title: Shared frailty models for recurrent events and a terminal event.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA. liulei@umich.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.forename: Robert A
authors.initials: RA
authors.lastname: Wolfe
authors.forename: Xuelin
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001699:Biometry; D005260:Female; D006760:Hospitalization; D006801:Humans; D007674:Kidney Diseases / Q000401:mortality / Q000628:therapy; D016030:Kidney Transplantation; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016019:Survival Analysis