Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event
针对临床研究中常见的聚类复发事件数据，提出贝叶斯共享随机效应模型，处理零膨胀和终止事件，并应用于跌倒损伤预防的聚类随机试验。
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.