title: Bayesian semiparametric nonlinear mixed-effects joint models for data with skewness, missing responses, and measurement errors in covariates.
abstract: It is a common practice to analyze complex longitudinal data using semiparametric nonlinear mixed-effects (SNLME) models with a normal distribution. Normality assumption of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models, but some covariates may often be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. Inferential procedures can be complicated dramatically when data with skewness, missing values, and measurement error are observed. In the literature, there has been considerable interest in accommodating either skewness, incompleteness or covariate measurement error in such models, but there has been relatively little study concerning all three features simultaneously. In this article, our objective is to address the simultaneous impact of skewness, missingness, and covariate measurement error by jointly modeling the response and covariate processes based on a flexible Bayesian SNLME model. The method is illustrated using a real AIDS data set to compare potential models with various scenarios and different distribution specifications.
authors.affiliation: Department of Epidemiology & Biostatistics, College of Public Health, University of South Florida, Tampa, Florida 33612, USA. yhuang@health.usf.edu
authors.forename: Yangxin
authors.initials: Y
authors.lastname: Huang
authors.forename: Getachew
authors.initials: G
authors.lastname: Dagne
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001499:Bayes Theorem; D001699:Biometry; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology / Q000821:virology; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D019562:Viral Load / Q000187:drug effects