survival (DFS) in the Breast International Group (BIG) 1-98 clinical trial. Methods The BIG 1-98 trial is a double-blind trial that randomly assigned 6,193 postmenopausal women with hormone receptor–positive early breast cancer in the four-arm option to 5 years of tamoxifen (Tam), letrozole (Let), or the agents in sequence (Let-Tam, Tam-Let). This analysis included 6,144 women who received at least one dose of study treatment. Conditional landmark analyses and marginal structural Cox proportional hazards models were used to evaluate the relationship between DFS and treatment adherence (persistence [duration] and compliance with dosage). Competing risks regression was used to assess demographic, disease, and treatment characteristics of the women who stopped treatment early because of adverse events. Results Both aspects of low adherence (early cessation of letrozole and a compliance score of < 90%) were associated with reduced DFS (multivariable model hazard ratio, 1.45; 95% CI, 1.09 to 1.93; P = .01; and multivariable model hazard ratio, 1.61; 95% CI, 1.08 to 2.38; P = .02, respectively). Sequential treatments were associated with higher rates of nonpersistence (Tam-Let, 20.8%; Let-Tam, 20.3%; Tam 16.9%; Let 17.6%). Adverse events were the reason for most trial treatment early discontinuations (82.7%). Apart from sequential treatment assignment, reduced adherence was associated with older age, smoking, node negativity, or prior thromboembolic event. Conclusion Both persistence and compliance are associated with DFS. Toxicity management and, for sequential treatments, patient and physician awareness, may improve adherence. PMID:27217455

Gender, position of authority, and the risk of depression and post-traumatic stress disorder among a national sample of U.S. Reserve Component Personnel

PubMed Central

Cohen, Gregory H.; Sampson, Laura A.; Fink, David S.; Wang, Jing; Russell, Dale; Gifford, Robert; Fullerton, Carol; Ursano, Robert; Galea, Sandro

2016-01-01

BACKGROUND Recent United States military operations in Iraq and Afghanistan have seen dramatic increases in the proportion of women serving, and the breadth of their occupational roles. General population studies suggest that women, compared to men, and persons with lower, as compared to higher, social position may be at greater risk of post-traumatic stress disorder (PTSD) and depression. However, these relations remain unclear in military populations. Accordingly, we aimed to estimate the effects of (1) gender, (2) military authority (i.e., rank) and (3) the interaction of gender and military authority upon: (a) risk of most-recent-deployment-related PTSD, and (b) risk of depression since most-recent-deployment. METHODS Using a nationally representative sample of 1024 previously deployed Reserve Component personnel surveyed in 2010, we constructed multivariable logistic regression models to estimate effects of interest. RESULTS Weighted multivariable logistic regression models demonstrated no statistically significant associations between gender or authority, and either PTSD or depression. Interaction models demonstrated multiplicative statistical interaction between gender and authority for PTSD (beta= −2.37;p=0.01), and depression (beta=-1.21; p=0.057). Predicted probabilities of PTSD and depression, respectively, were lowest in male officers (0.06, 0.09), followed by male enlisted (0.07, 0.14), female enlisted (0.07, 0.15), and female officers (0.30, 0.25). CONCLUSIONS Female officers in the Reserve Component may be at greatest risk for PTSD and depression following deployment, relative to their male and enlisted counterparts, and this relation is not explained by deployment trauma exposure. Future studies may fruitfully examine whether social support, family responsibilities peri-deployment, or contradictory class status may explain these findings. PMID:26899583

Reproductive Factors and Incidence of Heart Failure Hospitalization in the Women’s Health Initiative

PubMed Central

Hall, Philip S.; Nah, Gregory; Howard, Barbara V.; Lewis, Cora E.; Allison, Matthew A.; Sarto, Gloria E.; Waring, Molly E.; Jacobson, Lisette T.; Manson, JoAnn E.; Klein, Liviu; Parikh, Nisha I.

2017-01-01

BACKGROUND Reproductive factors reflective of endogenous sex hormone exposure might have an effect on cardiac remodeling and the development of heart failure (HF). OBJECTIVES This study examined the association between key reproductive factors and the incidence of HF. METHODS Women from a cohort of the Women’s Health Initiative were systematically evaluated for the incidence of HF hospitalization from study enrollment through 2014. Reproductive factors (number of live births, age at first pregnancy, and total reproductive duration [time from menarche to menopause]) were self-reported at study baseline in 1993 to 1998. We employed Cox proportional hazards regression analysis in age- and multivariable-adjusted models. RESULTS Among 28,516 women, with an average age of 62.7 ± 7.1 years at baseline, 1,494 (5.2%) had an adjudicated incident HF hospitalization during an average follow-up of 13.1 years. After adjusting for covariates, total reproductive duration in years was inversely associated with incident HF: hazard ratios (HRs) of 0.99 per year (95% confidence interval [CI]: 0.98 to 0.99 per year) and 0.95 per 5 years (95% CI: 0.91 to 0.99 per 5 years). Conversely, early age at first pregnancy and nulliparity were significantly associated with incident HF in age-adjusted models, but not after multivariable adjustment. Notably, nulliparity was associated with incident HF with preserved ejection fraction in the fully adjusted model (HR: 2.75; 95% CI: 1.16 to 6.52). CONCLUSIONS In postmenopausal women, shorter total reproductive duration was associated with higher risk of incident HF, and nulliparity was associated with higher risk for incident HF with preserved ejection fraction. Whether exposure to endogenous sex hormones underlies this relationship should be investigated in future studies. PMID:28521890

Stressful Life Events Around the Time of Unplanned Pregnancy and Women's Health: Exploratory Findings from a National Sample.

PubMed

Hall, Kelli Stidham; Dalton, Vanessa K; Zochowski, Melissa; Johnson, Timothy R B; Harris, Lisa H

2017-06-01

Objective Little is known about how women's social context of unintended pregnancy, particularly adverse social circumstances, relates to their general health and wellbeing. We explored associations between stressful life events around the time of unintended pregnancy and physical and mental health. Methods Data are drawn from a national probability study of 1078 U.S. women aged 18-55. Our internet-based survey measured 14 different stressful life events occurring at the time of unintended pregnancy (operationalized as an additive index score), chronic disease and mental health conditions, and current health and wellbeing symptoms (standardized perceived health, depression, stress, and discrimination scales). Multivariable regression modeled relationships between stressful life events and health conditions/symptoms while controlling for sociodemographic and reproductive covariates. Results Among ever-pregnant women (N = 695), stressful life events were associated with all adverse health outcomes/symptoms in unadjusted analyses. In multivariable models, higher stressful life event scores were positively associated with chronic disease (aOR 1.21, CI 1.03-1.41) and mental health (aOR 1.42, CI 1.23-1.64) conditions, higher depression (B 0.37, CI 0.19-0.55), stress (B 0.32, CI 0.22-0.42), and discrimination (B 0.74, CI 0.45-1.04) scores, and negatively associated with ≥ very good perceived health (aOR 0.84, CI 0.73-0.97). Stressful life event effects were strongest for emotional and partner-related sub-scores. Conclusion Women with adverse social circumstances surrounding their unintended pregnancy experienced poorer health. Findings suggest that reproductive health should be considered in the broader context of women's health and wellbeing and have implications for integrated models of care that address women's family planning needs, mental and physical health, and social environments.

Calorie intake and patient outcomes in severe acute kidney injury: findings from The Randomized Evaluation of Normal vs. Augmented Level of Replacement Therapy (RENAL) study trial

PubMed Central

2014-01-01

Introduction Current practice in the delivery of caloric intake (DCI) in patients with severe acute kidney injury (AKI) receiving renal replacement therapy (RRT) is unknown. We aimed to describe calorie administration in patients enrolled in the Randomized Evaluation of Normal vs. Augmented Level of Replacement Therapy (RENAL) study and to assess the association between DCI and clinical outcomes. Methods We performed a secondary analysis in 1456 patients from the RENAL trial. We measured the dose and evolution of DCI during treatment and analyzed its association with major clinical outcomes using multivariable logistic regression, Cox proportional hazards models, and time adjusted models. Results Overall, mean DCI during treatment in ICU was low at only 10.9 ± 9 Kcal/kg/day for non-survivors and 11 ± 9 Kcal/kg/day for survivors. Among patients with a lower DCI (below the median) 334 of 729 (45.8%) had died at 90-days after randomization compared with 316 of 727 (43.3%) patients with a higher DCI (above the median) (P = 0.34). On multivariable logistic regression analysis, mean DCI carried an odds ratio of 0.95 (95% confidence interval (CI): 0.91-1.00; P = 0.06) per 100 Kcal increase for 90-day mortality. DCI was not associated with significant differences in renal replacement (RRT) free days, mechanical ventilation free days, ICU free days and hospital free days. These findings remained essentially unaltered after time adjusted analysis and Cox proportional hazards modeling. Conclusions In the RENAL study, mean DCI was low. Within the limits of such low caloric intake, greater DCI was not associated with improved clinical outcomes. Trial registration ClinicalTrials.gov number, NCT00221013 PMID:24629036

Magnetic Resonance Spectroscopy (MRS) of Prostatic Fluids for Early Detection of Prostate Cancer

DTIC Science & Technology

2005-10-01

measured using external 1H-NMRS coils, increasing the practicality of the metabonomic approach. In conclusion, the absolute concentrations of the...Lindon, J.C., and Holmes, E. “ Metabonomics ”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

DTIC Science & Technology

1991-09-01

However, there is no guarantee that this would work; for instance if the data were generated by an ARCH model (Tong, 1990 pp. 116-117) then a simple...Hill, R., Griffiths, W., Lutkepohl, H., and Lee, T., Introduction to the Theory and Practice of Econometrics , 2th ed., Wiley, 1985. Kendall, M., Stuart

Motives to use Facebook and problematic Facebook use in adolescents.

PubMed

Marino, Claudia; Mazzieri, Elena; Caselli, Gabriele; Vieno, Alessio; Spada, Marcantonio M

2018-05-30

Background and aims There is a growing body of evidence suggesting that problematic Facebook use (PFU) is an emerging problem, particularly among adolescents. Although a number of motivations explaining why people engage in frequent Facebook use have been identified, less is known about the specific psychological needs underlying PFU. The aim of this study is to test a model designed to assess the unique contribution of psychological motives for using Facebook to the different PFU dimensions in a sample of adolescents. Methods A total of 864 Italian adolescents participated in the study. Multivariate multiple regression was run to test whether the four motives were differently associated with problematic dimensions. Results The results showed that the two motives with negative valence (coping and conformity) were significantly linked to the five dimensions of PFU, whereas the two motives with positive valence (enhancement and social) appeared to be weaker predictors for three out of these five dimensions. Discussion and conclusion In conclusion, psychological motives for using Facebook appeared to significantly contribute to explaining PFU among adolescents, and should be considered by researchers and educational practitioners.

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information.

PubMed

Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S

2017-02-01

The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information. Copyright © 2016. Published by Elsevier Inc.

DasPy – Open Source Multivariate Land Data Assimilation Framework with High Performance Computing

NASA Astrophysics Data System (ADS)

Han, Xujun; Li, Xin; Montzka, Carsten; Kollet, Stefan; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

2015-04-01

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. Multivariate data assimilation refers to the simultaneous assimilation of observation data for multiple model state variables into a simulation model. Our main motivation was to develop an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with C++ and Fortran language. This system has been evaluated in several soil moisture, L-band brightness temperature and land surface temperature assimilation studies. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be represented by perturbed atmospheric forcings, perturbed soil and vegetation properties and model initial conditions. The CLM4.5 (Community Land Model) was integrated as the model operator. The CMEM (Community Microwave Emission Modelling Platform), COSMIC (COsmic-ray Soil Moisture Interaction Code) and the two source formulation were integrated as observation operators for assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy is parallelized using the hybrid MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) techniques. All the input and output data flow is organized efficiently using the commonly used NetCDF file format. Online 1D and 2D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

PubMed

Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

2016-04-01

To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

The anatomy of absenteeism.

PubMed

Markussen, Simen; Røed, Knut; Røgeberg, Ole J; Gaure, Simen

2011-03-01

Based on comprehensive administrative register data from Norway, we examine the determinants of sickness absence behavior; in terms of employee characteristics, workplace characteristics, panel doctor characteristics, and economic conditions. The analysis is based on a novel concept of a worker's steady state sickness absence propensity, computed from a multivariate hazard rate model designed to predict the incidence and duration of sickness absence for all workers. Key conclusions are that (i) most of the cross-sectional variation in absenteeism is caused by genuine employee heterogeneity; (ii) the identity of a person's panel doctor has a significant impact on absence propensity; (iii) sickness absence insurance is frequently certified for reasons other than sickness; and (iv) the recovery rate rises enormously just prior to the exhaustion of sickness insurance benefits. Copyright © 2010 Elsevier B.V. All rights reserved.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Chemiluminescence-based multivariate sensing of local equivalence ratios in premixed atmospheric methane-air flames

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

Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.

Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using themore » leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.« less

Prevalence and determinants of cardiovascular disease risk factors among the residents of urban community housing projects in Malaysia.

PubMed

Amiri, Mohammadreza; Majid, Hazreen Abdul; Hairi, FarizahMohd; Thangiah, Nithiah; Bulgiba, Awang; Su, Tin Tin

2014-01-01

The objectives are to assess the prevalence and determinants of cardiovascular disease (CVD) risk factors among the residents of Community Housing Projects in metropolitan Kuala Lumpur, Malaysia. By using simple random sampling, we selected and surveyed 833 households which comprised of 3,722 individuals. Out of the 2,360 adults, 50.5% participated in blood sampling and anthropometric measurement sessions. Uni and bivariate data analysis and multivariate binary logistic regression were applied to identify demographic and socioeconomic determinants of the existence of having at least one CVD risk factor. As a Result, while obesity (54.8%), hypercholesterolemia (51.5%), and hypertension (39.3%) were the most common CVD risk factors among the low-income respondents, smoking (16.3%), diabetes mellitus (7.8%) and alcohol consumption (1.4%) were the least prevalent. Finally, the results from the multivariate binary logistic model illustrated that compared to the Malays, the Indians were 41% less likely to have at least one of the CVD risk factors (OR = 0.59; 95% CI: 0.37 - 0.93). In Conclusion, the low-income individuals were at higher risk of developing CVDs. Prospective policies addressing preventive actions and increased awareness focusing on low-income communities are highly recommended and to consider age, gender, ethnic backgrounds, and occupation classes.

Medication and Dietary Supplement Interactions among a Low-Income, Hospitalized Patient Population Who Take Cardiac Medications.

PubMed

Gardiner, Paula; Filippelli, Amanda C; Sadikova, Ekaterina; White, Laura F; Jack, Brian W

2015-01-01

Purpose. To identify characteristics associated with the use of potentially harmful combinations of dietary supplements (DS) and cardiac prescription medications in an urban, underserved, inpatient population. Methods. Cardiac prescription medication users were identified to assess the prevalence and risk factors of potentially harmful dietary supplement-prescription medication interactions (PHDS-PMI). We examined sociodemographic and clinical characteristics for crude (χ (2) or t-tests) and adjusted multivariable logistic regression associations with the outcome. Results. Among 558 patients, there were 121 who also used a DS. Of the 110 participants having a PHDS-PMI, 25% were asked about their DS use at admission, 75% had documentation of DS in their chart, and 21% reported the intention to continue DS use after discharge. A multivariable logistic regression model noted that for every additional medication or DS taken the odds of having a PHDS-PMI increase and that those with a high school education are significantly less likely to have a PHDS-PMI than those with a college education. Conclusion. Inpatients at an urban safety net hospital taking a combination of cardiac prescription medications and DS are at a high risk of harmful supplement-drug interactions. Providers must ask about DS use and should consider the potential for interactions when having patient discussions about cardiac medications and DS.

Jaundice: an important, poorly recognized risk factor for diminished survival in patients with adenocarcinoma of the head of the pancreas

PubMed Central

Strasberg, Steven M; Gao, Feng; Sanford, Dominic; Linehan, David C; Hawkins, William G; Fields, Ryan; Carpenter, Danielle H; Brunt, Elizabeth M; Phillips, Carolyn

2014-01-01

Objectives: Jaundice impairs cellular immunity, an important defence against the dissemination of cancer. Jaundice is a common mode of presentation in pancreatic head adenocarcinoma. The purpose of this study was to determine whether there is an association between preoperative jaundice and survival in patients who have undergone resection of such tumours. Methods: Thirty possible survival risk factors were evaluated in a database of over 400 resected patients. Univariate analysis was used to determine odds ratio for death. All factors for which a P-value of <0.30 was obtained were entered into a multivariate analysis using the Cox model with backward selection. Results: Preoperative jaundice, age, positive node status, poor differentiation and lymphatic invasion were significant indicators of poor outcome in multivariate analysis. Absence of jaundice was a highly favourable prognostic factor. Interaction emerged between jaundice and nodal status. The benefit conferred by the absence of jaundice was restricted to patients in whom negative node status was present. Five-year overall survival in this group was 66%. Jaundiced patients who underwent preoperative stenting had a survival advantage. Conclusions: Preoperative jaundice is a negative risk factor in adenocarcinoma of the pancreas. Additional studies are required to determine the exact mechanism for this effect. PMID:23600768

Predictors of retention in community-based methadone maintenance treatment program in Pearl River Delta, China

PubMed Central

2013-01-01

Background The aims were to identify predictors of treatment retention in methadone maintenance treatment (MMT) clinics in Pearl River Delta, China. Methods Retrospective longitudinal study. Participants: 6 MMT clinics in rural and urban area were selected. Statistical analysis: Stratified random sampling was employed, and the data were analyzed using Kaplan-Meier survival curves and life table method. Protective or risk factors were explored using Cox’s proportional hazards model. Independent variables were enrolled in univariate analysis and among which significant variables were analyzed by multivariate analysis. Results A total of 2728 patients were enrolled. The median of the retention duration was 13.63 months, and the cumulative retention rates at 1,2,3 years were 53.0%, 35.0%, 20.0%, respectively. Multivariate Cox analysis showed: age, relationship with family, live on support from family or friends, income, considering treatment cost suitable, considering treatment open time suitable, addiction severity (daily expense for drug), communication with former drug taking peer, living in rural area, daily treatment dosage, sharing needles, re-admission and history of being arrested were predictors for MMT retention. Conclusions MMT retention rate in Guangdong was low and treatment skills and quality should be improved. Meanwhile, participation of family and society should be encouraged. PMID:23497263

Analysis of threats to research validity introduced by audio recording clinic visits: Selection bias, Hawthorne effect, both, or neither?

PubMed Central

Henry, Stephen G.; Jerant, Anthony; Iosif, Ana-Maria; Feldman, Mitchell D.; Cipri, Camille; Kravitz, Richard L.

2015-01-01

Objective To identify factors associated with participant consent to record visits; to estimate effects of recording on patient-clinician interactions Methods Secondary analysis of data from a randomized trial studying communication about depression; participants were asked for optional consent to audio record study visits. Multiple logistic regression was used to model likelihood of patient and clinician consent. Multivariable regression and propensity score analyses were used to estimate effects of audio recording on 6 dependent variables: discussion of depressive symptoms, preventive health, and depression diagnosis; depression treatment recommendations; visit length; visit difficulty. Results Of 867 visits involving 135 primary care clinicians, 39% were recorded. For clinicians, only working in academic settings (P=0.003) and having worked longer at their current practice (P=0.02) were associated with increased likelihood of consent. For patients, white race (P=0.002) and diabetes (P=0.03) were associated with increased likelihood of consent. Neither multivariable regression nor propensity score analyses revealed any significant effects of recording on the variables examined. Conclusion Few clinician or patient characteristics were significantly associated with consent. Audio recording had no significant effect on any dependent variables. Practice Implications Benefits of recording clinic visits likely outweigh the risks of bias in this setting. PMID:25837372

Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution.

PubMed

Novikova, Anna; Carstensen, Jens M; Rades, Thomas; Leopold, Prof Dr Claudia S

2016-12-30

In the present study the applicability of multispectral UV imaging in combination with multivariate image analysis for surface evaluation of MUPS tablets was investigated with respect to the differentiation of the API pellets from the excipients matrix, estimation of the drug content as well as pellet distribution, and influence of the coating material and tablet thickness on the predictive model. Different formulations consisting of coated drug pellets with two coating polymers (Aquacoat ® ECD and Eudragit ® NE 30 D) at three coating levels each were compressed to MUPS tablets with various amounts of coated pellets and different tablet thicknesses. The coated drug pellets were clearly distinguishable from the excipients matrix using a partial least squares approach regardless of the coating layer thickness and coating material used. Furthermore, the number of the detected drug pellets on the tablet surface allowed an estimation of the true drug content in the respective MUPS tablet. In addition, the pellet distribution in the MUPS formulations could be estimated by UV image analysis of the tablet surface. In conclusion, this study revealed that UV imaging in combination with multivariate image analysis is a promising approach for the automatic quality control of MUPS tablets during the manufacturing process. Copyright © 2016 Elsevier B.V. All rights reserved.

Pattern of spread and prognosis in lower limb-onset ALS

PubMed Central

TURNER, MARTIN R.; BROCKINGTON, ALICE; SCABER, JAKUB; HOLLINGER, HANNAH; MARSDEN, RACHAEL; SHAW, PAMELA J.; TALBOT, KEVIN

2011-01-01

Our objective was to establish the pattern of spread in lower limb-onset ALS (contra- versus ipsi-lateral) and its contribution to prognosis within a multivariate model. Pattern of spread was established in 109 sporadic ALS patients with lower limb-onset, prospectively recorded in Oxford and Sheffield tertiary clinics from 2001 to 2008. Survival analysis was by univariate Kaplan-Meier log-rank and multivariate Cox proportional hazards. Variables studied were time to next limb progression, site of next progression, age at symptom onset, gender, diagnostic latency and use of riluzole. Initial progression was either to the contralateral leg (76%) or ipsilateral arm (24%). Factors independently affecting survival were time to next limb progression, age at symptom onset, and diagnostic latency. Time to progression as a prognostic factor was independent of initial direction of spread. In a regression analysis of the deceased, overall survival from symptom onset approximated to two years plus the time interval for initial spread. In conclusion, rate of progression in lower limb-onset ALS is not influenced by whether initial spread is to the contralateral limb or ipsilateral arm. The time interval to this initial spread is a powerful factor in predicting overall survival, and could be used to facilitate decision-making and effective care planning. PMID:20001488

Intakes of caffeine, coffee and tea and risk of amyotrophic lateral sclerosis: Results from five cohort studies.

PubMed

Fondell, Elinor; O'Reilly, É Ilis J; Fitzgerald, Kathryn C; Falcone, Guido J; Kolonel, Laurence N; Park, Yikyung; Gapstur, Susan M; Ascherio, Alberto

2015-01-01

Caffeine is thought to be neuroprotective by antagonizing the adenosine A2A receptors in the brain and thereby protecting motor neurons from excitotoxicity. We examined the association between consumption of caffeine, coffee and tea and risk of amyotrophic lateral sclerosis (ALS). Longitudinal analyses based on over 1,010,000 males and females in five large cohort studies (the Nurses' Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition Cohort, the Multiethnic Cohort Study, and the National Institutes of Health-AARP Diet and Health Study). Cohort-specific multivariable-adjusted risk ratios (RR) and 95% confidence intervals (CI) estimates of ALS incidence or death were estimated by Cox proportional hazards regression and pooled using random-effects models. Results showed that a total of 1279 cases of ALS were documented during a mean of 18 years of follow-up. Caffeine intake was not associated with ALS risk; the pooled multivariable-adjusted RR comparing the highest to the lowest quintile of intake was 0.96 (95% CI 0.81-1.16). Similarly, neither coffee nor tea was associated with ALS risk. In conclusion, the results of this large study do not support associations of caffeine or caffeinated beverages with ALS risk.

Intakes of caffeine, coffee and tea and risk of Amyotrophic Lateral Sclerosis: Results from five cohort studies

PubMed Central

Fondell, Elinor; O'Reilly, Éilis J.; Fitzgerald, Kathryn C.; Falcone, Guido J.; Kolonel, Laurence N.; Park, Yikyung; Gapstur, Susan M.; Ascherio, Alberto

2015-01-01

Objective Caffeine is thought to be neuroprotective by antagonizing the adenosine A2A receptors in the brain and thereby protecting motor neurons from excitotoxicity. We examined the association between consumption of caffeine, coffee and tea and risk of Amyotrophic Lateral Sclerosis (ALS). Methods Longitudinal analyses based on over 1 010 000 men and women in 5 large cohort studies [the Nurses’ Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition Cohort, the Multiethnic Cohort Study, and the National Institutes of Health – AARP Diet and Health Study]. Cohort-specific multivariable-adjusted risk ratios (RR) and 95% confidence intervals (CI) estimates of ALS incidence or death was estimated by Cox proportional hazards regression and pooled using random-effects models. Results A total of 1279 cases of ALS were documented during a mean of 18 years of follow-up. Caffeine intake was not associated with ALS risk; the pooled multivariable-adjusted RR comparing the highest to the lowest quintile of intake was 0.96 (95% CI 0.81-1.16). Similarly, neither coffee nor tea was associated with ALS risk. Conclusion The results of this large study do not support associations of caffeine or caffeinated beverages with ALS risk. PMID:25822002

Comparing Medical and Recreational Cannabis Users on Socio-Demographic, Substance and Medication Use, and Health and Disability Characteristics.

PubMed

Goulet-Stock, Sybil; Rueda, Sergio; Vafaei, Afshin; Ialomiteanu, Anca; Manthey, Jakob; Rehm, Jürgen; Fischer, Benedikt

2017-01-01

While recreational cannabis use is common, medical cannabis programs have proliferated across North America, including a federal program in Canada. Few comparisons of medical and recreational cannabis users (RCUs) exist; this study compared these groups on key characteristics. Data came from a community-recruited sample of formally approved medical cannabis users (MCUs; n = 53), and a sub-sample of recreational cannabis users (RCUs; n = 169) from a representative adult survey in Ontario (Canada). Samples were telephone-surveyed on identical measures, including select socio-demographic, substance and medication use, and health and disability measures. Based on initial bivariate comparisons, multivariate logistical regression with a progressive adjustment approach was performed to assess independent predictors of group status. In bivariate analyses, older age, lower household income, lower alcohol use, higher cocaine, prescription opioid, depression and anxiety medication use, and lower health and disability status were significantly associated with medical cannabis use. In the multivariate analysis, final model, household income, alcohol use, and disability levels were associated with medical cannabis use. Conclusions/Scientific Significance: Compared to RCUs, medical users appear to be mainly characterized by factors negatively influencing their overall health status. Future studies should investigate the actual impact and net benefits of medical cannabis use on these health problems. © 2017 S. Karger AG, Basel.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

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

Nesteruk, M; Riesterer, O; Veit-Haibach, P

2016-06-15

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, bloodmore » flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.« less

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

CONSTRUCTING KNOWLEDGE FROM MULTIVARIATE SPATIOTEMPORAL DATA: INTEGRATING GEOGRAPHIC VISUALIZATION WITH KNOWLEDGE DISCOVERY IN DATABASE METHODS. (R825195)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

DEVELOPMENT OF HOURLY PROBABILISTIC UTILITY NOX EMISSION INVENTORIES USING TIME SERIES TECHNIQUES: PART 2-MULTIVARIATE APPROACH. (R826766)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

A RUTCOR Project in Discrete Applied Mathematics

DTIC Science & Technology

1990-02-20

representations of smooth piecewise polynomial functions over triangulated regions have led in particular to the conclusion that Groebner basis methods of...Reversing Number of a Digraph," in preparation. 4. Billera, L.J., and Rose, L.L., " Groebner Basis Methods for Multivariate Splines," RRR 1-89, January

Modeling of turbulent supersonic H2-air combustion with a multivariate beta PDF

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Hassan, H. A.

1993-01-01

Recent calculations of turbulent supersonic reacting shear flows using an assumed multivariate beta PDF (probability density function) resulted in reduced production rates and a delay in the onset of combustion. This result is not consistent with available measurements. The present research explores two possible reasons for this behavior: use of PDF's that do not yield Favre averaged quantities, and the gradient diffusion assumption. A new multivariate beta PDF involving species densities is introduced which makes it possible to compute Favre averaged mass fractions. However, using this PDF did not improve comparisons with experiment. A countergradient diffusion model is then introduced. Preliminary calculations suggest this to be the cause of the discrepancy.

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

PubMed Central

Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.

2016-01-01

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times. PMID:27563373

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data.

PubMed

Pathan, Sameer A; Bhutta, Zain A; Moinudheen, Jibin; Jenkins, Dominic; Silva, Ashwin D; Sharma, Yogdutt; Saleh, Warda A; Khudabakhsh, Zeenat; Irfan, Furqan B; Thomas, Stephen H

2016-01-01

Background: Standard Emergency Department (ED) operations goals include minimization of the time interval (tMD) between patients' initial ED presentation and initial physician evaluation. This study assessed factors known (or suspected) to influence tMD with a two-step goal. The first step was generation of a multivariate model identifying parameters associated with prolongation of tMD at a single study center. The second step was the use of a study center-specific multivariate tMD model as a basis for predictive marginal probability analysis; the marginal model allowed for prediction of the degree of ED operations benefit that would be affected with specific ED operations improvements. Methods: The study was conducted using one month (May 2015) of data obtained from an ED administrative database (EDAD) in an urban academic tertiary ED with an annual census of approximately 500,000; during the study month, the ED saw 39,593 cases. The EDAD data were used to generate a multivariate linear regression model assessing the various demographic and operational covariates' effects on the dependent variable tMD. Predictive marginal probability analysis was used to calculate the relative contributions of key covariates as well as demonstrate the likely tMD impact on modifying those covariates with operational improvements. Analyses were conducted with Stata 14MP, with significance defined at p  < 0.05 and confidence intervals (CIs) reported at the 95% level. Results: In an acceptable linear regression model that accounted for just over half of the overall variance in tMD (adjusted r 2 0.51), important contributors to tMD included shift census ( p  = 0.008), shift time of day ( p  = 0.002), and physician coverage n ( p  = 0.004). These strong associations remained even after adjusting for each other and other covariates. Marginal predictive probability analysis was used to predict the overall tMD impact (improvement from 50 to 43 minutes, p  < 0.001) of consistent staffing with 22 physicians. Conclusions: The analysis identified expected variables contributing to tMD with regression demonstrating significance and effect magnitude of alterations in covariates including patient census, shift time of day, and number of physicians. Marginal analysis provided operationally useful demonstration of the need to adjust physician coverage numbers, prompting changes at the study ED. The methods used in this analysis may prove useful in other EDs wishing to analyze operations information with the goal of predicting which interventions may have the most benefit.

Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Ping, Pung Yean; Ahmad, Maizah Hura Binti

2014-12-01

World gold price is a popular investment commodity. The series have often been modeled using univariate models. The objective of this paper is to show that there is a co-movement between gold price and USD foreign exchange rate. Using the effect of the USD foreign exchange rate on the gold price, a model that can be used to forecast future gold prices is developed. For this purpose, the current paper proposes a multivariate GARCH (Bivariate GARCH) model. Using daily prices of both series from 01.01.2000 to 05.05.2014, a causal relation between the two series understudied are found and a bivariate GARCH model is produced.

Design, evaluation and test of an electronic, multivariable control for the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Skira, C. A.; Dehoff, R. L.; Hall, W. E., Jr.

1980-01-01

A digital, multivariable control design procedure for the F100 turbofan engine is described. The controller is based on locally linear synthesis techniques using linear, quadratic regulator design methods. The control structure uses an explicit model reference form with proportional and integral feedback near a nominal trajectory. Modeling issues, design procedures for the control law and the estimation of poorly measured variables are presented.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data

PubMed Central

Franco-Pedroso, Javier; Ramos, Daniel; Gonzalez-Rodriguez, Joaquin

2016-01-01

In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (Cllr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints. PMID:26901680

Implementation Challenges for Multivariable Control: What You Did Not Learn in School

NASA Technical Reports Server (NTRS)

Garg, Sanjay

2008-01-01

Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection.

PubMed

Mwanza, Jean-Claude; Warren, Joshua L; Hochberg, Jessica T; Budenz, Donald L; Chang, Robert T; Ramulu, Pradeep Y

2015-01-01

To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. One hundred ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike's information criterion (AIC), and prediction confidence interval lengths. For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDx-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT×NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single-variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAP-FDT, and interaction GDx-TSNIT×NAP-FDT consistently provided better discriminating abilities for detecting early, moderate, and severe glaucoma than the best single-variable models. The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDx-TSNIT×NAP-FDT provides the best glaucoma prediction compared with all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared with using GDx or FDT alone.

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

PubMed

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These two coefficients of friction show a strong correlation. Consequently the two multivariable models resemble, with the static coefficient of friction being on average 18% lower than the dynamic coefficient of friction. The multivariable models in this study can be used to describe the data set that was the basis for this study. Care should be taken when generalising these results. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Investigation of Association Between Hip Osteoarthritis Susceptibility Loci and Radiographic Proximal Femur Shape

PubMed Central

Thiagarajah, Shankar; Wilkinson, J. Mark; Panoutsopoulou, Kalliope; Day‐Williams, Aaron G.; Cootes, Timothy F.; Wallis, Gillian A.; Loughlin, John; Arden, Nigel; Birrell, Fraser; Carr, Andrew; Chapman, Kay; Deloukas, Panos; Doherty, Michael; McCaskie, Andrew; Ollier, William E. R.; Rai, Ashok; Ralston, Stuart H.; Spector, Timothy D.; Valdes, Ana M.; Wallis, Gillian A.; Mark Wilkinson, J.; Zeggini, Eleftheria

2015-01-01

Objective To test whether previously reported hip morphology or osteoarthritis (OA) susceptibility loci are associated with proximal femur shape as represented by statistical shape model (SSM) modes and as univariate or multivariate quantitative traits. Methods We used pelvic radiographs and genotype data from 929 subjects with unilateral hip OA who had been recruited previously for the Arthritis Research UK Osteoarthritis Genetics Consortium genome‐wide association study. We built 3 SSMs capturing the shape variation of the OA‐unaffected proximal femur in the entire mixed‐sex cohort and for male/female‐stratified cohorts. We selected 41 candidate single‐nucleotide polymorphisms (SNPs) previously reported as being associated with hip morphology (for replication analysis) or OA (for discovery analysis) and for which genotype data were available. We performed 2 types of analysis for genotype–phenotype associations between these SNPs and the modes of the SSMs: 1) a univariate analysis using individual SSM modes and 2) a multivariate analysis using combinations of SSM modes. Results The univariate analysis identified association between rs4836732 (within the ASTN2 gene) and mode 5 of the female SSM (P = 0.0016) and between rs6976 (within the GLT8D1 gene) and mode 7 of the mixed‐sex SSM (P = 0.0003). The multivariate analysis identified association between rs5009270 (near the IFRD1 gene) and a combination of modes 3, 4, and 9 of the mixed‐sex SSM (P = 0.0004). Evidence of associations remained significant following adjustment for multiple testing. All 3 SNPs had previously been associated with hip OA. Conclusion These de novo findings suggest that rs4836732, rs6976, and rs5009270 may contribute to hip OA susceptibility by altering proximal femur shape. PMID:25939412

Neuropsychological Testing Predicts Cerebrospinal Fluid Aβ in Mild Cognitive Impairment (MCI)

PubMed Central

Kandel, Benjamin M.; Avants, Brian B.; Gee, James C.; Arnold, Steven E.; Wolk, David A.

2015-01-01

Background Psychometric tests predict conversion of Mild Cognitive Impairment (MCI) to probable Alzheimer's Disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by CSF Aβ1–42, in patients with MCI, as compared to neuroimaging biomarkers. Methods We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. Results The 30-minute delayed recall score of the Rey Auditory Verbal Learning Test (AVLT) was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67±0.02 and odds ratio of 2.5±0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67±0.03, OR 3.2±1.2), followed by hippocampal volume (AUC 0.64±0.02,,OR 2.4±0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68±0.03 (OR 3.38±1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD. PMID:25881908

Energy intake and leukocyte telomere length in young adults123

PubMed Central

Goldberger, Nehama; Kimura, Masayuki; Sinnreich, Ronit; Aviv, Abraham

2012-01-01

Background: Dietary energy restriction in mammals, particularly at a young age, extends the life span. Leukocyte telomere length (LTL) is thought to be a bioindicator of aging in humans. High n−6 (omega-6) PUFA intake may accelerate LTL attrition. Objective: We determined whether lower energy and higher PUFA intakes in young adulthood are associated with shorter LTL in cross-sectional and longitudinal analyses. Design: In a longitudinal observational study (405 men, 204 women), diet was determined at baseline by a semiquantitative food-frequency questionnaire, and LTL was determined by Southern blots at mean ages of 30.1 y (baseline) and 43.2 y (follow-up). Spearman correlations and multivariable linear regression were used. Results: Baseline energy intake was inversely associated with follow-up LTL in men (standardized β = −0.171, P = 0.0005) but not in women (P = 0.039 for sex interaction). The difference in men between the highest and lowest quintiles of energy was 244 base pairs (bp) (95% CI: 59, 429 bp) and between extreme quintiles of LTL was 440 kcal (95% CI: 180, 700 kcal). Multivariable adjustment modestly attenuated the association (β = −0.157, P = 0.002). Inverse associations, which were noted for all macronutrients, were strongest for the unsaturated fatty acids. In multivariable models including energy and the macronutrients (as percentage of energy), the significant inverse energy-LTL association (but not the PUFA-LTL association) persisted. The energy-LTL association was restricted to never smokers (standardized β = −0.259, P = 0.0008; P = 0.050 for the smoking × calorie interaction). Conclusions: The inverse calorie intake–LTL association is consistent with trial data showing beneficial effects of calorie restriction on aging biomarkers. Further exploration of energy intake and LTL dynamics in the young is needed. PMID:22237065

Correlation between smoking habit and surgical outcomes on viral-associated hepatocellular carcinomas

PubMed Central

Kai, Keita; Komukai, Sho; Koga, Hiroki; Yamaji, Koutaro; Ide, Takao; Kawaguchi, Atsushi; Aishima, Shinichi; Noshiro, Hirokazu

2018-01-01

AIM To investigate the association between smoking habits and surgical outcomes in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) (B-HCC) and hepatitis C virus (HCV)-related HCC (C-HCC) and clarify the clinicopathological features associated with smoking status in B-HCC and C-HCC patients. METHODS We retrospectively examined the cases of the 341 consecutive patients with viral-associated HCC (C-HCC, n = 273; B-HCC, n = 68) who underwent curative surgery for their primary lesion. We categorized smoking status at the time of surgery into never, ex- and current smoker. We analyzed the B-HCC and C-HCC groups’ clinicopathological features and surgical outcomes, i.e., disease-free survival (DFS), overall survival (OS), and disease-specific survival (DSS). Univariate and multivariate analyses were performed using a Cox proportional hazards regression model. We also performed subset analyses in both patient groups comparing the current smokers to the other patients. RESULTS The multivariate analysis in the C-HCC group revealed that current-smoker status was significantly correlated with both OS (P = 0.0039) and DSS (P = 0.0416). In the B-HCC patients, no significant correlation was observed between current-smoker status and DFS, OS, or DSS in the univariate or multivariate analyses. The subset analyses comparing the current smokers to the other patients in both the C-HCC and B-HCC groups revealed that the current smokers developed HCC at significantly younger ages than the other patients irrespective of viral infection status. CONCLUSION A smoking habit is significantly correlated with the overall and disease-specific survivals of patients with C-HCC. In contrast, the B-HCC patients showed a weak association between smoking status and surgical outcomes. PMID:29358882

Correlates of opium use: retrospective analysis of a survey of tribal communities in Arunachal Pradesh, India

PubMed Central

2013-01-01

Background Household survey data of Changlang district, Arunachal Pradesh, were used in the present study to assess the prevalence of opium use among different tribes, and to examine the association between sociodemographic factors and opium use. Methods A sample of 3421 individuals (1795 men and 1626 women) aged 15 years and older was analyzed using a multivariate logistic regression model to determine factors associated with opium use. Sociodemographic information such as age, education, occupation, religion, ethnicity and marital status were included in the analysis. Results The prevalence of opium use was significantly higher (10.6%) among men than among women (2.1%). It varied according to age, educational level, occupation, marital status and religion of the respondents. In both sexes, opium use was significantly higher among Singpho and Khamti tribes compared with other tribes. Multivariate logistic regression indicated that opium use was significantly associated with age, occupation, ethnicity, religion and marital status of the respondents of both sexes. Multivariate rate ratios (MRR) for opium use were significantly higher (4–6 times) among older age groups (≥35 years) and male respondents. In males, the MRR was also significantly higher in respondents of Buddhist and Indigenous religion, while in females, the MRR was significantly higher in Buddhists. Most of the female opium users had taken opium for more than 5 years and were introduced to it by their husbands after marriage. Use of other substances among opium users comprised mainly tobacco (76%) and alcohol (44%). Conclusions The study reveals the sociodemographic factors, such as age, sex, ethnicity, religion and occupation, which are associated with opium use. Such information is useful for institution of intervention measures to reduce opium use. PMID:23575143

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

PubMed Central

Jackson, Rod

2017-01-01

Background Many national cardiovascular disease (CVD) risk factor management guidelines now recommend that drug treatment decisions should be informed primarily by patients’ multi-variable predicted risk of CVD, rather than on the basis of single risk factor thresholds. To investigate the potential impact of treatment guidelines based on CVD risk thresholds at a national level requires individual level data representing the multi-variable CVD risk factor profiles for a country’s total adult population. As these data are seldom, if ever, available, we aimed to create a synthetic population, representing the joint CVD risk factor distributions of the adult New Zealand population. Methods and results A synthetic population of 2,451,278 individuals, representing the actual age, gender, ethnicity and social deprivation composition of people aged 30–84 years who completed the 2013 New Zealand census was generated using Monte Carlo sampling. Each ‘synthetic’ person was then probabilistically assigned values of the remaining cardiovascular disease (CVD) risk factors required for predicting their CVD risk, based on data from the national census national hospitalisation and drug dispensing databases and a large regional cohort study, using Monte Carlo sampling and multiple imputation. Where possible, the synthetic population CVD risk distributions for each non-demographic risk factor were validated against independent New Zealand data sources. Conclusions We were able to develop a synthetic national population with realistic multi-variable CVD risk characteristics. The construction of this population is the first step in the development of a micro-simulation model intended to investigate the likely impact of a range of national CVD risk management strategies that will inform CVD risk management guideline updates in New Zealand and elsewhere. PMID:28384217

Single-Isocenter Multiple-Target Stereotactic Radiosurgery: Risk of Compromised Coverage

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

Roper, Justin, E-mail: justin.roper@emory.edu; Department of Biostatistics and Bioinformatics, Winship Cancer Institute of Emory University, Atlanta, Georgia; Chanyavanich, Vorakarn

2015-11-01

Purpose: To determine the dosimetric effects of rotational errors on target coverage using volumetric modulated arc therapy (VMAT) for multitarget stereotactic radiosurgery (SRS). Methods and Materials: This retrospective study included 50 SRS cases, each with 2 intracranial planning target volumes (PTVs). Both PTVs were planned for simultaneous treatment to 21 Gy using a single-isocenter, noncoplanar VMAT SRS technique. Rotational errors of 0.5°, 1.0°, and 2.0° were simulated about all axes. The dose to 95% of the PTV (D95) and the volume covered by 95% of the prescribed dose (V95) were evaluated using multivariate analysis to determine how PTV coverage was relatedmore » to PTV volume, PTV separation, and rotational error. Results: At 0.5° rotational error, D95 values and V95 coverage rates were ≥95% in all cases. For rotational errors of 1.0°, 7% of targets had D95 and V95 values <95%. Coverage worsened substantially when the rotational error increased to 2.0°: D95 and V95 values were >95% for only 63% of the targets. Multivariate analysis showed that PTV volume and distance to isocenter were strong predictors of target coverage. Conclusions: The effects of rotational errors on target coverage were studied across a broad range of SRS cases. In general, the risk of compromised coverage increased with decreasing target volume, increasing rotational error and increasing distance between targets. Multivariate regression models from this study may be used to quantify the dosimetric effects of rotational errors on target coverage given patient-specific input parameters of PTV volume and distance to isocenter.« less

Racial/Ethnic Disparities in the Mental Health Care Utilization of Fifth Grade Children

PubMed Central

Coker, Tumaini R.; Elliott, Marc N.; Kataoka, Sheryl; Schwebel, David C.; Mrug, Sylvie; Grunbaum, Jo Anne; Cuccaro, Paula; Peskin, Melissa F.; Schuster, Mark A.

2015-01-01

Objective The aim of this study was to examine racial/ethnic differences in fifth grade children’s mental health care utilization. Methods We analyzed cross-sectional data from a study of 5147 fifth graders and their parents in 3 US metropolitan areas from 2004–06. Multivariate logistic regression was used to examine racial/ethnic differences in mental health care utilization. Results Nine percent of parents reported that their child had ever used mental health care services; fewer black (6%) and Hispanic (8%) children had used services than white children (14%). Fewer black and Hispanic children with recent symptoms of attention-deficit/hyperactivity disorder, oppositional defiant disorder, and conduct disorder, and fewer black children with symptoms of depression had ever utilized services compared with white children. In multivariate analyses controlling for demographic factors, parental mental health, social support, and symptoms of the 4 mental health conditions, we found that black children were less likely than white children to have ever used services (Odds ratio [OR] 0.3, 95% confidence interval [95% CI], 0.2–0.4, P <.001). The odds ratio for black children remained virtually unchanged when the analysis was restricted to children with symptoms of ≥1 mental health condition, and when the analysis was stratified by mental health condition. The difference in utilization for Hispanic compared with white children was fully explained by sociodemographics in all multivariate models. Conclusions Disparities exist in mental health care utilization for black and Hispanic children; the disparity for black children is independent of sociodemographics and child mental health need. Efforts to reduce this disparity may benefit from addressing not only access and diagnosis issues, but also parents’ help-seeking preferences for mental health care for their children. PMID:19329099

Impact of specialized inpatient IBD care on outcomes of IBD hospitalizations: A cohort study

PubMed Central

Law, Cindy CY; Sasidharan, Saranya; Rodrigues, Rodrigo; Nguyen, Deanna D; Sauk, Jenny; Garber, John; Giallourakis, Cosmas; Xavier, Ramnik; Khalili, Hamed; Yajnik, Vijay; Ananthakrishnan, Ashwin N

2016-01-01

Background The management of inflammatory bowel diseases (IBD; Crohn’s disease (CD), ulcerative colitis (UC)) is increasingly complex. Specialized care has been associated with improved ambulatory IBD outcomes. Aims To examine if the implementation of specialized inpatient IBD care modified short and long-term clinical outcomes in IBD-related hospitalizations. Methods This retrospective cohort study included IBD patients hospitalized between July 2013 and April 2015 at a single tertiary referral center where a specialized inpatient IBD care model was implemented in July 2014. In-hospital medical and surgical outcomes as well as post-discharge outcomes at 30 and 90 days were analyzed along with measures of quality of in-hospital care. Effect of specialist IBD care was examined on multivariate analysis. Results A total of 408 IBD-related admissions were included. With implementation of specialized IBD inpatient care, we observed increased frequency of use of high-dose biologic therapy for induction (26% vs. 9%, odds ratio (OR) 5.50, 95% CI 1.30 – 23.17) and higher proportion of patients in remission at 90 days after discharge (multivariate OR 1.60, 95% CI 0.99 – 2.69). While there was no difference in surgery by 90 days, among those who underwent surgery, early surgery defined as in-hospital or within 30 days of discharge, was more common in the study period (71%) compared to the control period (46%, multivariate OR 2.73, 95% CI 1.22 – 6.12). There was no difference in length of stay between the two years. Conclusions Implementation of specialized inpatient IBD care beneficially impacted remission and facilitated early surgical treatment. PMID:27482978

Association of Low-Grade Albuminuria with Adverse Cardiac Mechanics: Findings from the HyperGEN Study

PubMed Central

Katz, Daniel H.; Selvaraj, Senthil; Aguilar, Frank G.; Martinez, Eva E.; Beussink, Lauren; Kim, Kwang-Youn A.; Peng, Jie; Sha, Jin; Irvin, Marguerite R.; Eckfeldt, John H.; Turner, Stephen T.; Freedman, Barry I.; Arnett, Donna K.; Shah, Sanjiv J.

2013-01-01

Introduction Albuminuria is a marker of endothelial dysfunction and has been associated with adverse cardiovascular outcomes. The reasons for this association are unclear, but may be due to the relationship between endothelial dysfunction and intrinsic myocardial dysfunction. Methods and Results In the HyperGEN study, a population- and family-based study of hypertension, we examined the relationship between urine albumin-to-creatinine ratio (UACR) and cardiac mechanics (N=1894, all of whom had normal left ventricular ejection fraction and wall motion). We performed speckle-tracking echocardiographic analysis to quantify global longitudinal, circumferential, and radial strain (GLS, GCS, and GRS, respectively), and early diastolic (e′) tissue velocities. We used E/e′ ratio as a marker of increased LV filling pressures. We used multivariable-adjusted linear mixed effect models to determine independent associations between UACR and cardiac mechanics. The mean age was 50±14 years, 59% were female, and 46% were African-American. Comorbidities were increasingly prevalent among higher UACR quartiles. Albuminuria was associated with GLS, GCS, GRS, e′ velocity, and E/e′ ratio on unadjusted analyses. After adjustment for covariates, UACR was independently associated with lower absolute GLS (multivariable-adjusted mean GLS [95% CI] for UACR Quartile 1 = 15.3 [15.0–15.5]% vs. UACR Q4 = 14.6 [14.3–14.9]%, P for trend <0.001) and increased E/e′ ratio (Q1 = 25.3 [23.5–27.1] vs. Q4 = 29.0 [27.0–31.0], P= 0.003). The association between UACR and GLS was present even in participants with UACR < 30 mg/g (P<0.001 after multivariable adjustment). Conclusions Albuminuria, even at low levels, is associated with adverse cardiac mechanics and higher E/e′ ratio. PMID:24077169

Concentration-Dependent Antagonism and Culture Conversion in Pulmonary Tuberculosis

PubMed Central

Pasipanodya, Jotam G.; Denti, Paolo; Sirgel, Frederick; Lesosky, Maia; Gumbo, Tawanda; Meintjes, Graeme; McIlleron, Helen; Wilkinson, Robert J.

2017-01-01

Abstract Background. There is scant evidence to support target drug exposures for optimal tuberculosis outcomes. We therefore assessed whether pharmacokinetic/pharmacodynamic (PK/PD) parameters could predict 2-month culture conversion. Methods. One hundred patients with pulmonary tuberculosis (65% human immunodeficiency virus coinfected) were intensively sampled to determine rifampicin, isoniazid, and pyrazinamide plasma concentrations after 7–8 weeks of therapy, and PK parameters determined using nonlinear mixed-effects models. Detailed clinical data and sputum for culture were collected at baseline, 2 months, and 5–6 months. Minimum inhibitory concentrations (MICs) were determined on baseline isolates. Multivariate logistic regression and the assumption-free multivariate adaptive regression splines (MARS) were used to identify clinical and PK/PD predictors of 2-month culture conversion. Potential PK/PD predictors included 0- to 24-hour area under the curve (AUC0-24), maximum concentration (Cmax), AUC0-24/MIC, Cmax/MIC, and percentage of time that concentrations persisted above the MIC (%TMIC). Results. Twenty-six percent of patients had Cmax of rifampicin <8 mg/L, pyrazinamide <35 mg/L, and isoniazid <3 mg/L. No relationship was found between PK exposures and 2-month culture conversion using multivariate logistic regression after adjusting for MIC. However, MARS identified negative interactions between isoniazid Cmax and rifampicin Cmax/MIC ratio on 2-month culture conversion. If isoniazid Cmax was <4.6 mg/L and rifampicin Cmax/MIC <28, the isoniazid concentration had an antagonistic effect on culture conversion. For patients with isoniazid Cmax >4.6 mg/L, higher isoniazid exposures were associated with improved rates of culture conversion. Conclusions. PK/PD analyses using MARS identified isoniazid Cmax and rifampicin Cmax/MIC thresholds below which there is concentration-dependent antagonism that reduces 2-month sputum culture conversion. PMID:28205671

Disparities in the Use of Radiation Therapy in Patients With Local-Regionally Advanced Breast Cancer

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

Martinez, Steve R., E-mail: steve.martinez@ucdmc.ucdavis.ed; Beal, Shannon H.; Chen, Steven L.

2010-11-01

Background: Radiation therapy (RT) is indicated for the treatment of local-regionally advanced breast cancer (BCa). Hypothesis: We hypothesized that black and Hispanic patients with local-regionally advanced BCa would receive lower rates of RT than their white counterparts. Methods: The Surveillance Epidemiology and End Results database was used to identify white, black, Hispanic, and Asian patients with invasive BCa and {>=}10 metastatic lymph nodes diagnosed between 1988 and 2005. Univariate and multivariate logistic regression evaluated the relationship of race/ethnicity with use of RT. Multivariate models stratified for those undergoing mastectomy or lumpectomy. Results: Entry criteria were met by 12,653 patients. Approximatelymore » half of the patients did not receive RT. Most patients were white (72%); the remainder were Hispanic (10.4%), black (10.3%), and Asian (7.3%). On univariate analysis, Hispanics (odd ratio [OR] 0.89; 95% confidence interval [CI], 0.79-1.00) and blacks (OR 0.79; 95% CI, 0.70-0.89) were less likely to receive RT than whites. On multivariate analysis, blacks (OR 0.76; 95% CI, 0.67-0.86) and Hispanics (OR 0.80; 95% CI, 0.70-0.90) were less likely than whites to receive RT. Disparities persisted for blacks (OR 0.74; 95% CI, 0.64-0.85) and Hispanics (OR 0.77; 95% CI, 0.67-0.89) who received mastectomy, but not for those who received lumpectomy. Conclusions: Many patients with local-regionally advanced BCa do not receive RT. Blacks and Hispanics were less likely than whites to receive RT. This disparity was noted predominately in patients who received mastectomy. Future efforts at improving rates of RT are warranted. Efforts at eliminating racial/ethnic disparities should focus on black and Hispanic candidates for postmastectomy RT.« less

Joint coverage probability in a simulation study on Continuous-Time Markov Chain parameter estimation.

PubMed

Benoit, Julia S; Chan, Wenyaw; Doody, Rachelle S

2015-01-01

Parameter dependency within data sets in simulation studies is common, especially in models such as Continuous-Time Markov Chains (CTMC). Additionally, the literature lacks a comprehensive examination of estimation performance for the likelihood-based general multi-state CTMC. Among studies attempting to assess the estimation, none have accounted for dependency among parameter estimates. The purpose of this research is twofold: 1) to develop a multivariate approach for assessing accuracy and precision for simulation studies 2) to add to the literature a comprehensive examination of the estimation of a general 3-state CTMC model. Simulation studies are conducted to analyze longitudinal data with a trinomial outcome using a CTMC with and without covariates. Measures of performance including bias, component-wise coverage probabilities, and joint coverage probabilities are calculated. An application is presented using Alzheimer's disease caregiver stress levels. Comparisons of joint and component-wise parameter estimates yield conflicting inferential results in simulations from models with and without covariates. In conclusion, caution should be taken when conducting simulation studies aiming to assess performance and choice of inference should properly reflect the purpose of the simulation.

[Association between physical fitness parameters and health related quality of life in Chilean community-dwelling older adults].

PubMed

Guede Rojas, Francisco; Chirosa Ríos, Luis Javier; Fuentealba Urra, Sergio; Vergara Ríos, César; Ulloa Díaz, David; Campos Jara, Christian; Barbosa González, Paola; Cuevas Aburto, Jesualdo

2017-01-01

There is no conclusive evidence about the association between physical fitness (PF) and health related quality of life (HRQOL) in older adults. To seek for an association between PF and HRQOL in non-disabled community-dwelling Chilean older adults. One hundred and sixteen subjects participated in the study. PF was assessed using the Senior Fitness Test (SFT) and hand grip strength (HGS). HRQOL was assessed using eight dimensions provided by the SF-12v2 questionnaire. Binary multivariate logistic regression models were carried out considering the potential influence of confounder variables. Non-adjusted models, indicated that subjects with better performance in arm curl test (ACT) were more likely to score higher on vitality dimension (OR > 1) and those with higher HGS were more likely to score higher on physical functioning, bodily pain, vitality and mental health (OR > 1). The adjusted models consistently showed that ACT and HGS predicted a favorable perception of vitality and mental health dimensions respectively (OR > 1). HGS and ACT have a predictive value for certain dimensions of HRQOL.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Novel, Objective, Multivariate Biomarkers Composed of Plasma Amino Acid Profiles for the Diagnosis and Assessment of Inflammatory Bowel Disease

PubMed Central

Hisamatsu, Tadakazu; Okamoto, Susumu; Hashimoto, Masaki; Muramatsu, Takahiko; Andou, Ayatoshi; Uo, Michihide; Kitazume, Mina T.; Matsuoka, Katsuyoshi; Yajima, Tomoharu; Inoue, Nagamu; Kanai, Takanori; Ogata, Haruhiko; Iwao, Yasushi; Yamakado, Minoru; Sakai, Ryosei; Ono, Nobukazu; Ando, Toshihiko; Suzuki, Manabu; Hibi, Toshifumi

2012-01-01

Background Inflammatory bowel disease (IBD) is a chronic intestinal disorder that is associated with a limited number of clinical biomarkers. In order to facilitate the diagnosis of IBD and assess its disease activity, we investigated the potential of novel multivariate indexes using statistical modeling of plasma amino acid concentrations (aminogram). Methodology and Principal Findings We measured fasting plasma aminograms in 387 IBD patients (Crohn's disease (CD), n = 165; ulcerative colitis (UC), n = 222) and 210 healthy controls. Based on Fisher linear classifiers, multivariate indexes were developed from the aminogram in discovery samples (CD, n = 102; UC, n = 102; age and sex-matched healthy controls, n = 102) and internally validated. The indexes were used to discriminate between CD or UC patients and healthy controls, as well as between patients with active disease and those in remission. We assessed index performances using the area under the curve of the receiver operating characteristic (ROC AUC). We observed significant alterations to the plasma aminogram, including histidine and tryptophan. The multivariate indexes established from plasma aminograms were able to distinguish CD or UC patients from healthy controls with ROC AUCs of 0.940 (95% confidence interval (CI): 0.898–0.983) and 0.894 (95%CI: 0.853–0.935), respectively in validation samples (CD, n = 63; UC, n = 120; healthy controls, n = 108). In addition, other indexes appeared to be a measure of disease activity. These indexes distinguished active CD or UC patients from each remission patients with ROC AUCs of 0.894 (95%CI: 0.853–0.935) and 0.849 (95%CI: 0.770–0.928), and correlated with clinical disease activity indexes for CD (rs = 0.592, 95%CI: 0.385–0.742, p<0.001) or UC (rs = 0.598, 95%CI: 0.452–0.713, p<0.001), respectively. Conclusions and Significance In this study, we demonstrated that established multivariate indexes composed of plasma amino acid profiles can serve as novel, non-invasive, objective biomarkers for the diagnosis and monitoring of IBD, providing us with new insights into the pathophysiology of the disease. PMID:22303484

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

The effect of molecular subtype and body mass index on neo-adjuvant chemotherapy in breast cancer patients.

PubMed

Iwase, Toshiaki; Nakamura, Rikiya; Yamamoto, Naohito; Yoshi, Atushi; Itami, Makiko; Miyazaki, Masaru

2014-06-01

The aim of the present study was to analyze the effect of subtype and body mass index (BMI) on neo-adjuvant chemotherapy (NAC) and postoperative prognosis. Two-hundred and forty nine patients who underwent surgery after NAC were included. A multivariate analysis and survival analysis were used to clarify the relationship between BMI, subtype, and NAC. In the logistic regression model, the pCR rate had a significant relationship with the subtype and tumor stage. In the non-pCR group, more overweight patients had significantly a worse disease-free survival (DFS) compared to normal range patients (Log lank test, p < 0.05). In the Cox proportional hazards model, subtype and tumor stage were significantly associated with decreased DFS. In conclusion, patients with the ER (+), HER (-) type and a high BMI had a high risk for recurrence when they achieved non-pCR after NAC. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Comparative Study of Thought Fusion Beliefs and Thought Control Strategies in Patient With Obsessive-Compulsive Disorder, Major Depressive Disorder and Normal People

PubMed Central

Amiri Pichakolaei, Ahmad; Fahimi, Samad; Bakhshipour Roudsari, Abbas; Fakhari, Ali; Akbari, Ebrahim; Rahimkhanli, Masoumeh

2014-01-01

Objective: The present study aimed to investigate the metacognitive model of obsessive-compulsive disorder (OCD), through a comparative study of thought fusion beliefs and thought control strategies between patients with OCD, depression, and normal people. Methods: This is a causal-comparative study. About 20 patients were selected with OCD, and 20 patients with major depression disorder (MDD), and 20 normal individuals. Participants completed a thought fusion instrument and thought control questionnaire. Data were analyzed using multivariate analysis of variance. Results: Results indicated that patients with OCD obtained higher scores than two other groups. Also, there was a statistical significant difference between the three groups in thought control strategies and punishment, worry, and distraction subscales. Conclusion: Therefore, the results of the present study supported the metacognitive model of obsessive and showed thought fusion beliefs and thought control strategies can be effective in onset and continuity of OCD. PMID:25780373

Exploring the association of homicides in northern Mexico and healthcare access for US residents

PubMed Central

Geissler, Kimberley; Becker, Charles; Stearns, Sally; Thirumurthy, Harsha; Holmes, George M.

2016-01-01

Background Many legal residents in the United States (US)-Mexico border region cross from the US into Mexico for medical treatment and pharmaceuticals. We analyzed whether recent increases in homicides in Mexico are associated with reduced healthcare access for US border residents. Methods We used data on healthcare access, legal entries to the US from Mexico, and Mexican homicide rates (2002–2010). Poisson regression models estimated associations between homicide rates and total legal US entries. Multivariate difference-in-difference linear probability models evaluated associations between Mexican homicide rates and self-reported measures of healthcare access for US residents. Results Increased homicide rates were associated with decreased legal entries to the US from Mexico. Contrary to expectations, homicides did not have significant associations with healthcare access measures for legal residents in US border counties. Conclusions Despite a decrease in border crossings, increased violence in Mexico did not appear to negatively affect access for US border residents. PMID:24917240

Incidence of silicosis among ceramic workers in central Italy.

PubMed

Cavariani, F; Di Pietro, A; Miceli, M; Forastiere, F; Biggeri, A; Scavalli, P; Petti, A; Borgia, P

1995-01-01

The incidence of radiological silicosis was studied among 2480 male workers employed in the ceramics industry. The subjects entered the surveillance program during 1974-1987 and were followed through 1991 with annual chest radiographs. The cumulative risk of silicosis (1/1 or greater; p,q,r) reached 48% (95% confidence interval 41.5-54.9) after 30 years of employment. In a multivariate Cox's proportional hazards model, the effect of duration of exposure increased linearly up to the category of 25-29 years; an extremely high hazard risk of 14.6 was found among those with 30 years or more of exposure in comparison with those employed 10 years or less. Smoking habit also significantly contributed to the model, although its role in the biological process is unclear. In conclusion, exposure to silica dust has been associated with a high incidence of silicosis among ceramics workers. The risk estimates are consistent with the recent findings of silicosis incidence among South African gold miners.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

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

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836