Competing risks regression for clustered data with covariate-dependent censoring. Commun Stat Theory Methods 2025;54(4):1081-1099
Date
01/10/2025Pubmed ID
39790905Pubmed Central ID
PMC11708822DOI
10.1080/03610926.2024.2329771Scopus ID
2-s2.0-85189627495 (requires institutional sign-in at Scopus site) 1 CitationAbstract
Competing risks data in clinical trial or observational studies often suffer from cluster effects such as center effects and matched pairs design. The proportional subdistribution hazards (PSH) model is one of the most widely used methods for competing risks data analyses. However, the current literature on the PSH model for clustered competing risks data is limited to covariate-independent censoring and the unstratified model. In practice, competing risks data often face covariate-dependent censoring and have the non-PSH structure. Thus, we propose a marginal stratified PSH model with covariate-adjusted censoring weight for clustered competing risks data. We use a marginal stratified proportional hazards model to estimate the survival probability of censoring by taking clusters and non-proportional hazards structure into account. Our simulation results show that, in the presence of covariate-dependent censoring, the parameter estimates of the proposed method are unbiased with approximate 95% coverage rates. We apply the proposed method to stem cell transplant data of leukemia patients to evaluate the clinical implications of donor-recipient HLA matching on chronic graft-versus-host disease.
Author List
Khanal M, Kim S, Fang X, Ahn KWAuthors
Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinXi Fang Assistant Professor in the Data Science Institute department at Medical College of Wisconsin
Soyoung Kim PhD, BS, MS Associate Professor in the Data Science Institute department at Medical College of Wisconsin
