Multi-type recurrent events are often encountered in medical applications when two or more different event types could repeatedly occur over an observation period. For example, patients may experience recurrences of multi-type nonmelanoma skin cancers in a clinical trial for skin cancer prevention. The aims in those applications are to characterize features of the marginal processes, evaluate covariate effects, and quantify both the within-subject recurrence dependence and the dependence among different event types. We use copula-frailty models to analyze correlated recurrent events of different types. Parameter estimation and inference are carried out by using a Monte Carlo expectation-maximization (MCEM) algorithm, which can handle a relatively large (i.e. three or more) number of event types. Performances of the proposed methods are evaluated via extensive simulation studies. The developed methods are used to model the recurrences of skin cancer with different types.

当两种或多种不同的事件类型在观察期内可能重复发生时，在医学应用中经常会遇到多类型的复发事件。例如，在预防皮肤癌的临床试验中，患者可能会经历多类型非黑色素瘤皮肤癌的复发。这些应用程序的目的是表征边缘过程的特征，评估协变量效应，并量化主体内重复依赖和不同事件类型之间的依赖。我们使用 copula-failty 模型来分析不同类型的相关复发事件。参数估计和推理是通过使用蒙特卡罗期望最大化（MCEM）算法进行的，该算法可以处理相对较大（即三个或更多）数量的事件类型。通过广泛的模拟研究来评估所提出方法的性能。开发的方法用于模拟不同类型皮肤癌的复发。