The recurrent event data are encountered in many longitudinal studies. In many situations, there are a nonsusceptible fraction of subjects without any recurrent events, manifesting heterogeneity in risk among study participants. The frailty models have been widely used to account for this heterogeneity that does not rely on self‐reported survival data. Frailty is commonly modelled with a gamma distribution because of mathematical convenience, not based on biological reasons. One model that accommodates zero inflation and a continuous frailty is based on the compound Poisson model. On the other hand, dependent termination may occur, which could be correlated with the recurrent events. In this paper, we consider a joint model of recurrent event process and dependent termination under a Bayesian framework in which a shared compound Poisson frailty is used to account the association between the intensity of the recurrent event process and the hazard of the dependent termination. In addition, the proportion of participants insusceptible to the event is estimated. We assess model performance via simulation and apply the model to data from a cohort of achalasia patients. Simulation results suggest that misspecifying frailty distributions such as the gamma distribution when faced with zero‐inflated recurrent events may introduce bias in regression coefficients estimation.

中文翻译：

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复合膨胀泊松脆弱模型的零膨胀反复事件和相关终止的贝叶斯建模

在许多纵向研究中都遇到了复发事件数据。在许多情况下，有一部分受试者没有任何复发事件，这是不敏感的，这表明研究参与者的风险存在异质性。脆弱模型已被广泛用于解释这种不均一性，这种异质性不依赖于自我报告的生存数据。由于数学上的便利性（而不是基于生物学的原因），通常用伽玛分布来建模脆弱性。一种适应零通胀和持续脆弱的模型是基于复合泊松模型的。另一方面，可能发生依赖终止，这可能与复发事件相关。在本文中，我们考虑在贝叶斯框架下的重复事件过程和相关终止的联合模型，其中使用共享复合泊松脆弱性来说明重复事件过程的强度与相关终止危险之间的关联。另外，估计不易受事件影响的参与者的比例。我们通过模拟评估模型的性能，并将模型应用于来自一群门失弛缓患者的数据。仿真结果表明，当面对零膨胀的反复事件时，错误地指定脆弱性分布（例如伽马分布）可能会在回归系数估计中引入偏差。估计该事件不敏感的参与者的比例。我们通过模拟评估模型的性能，并将模型应用于来自一群门失弛缓患者的数据。仿真结果表明，当面对零膨胀的反复事件时，错误地指定脆弱性分布（例如伽马分布）可能会在回归系数估计中引入偏差。估计该事件不敏感的参与者的比例。我们通过模拟评估模型的性能，并将模型应用于来自一群门失弛缓患者的数据。仿真结果表明，当面对零膨胀的反复事件时，错误地指定脆弱性分布（例如伽马分布）可能会在回归系数估计中引入偏差。