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Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.

Authors
  • Zhu, Huirong1
  • DeSantis, Stacia M1
  • Luo, Sheng1
  • 1 Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
Type
Published Article
Journal
Statistical Methods in Medical Research
Publisher
SAGE Publications
Publication Date
Apr 01, 2018
Volume
27
Issue
4
Pages
1258–1270
Identifiers
DOI: 10.1177/0962280216659312
PMID: 27460540
Source
Medline
Keywords
License
Unknown

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.

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