The Experts below are selected from a list of 18159 Experts worldwide ranked by ideXlab platform
Emmanuel Lesaffre - One of the best experts on this subject based on the ideXlab platform.
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generalized linear mixed model with a penalized gaussian mixture as a random effects distribution
Computational Statistics & Data Analysis, 2008Co-Authors: Arnost Komarek, Emmanuel LesaffreAbstract:Generalized linear mixed models are popular for regressing a discrete response when there is clustering, e.g. in longitudinal studies or in hierarchical data structures. It is standard to assume that the random effects have a normal distribution. Recently, it has been examined whether wrongly assuming a normal distribution for the random effects is important for the estimation of the fixed effects parameters. While it has been shown that misspecifying the distribution of the random effects has a minor effect in the context of linear mixed models, the conclusion for generalized mixed models is less clear. Some studies report a minor impact, while others report that the Assumption of Normality really matters especially when the variance of the random effect is relatively high. Since it is unclear whether the Normality Assumption is truly satisfied in practice, it is important that generalized mixed models are available which relax the Normality Assumption. A replacement of the normal distribution with a mixture of Gaussian distributions specified on a grid whereby only the weights of the mixture components are estimated using a penalized approach ensuring a smooth distribution for the random effects is proposed. The parameters of the model are estimated in a Bayesian context using MCMC techniques. The usefulness of the approach is illustrated on two longitudinal studies using R-functions.
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a linear mixed effects model with heterogeneity in the random effects population
Journal of the American Statistical Association, 1996Co-Authors: Geert Verbeke, Emmanuel LesaffreAbstract:Abstract This article investigates the impact of the Normality Assumption for random effects on their estimates in the linear mixed-effects model. It shows that if the distribution of random effects is a finite mixture of normal distributions, then the random effects may be badly estimated if Normality is assumed, and the current methods for inspecting the appropriateness of the model Assumptions are not sound. Further, it is argued that a better way to detect the components of the mixture is to build this Assumption in the model and then “compare” the fitted model with the Gaussian model. All of this is illustrated on two practical examples.
Hrishikesh Chakraborty - One of the best experts on this subject based on the ideXlab platform.
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bivariate random effect model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random effects models are commonly used to model correlated data. Mostly in the longitudinal data setting we assume that the random effects and within subject errors are normally distributed. However, the Normality Assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed model and relax the Normality Assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential models and illustrate the procedure using a real data set from HIV study.
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bivariate random effect model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random effects models are commonly used to model correlated data. Mostly in the longitudinal data setting we assume that the random effects and within subject errors are normally distributed. However, the Normality Assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed model and relax the Normality Assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential models and illustrate the procedure using a real data set from HIV study. Copyright © 2006 John Wiley & Sons, Ltd.
Mark E Mcgovern - One of the best experts on this subject based on the ideXlab platform.
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on the Assumption of joint Normality in selection models a copula based approach applied to estimating hiv prevalence
2015Co-Authors: Mark E Mcgovern, Till Barnighausen, Giampiero Marra, Rosalba RadiceAbstract:- Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong Assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. - Methods: We introduce a novel approach for relaxing the bivariate Normality Assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). - Results: We show in a simulation study that selection models can generate biased results when the bivariate Normality Assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%). - Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.
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on the Assumption of bivariate Normality in selection models a copula approach applied to estimating hiv prevalence
Epidemiology, 2015Co-Authors: Mark E Mcgovern, Till Barnighausen, Giampiero Marra, Rosalba RadiceAbstract:Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong Assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. Methods: We introduce a novel approach for relaxing the bivariate Normality Assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). Results: We show in a simulation study that selection models can generate biased results when the bivariate Normality Assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%). Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.
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on the Assumption of bivariate Normality in selection models a copula approach applied to estimating hiv prevalence
Research Papers in Economics, 2015Co-Authors: Mark E Mcgovern, Till Barnighausen, Giampiero Marra, Rosalba RadiceAbstract:Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong Assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. We introduce a novel approach for relaxing the bivariate Normality Assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). We show in a simulation study that selection models can generate biased results when the bivariate Normality Assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%?25%) compared with 12% (11%?13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%?24%) and 16% (15%?17%). Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.
Rosalba Radice - One of the best experts on this subject based on the ideXlab platform.
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on the Assumption of joint Normality in selection models a copula based approach applied to estimating hiv prevalence
2015Co-Authors: Mark E Mcgovern, Till Barnighausen, Giampiero Marra, Rosalba RadiceAbstract:- Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong Assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. - Methods: We introduce a novel approach for relaxing the bivariate Normality Assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). - Results: We show in a simulation study that selection models can generate biased results when the bivariate Normality Assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%). - Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.
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on the Assumption of bivariate Normality in selection models a copula approach applied to estimating hiv prevalence
Epidemiology, 2015Co-Authors: Mark E Mcgovern, Till Barnighausen, Giampiero Marra, Rosalba RadiceAbstract:Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong Assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. Methods: We introduce a novel approach for relaxing the bivariate Normality Assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). Results: We show in a simulation study that selection models can generate biased results when the bivariate Normality Assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%). Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.
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on the Assumption of bivariate Normality in selection models a copula approach applied to estimating hiv prevalence
Research Papers in Economics, 2015Co-Authors: Mark E Mcgovern, Till Barnighausen, Giampiero Marra, Rosalba RadiceAbstract:Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong Assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal. We introduce a novel approach for relaxing the bivariate Normality Assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status). We show in a simulation study that selection models can generate biased results when the bivariate Normality Assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%?25%) compared with 12% (11%?13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%?24%) and 16% (15%?17%). Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.
Pulak Ghosh - One of the best experts on this subject based on the ideXlab platform.
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bivariate random effect model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random effects models are commonly used to model correlated data. Mostly in the longitudinal data setting we assume that the random effects and within subject errors are normally distributed. However, the Normality Assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed model and relax the Normality Assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential models and illustrate the procedure using a real data set from HIV study.
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bivariate random effect model using skew normal distribution with application to hiv rna
Statistics in Medicine, 2007Co-Authors: Pulak Ghosh, Marcia D Branco, Hrishikesh ChakrabortyAbstract:Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random effects models are commonly used to model correlated data. Mostly in the longitudinal data setting we assume that the random effects and within subject errors are normally distributed. However, the Normality Assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed model and relax the Normality Assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential models and illustrate the procedure using a real data set from HIV study. Copyright © 2006 John Wiley & Sons, Ltd.
