Multivariate generalized linear frailty models for clustered competing risk data
Year: 2025
Type: article
Abstract: Clustered competing risk data occur when individuals within clusters are subject to multiple mutually exclusive event types, inducing dependence both across events and within clusters. We propose a joint modeling framework for such data based on a multivariate generalized linear frailty model. Cause-specific hazards are specified in a piecewise exponential form with shared cluster-level random effects to account for unobserved heterogeneity and between-event correlation. Using the equivalence between the piecewise exponential and Poisson regression likelihoods, estimation is performed under the generalized linear mixed model (GLMM) framework, allowing implementation with standard mixed-model software. Simulation studies show that the proposed method yields nearly unbiased and efficient estimation across a wide range of correlation structures, whereas conventional univariate and Cox-type frailty models exhibit bias or instability under moderate dependence. Application to multicenter clinical trial data illustrates the practical utility and interpretability of the proposed model. The approach offers a flexible and extensible framework for modeling clustered survival data with competing risks.
Authors: Teranishi, Ren, Furukawa, Kyoji, Emura, Takeshi
Institutions: Kurume University, Hiroshima University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7090740441
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