Copula-frailty models for recurrent event data based on Monte Carlo EM algorithm

作     者：Bedair, Khaled F. Hong, Yili Al-Khalidi, Hussein R. 

作者机构：Tanta Univ Fac Commerce Tanta Egypt Univ Dundee Sch Med Dundee Scotland Virginia Tech Dept Stat Blacksburg VA 24061 USA Duke Univ Dept Biostat & Bioinformat Durham NC USA 

出 版 物：《JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION》 (统计计算与模拟杂志)

年 卷 期：2021年第91卷第17期

页      面：3530-3548页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation [CMMI-1904165] 

主　　题：Clinical trial MCEM algorithm multi-type recurrences multivariate frailty skin cancers survival models 

摘      要：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.