Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event

Abstract Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommod...

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Published inJournal of the Royal Statistical Society Series C: Applied Statistics Vol. 73; no. 3; pp. 598 - 620
Main Authors Tian, Xinyuan, Ciarleglio, Maria, Cai, Jiachen, Greene, Erich J, Esserman, Denise, Li, Fan, Zhao, Yize
Format Journal Article
LanguageEnglish
Published UK Oxford University Press 01.06.2024
Subjects
Original
accelerated failure time model
semi-competing risks
Dirichlet process
pragmatic clinical trials
Bayesian survival analysis
zero inflation
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Summary:Abstract Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention.
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F.L. and Y.Z. are co-senior authors.
Conflict of interest: None declared.
ISSN:0035-9254
1467-9876
DOI:10.1093/jrsssc/qlae003