title: Competing risks regression for stratified data.
abstract: For competing risks data, the Fine-Gray proportional hazards model for subdistribution has gained popularity for its convenience in directly assessing the effect of covariates on the cumulative incidence function. However, in many important applications, proportional hazards may not be satisfied, including multicenter clinical trials, where the baseline subdistribution hazards may not be common due to varying patient populations. In this article, we consider a stratified competing risks regression, to allow the baseline hazard to vary across levels of the stratification covariate. According to the relative size of the number of strata and strata sizes, two stratification regimes are considered. Using partial likelihood and weighting techniques, we obtain consistent estimators of regression parameters. The corresponding asymptotic properties and resulting inferences are provided for the two regimes separately. Data from a breast cancer clinical trial and from a bone marrow transplantation registry illustrate the potential utility of the stratified Fine-Gray model.
authors.affiliation: Division of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut 06520, USA. bingqing.zhou@yale.edu
authors.forename: Bingqing
authors.initials: B
authors.lastname: Zhou
authors.forename: Aurelien
authors.initials: A
authors.lastname: Latouche
authors.forename: Vanderson
authors.initials: V
authors.lastname: Rocha
authors.forename: Jason
authors.initials: J
authors.lastname: Fine
mesh_terms: D001699:Biometry / Q000379:methods*; D016026:Bone Marrow Transplantation; D001943:Breast Neoplasms; D002986:Clinical Trials as Topic; D006801:Humans; D016016:Proportional Hazards Models; D012306:Risk; D012307:Risk Factors; D016008:Statistical Distributions