title: Simultaneous inference for semiparametric mixed-effects joint models with skew distribution and covariate measurement error for longitudinal competing risks data analysis.
abstract: Semiparametric mixed-effects joint models are flexible for modeling complex longitudinal-competing risks data. Skew distributions are commonly observed for this type of data. Covariates in the joint models are usually measured with substantial errors. We propose a Bayesian method for semiparametric mixed-effects joint models with covariate measurement errors and skew distribution. The methods are illustrated with AIDS clinical data. Simulation results are conducted to validate the proposed methods.
authors.affiliation: a Department of Mathematics and Statistics , University of Nevada , Reno , Nevada , USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Lu
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000453:epidemiology; D001499:Bayes Theorem; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods / Q000706:statistics & numerical data*; D011446:Prospective Studies; D012107:Research Design / Q000706:statistics & numerical data*; D012307:Risk Factors
keywords: Bayesian inference; competing risks; longitudinal data; measurement error; partially linear mixed-effects models; proportional hazard models; skew distribution; survival data