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 l

SUBMITTER: Zhu H 

PROVIDER: S-EPMC5269555 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature