Competing Risks Regression for Stratified Data
Article first published online: 14 DEC 2010
© 2010, The International Biometric Society
Volume 67, Issue 2, pages 661–670, June 2011
How to Cite
Zhou, B., Latouche, A., Rocha, V. and Fine, J. (2011), Competing Risks Regression for Stratified Data. Biometrics, 67: 661–670. doi: 10.1111/j.1541-0420.2010.01493.x
- Issue published online: 20 JUN 2011
- Article first published online: 14 DEC 2010
- Received March 2009. Revised June 2010. Accepted June 2010.
- Dependent censoring;
- Hazard of subdistribution;
- Inverse weighting;
- Multicenter trials;
- Partial likelihood
Summary 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.