Multivariate generalized linear frailty models for clustered competing risk data

Music City Center 

Competing risk data arises when the occurrence of one event prevents observation of another type of events. While competing risks are commonly encountered in biomedical research, failure to account for them can distort estimation of the effects of interest. A joint model for competing risks with shared random effects (frailty) could be useful to address this issue. In this study, we propose a joint modeling framework for analysis of clustered competing risk data, where the hazard for each competing risk event is assumed to be of the piecewise exponential form with a frailty shared between the events of individuals within each cluster. Based on the equivalence to Poisson regression likelihood, models of this approach can be fit under the framework of generalized linear mixed models. The performance of the proposed method was evaluated through an extensive simulation study. The proposed joint model showed stable estimation performance, even with a moderate correlation between the events, while univariate models not accounting for competing risks yielded considerable bias. The usefulness of the proposed method was demonstrated by application to real data from a clinical study.

survival analysis

competing risks

frailty

clustered survival data

biomedical data analysis 

Biometrics Section