title: Bayesian quantile regression for nonlinear mixed-effects joint models for longitudinal data in the presence of mismeasured covariate errors.
abstract: Quantile regression (QR) models have recently received increasing attention in longitudinal studies where measurements of the same individuals are taken repeatedly over time. When continuous (longitudinal) responses follow a distribution that is quite different from a normal distribution, usual mean regression (MR)-based linear models may fail to produce efficient estimators, whereas QR-based linear models may perform satisfactorily. To the best of our knowledge, there have been very few studies on QR-based nonlinear models for longitudinal data in comparison to MR-based nonlinear models. In this article, we study QR-based nonlinear mixed-effects (NLME) joint models for longitudinal data with non-central location and outliers and/or heavy tails in response, and non-normality and measurement errors in covariate under Bayesian framework. The proposed QR-based modeling method is compared with an MR-based one by an AIDS clinical dataset and through simulation studies. The proposed QR joint modeling approach can be not only applied to AIDS clinical studies, but also may have general applications in other fields as long as relevant technical specifications are met.
authors.affiliation: a Department of Epidemiology and Biostatistics , University of South Florida , Tampa , Florida , USA.|b Department of Statistics, College of Science , Wuhan University of Technology , Wuhan , Hubei , P.R. China.|c School of Mathematics and Computers , Wuhan Textile University , Wuhan , P.R. China.
authors.forename: Yangxin
authors.initials: Y
authors.lastname: Huang
authors.affiliation: b Department of Statistics, College of Science , Wuhan University of Technology , Wuhan , Hubei , P.R. China.
authors.forename: Jiaqing
authors.initials: J
authors.lastname: Chen
authors.affiliation: c School of Mathematics and Computers , Wuhan Textile University , Wuhan , P.R. China.
authors.forename: Huahai
authors.initials: H
authors.lastname: Qiu
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000097:blood / Q000453:epidemiology / Q000628:therapy; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004311:Double-Blind Method; D006801:Humans; D008137:Longitudinal Studies; D017711:Nonlinear Dynamics; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012044:Regression Analysis
keywords: AIDS clinical data; Bayesian analysis; QR-based joint models; asymmetric Laplace distribution; covariate measurement error; longitudinal data; skew-normal distribution