title: Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.
abstract: Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-event data by using a Cox proportional hazards model with piecewise constant baseline hazard. We use a Bayesian framework via Markov chain Monte Carlo simulations implemented in the BUGS programming language. Via an extensive simulation study, we apply the joint model and obtain estimates that are more accurate than those of the corresponding independence model. We apply the proposed method to an alpha-tocopherol, beta-carotene lung cancer prevention study.
authors.affiliation: Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Huirong
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Stacia M
authors.initials: SM
authors.lastname: DeSantis
authors.affiliation: Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Sheng
authors.initials: S
authors.lastname: Luo
mesh_terms: D001499:Bayes Theorem; D035843:Biomedical Research / Q000706:statistics & numerical data; D008137:Longitudinal Studies; D008390:Markov Chains; D009010:Monte Carlo Method; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D016012:Poisson Distribution; D016019:Survival Analysis
keywords: Joint model; Markov chain Monte Carlo; count data; mixed model; zero-inflation generalized Poisson