Copula-Frailty Models for Recurrent Event Data Based on Monte Carlo EM Algorithm

TL;DR

This paper introduces copula-frailty models combined with a Monte Carlo EM algorithm to analyze multi-type recurrent event data, effectively capturing dependence structures and covariate effects in complex medical studies.

Contribution

The paper develops a novel MCEM-based estimation approach for copula-frailty models handling multiple event types, improving analysis of correlated recurrent events.

Findings

Effective modeling of multi-type recurrent events in simulations

Successful application to skin cancer recurrence data

Enhanced understanding of dependence among event types

Abstract

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.