2004/08/27 by Lei Liu, Robert A. Wolfe, Xuelin Huang · 340 citations
Economics, Econometrics and Finance · Engineering · Mathematics · Social Sciences · #Artificial intelligence #Computer science #Covariate #Econometrics #Engineering #Estimation #Event (particle physics) #Hazard #Health Systems, Economic Evaluations, Quality of Life #Inference #Insurance, Mortality, Demography, Risk Management #Mathematics #Proportional hazards model #Statistical Methods and Inference #Statistics
paper · pdf · doi:10.1111/j.0006-341x.2004.00225.x
published in Biometrics 60(3), 747-756 (Oxford University Press)
openalex publication_date 2004/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
There has been an increasing interest in the analysis of recurrent event data (Cook and Lawless, 2002, Statistical Methods in Medical Research 11, 141-166). In many situations, a terminating event such as death can happen during the follow-up period to preclude further occurrence of the recurrent events. Furthermore, the death time may be dependent on the recurrent event history. In this article we consider frailty proportional hazards models for the recurrent and terminal event processes. The dependence is modeled by conditioning on a shared frailty that is included in both hazard functions. Covariate effects can be taken into account in the model as well. Maximum likelihood estimation and inference are carried out through a Monte Carlo EM algorithm with Metropolis-Hastings sampler in the E-step. An analysis of hospitalization and death data for waitlisted dialysis patients is presented to illustrate the proposed methods. Methods to check the validity of the proposed model are also demonstrated. This model avoids the difficulties encountered in alternative approaches which attempt to specify a dependent joint distribution with marginal proportional hazards and yields an estimate of the degree of dependence.