Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials.

TitleBayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials.
Publication TypeJournal Article
Year of Publication2017
AuthorsZhang, Danjie, Ming-Hui Chen, Joseph G. Ibrahim, Mark E. Boye, and Wei Shen
JournalJ Comput Graph Stat
Volume26
Issue1
Pagination121-133
Date Published2017
ISSN1061-8600
Abstract

Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes (PROs). In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the Conditional Predictive Ordinate (CPO) statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma.

DOI10.1080/10618600.2015.1117472
Alternate JournalJ Comput Graph Stat
Original PublicationBayesian model assessment in joint modeling of longitudinal and survival data with applications to cancer clinical trials.
PubMed ID28239247
PubMed Central IDPMC5321618
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States