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Biometrics. 2011 Jun;67(2):661-70. doi: 10.1111/j.1541-0420.2010.01493.x. Epub 2010 Dec 14.

Competing risks regression for stratified data.

Author information

1
Division of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut 06520, USA. bingqing.zhou@yale.edu

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.

PMID:
21155744
PMCID:
PMC3431205
DOI:
10.1111/j.1541-0420.2010.01493.x
[Indexed for MEDLINE]
Free PMC Article

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