A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data

Research output: Contribution to journalJournal articleResearchpeer-review

Peng He, Frank Eriksson, Thomas H. Scheike, Mei Jie Zhang

With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate-dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate-dependent censoring. We consider a covariate-adjusted weight function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate-adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate-adjusted weight approach works well for the variance estimator as well. We illustrate our methods with bone marrow transplant data from the Center for International Blood and Marrow Transplant Research. Here, cancer relapse and death in complete remission are two competing risks.

Original languageEnglish
JournalScandinavian Journal of Statistics
Volume43
Issue number1
Pages (from-to)103-122
Number of pages20
ISSN0303-6898
DOIs
Publication statusPublished - Mar 2016

    Research areas

  • Competing risks, Cumulative incidence function, Inverse probability of censoring weight, Proportional hazards model, Subdistribution

ID: 150981960