Pulse lineResearch With Heart Logo

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design.

TitleCompeting risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design.
Publication TypeJournal Article
Year of Publication2022
AuthorsXu Y, Kim S, Zhang M-J, Couper D, Ahn KWoo
JournalLifetime Data Anal
Volume28
Issue2
Pagination241-262
Date Published2022 04
ISSN1572-9249
KeywordsCohort Studies, Computer Simulation, Humans, Incidence, Proportional Hazards Models, Research Design
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.

DOI10.1007/s10985-022-09546-8
Alternate JournalLifetime Data Anal
PubMed ID35034255
PubMed Central IDPMC8977245
Grant ListU24 CA076518 / CA / NCI NIH HHS / United States
HHSN268201700002C / HL / NHLBI NIH HHS / United States
HHSN268201700001I / HL / NHLBI NIH HHS / United States
HHSN268201700004I / HL / NHLBI NIH HHS / United States
HHSN268201700004C / HL / NHLBI NIH HHS / United States
R01 DK056918 / DK / NIDDK NIH HHS / United States
HHSN268201700003I / HL / NHLBI NIH HHS / United States
HHSN268201700005C / HL / NHLBI NIH HHS / United States
HHSN268201700001C / HL / NHLBI NIH HHS / United States
HHSN268201700003C / HL / NHLBI NIH HHS / United States
HHSN268201700002I / HL / NHLBI NIH HHS / United States
HHSN268201700005I / HL / NHLBI NIH HHS / United States