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Recurrent event data are common in clinical studies when participants arefollowed longitudinally, and are often subject to a terminal event. With theincreasing popularity of large pragmatic trials with a heterogeneous sourcepopulation, participants are often nested in clinics and can be eithersusceptible or structurally unsusceptible to the recurrent process. Thesecomplications require new modeling strategies to accommodate potentialzero-event inflation as well as hierarchical data structures in both theterminal and non-terminal event processes. In this paper, we develop a Bayesiansemi-parametric model to jointly characterize the zero-inflated recurrent eventprocess and the terminal event process. We use a point mass mixture ofnon-homogeneous Poisson processes to describe the recurrent intensity andintroduce shared random effects from different sources to bridge thenon-terminal and terminal event processes. To achieve robustness, we considernonparametric Dirichlet processes to model the residual of the acceleratedfailure time model for the survival process as well as the cluster-specificfrailty distribution, and develop a Markov Chain Monte Carlo algorithm forposterior inference. We demonstrate the superiority of our proposed modelcompared with competing models via simulations and apply our method to apragmatic cluster randomized trial for fall injury prevention among theelderly.

Bayesian semi-parametric inference for clustered recurrent events with zero-inflation and a terminal event/4163305