Bayesian joint models for longitudinal, recurrent, and terminal event data

Emily M. Damone (), Matthew A. Psioda and Joseph G. Ibrahim
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Emily M. Damone: Department of Biostatistics - University of North Carolina at Chapel Hill
Matthew A. Psioda: GlaxoSmithKline
Joseph G. Ibrahim: Department of Biostatistics - University of North Carolina at Chapel Hill

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 4, No 9, 932-949

Abstract: Abstract Many methods exist to jointly model either recurrent and related terminal survival events or longitudinal outcome measures and related terminal survival event. However, few methods exist which can account for the dependency between all three outcomes of interest, and none allow for the modeling of all three outcomes without strong correlation assumptions. We propose a joint model which uses subject-specific random effects to connect the survival model (terminal and recurrent events) with a longitudinal outcome model. In the proposed method, proportional hazards models with shared frailties are used to model dependence between the recurrent and terminal events, while a separate (but correlated) set of random effects are utilized in a generalized linear mixed model to model dependence with longitudinal outcome measures. All random effects are related based on an assumed multivariate normal distribution. The proposed joint modeling approach allows for flexible models, particularly for unique longitudinal trajectories, that can be utilized in a wide range of health applications. We evaluate the model through simulation studies as well as through an application to data from the Atherosclerosis Risk in Communities (ARIC) study.

Keywords: Joint models; Bayesian methods; Longitudinal analysis; Recurrent events (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10985-025-09673-y

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