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

Khaled F. Bedair, Yili Hong (Lead / Corresponding author), Hussein R. Al-Khalidi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
43 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)3530-3548
Number of pages19
JournalJournal of Statistical Computation and Simulation
Issue number17
Early online date17 Jun 2021
Publication statusPublished - 2021


  • Clinical trial
  • MCEM algorithm
  • multi-type recurrences
  • multivariate frailty
  • skin cancers
  • survival models

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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