Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design. Lifetime Data Anal 2022 Apr;28(2):241-262
Date
01/17/2022Pubmed ID
35034255Pubmed Central ID
PMC8977245DOI
10.1007/s10985-022-09546-8Scopus ID
2-s2.0-85123103100 (requires institutional sign-in at Scopus site)Abstract
A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al. (Scand J Stat 43:103-122, 2016) studied the non-stratified proportional subdistribution hazards model with covariate-dependent censoring to directly evaluate covariate effects on the cumulative incidence function. In this paper, we propose a stratified proportional subdistribution hazards model with covariate-adjusted censoring weights for competing risks data under the generalized case-cohort design. We consider a general class of weight functions to account for the generalized case-cohort design. Then, we derive the optimal weight function which minimizes the asymptotic variance of parameter estimates within the general class of weight functions. The proposed estimator is shown to be consistent and asymptotically normally distributed. The simulation studies show (i) the proposed estimator with covariate-adjusted weight is unbiased when the censoring distribution depends on covariates; and (ii) the proposed estimator with the optimal weight function gains parameter estimation efficiency. We apply the proposed method to stem cell transplantation and diabetes data sets.
Author List
Xu Y, Kim S, Zhang MJ, Couper D, Ahn KWAuthors
Kwang Woo Ahn PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinSoyoung Kim PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Cohort StudiesComputer Simulation
Humans
Incidence
Proportional Hazards Models
Research Design