A proportional hazards regression model for the subdistribution with right-censored and left-truncated competing risks data. Stat Med 2011 Jul 20;30(16):1933-51
Date
05/11/2011Pubmed ID
21557288Pubmed Central ID
PMC3408877DOI
10.1002/sim.4264Scopus ID
2-s2.0-79959212043 (requires institutional sign-in at Scopus site) 78 CitationsAbstract
With competing risks failure time data, one often needs to assess the covariate effects on the cumulative incidence probabilities. Fine and Gray proposed a proportional hazards regression model to directly model the subdistribution of a competing risk. They developed the estimating procedure for right-censored competing risks data, based on the inverse probability of censoring weighting. Right-censored and left-truncated competing risks data sometimes occur in biomedical researches. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with right-censored and left-truncated data. We adopt a new weighting technique to estimate the parameters in this model. We have derived the large sample properties of the proposed estimators. To illustrate the application of the new method, we analyze the failure time data for children with acute leukemia. In this example, the failure times for children who had bone marrow transplants were left truncated.
Author List
Zhang X, Zhang MJ, Fine JAuthor
Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
BiostatisticsBone Marrow Transplantation
Child
Data Interpretation, Statistical
Humans
Leukemia
Proportional Hazards Models
Risk
Time Factors
Treatment Failure