Premium
Stratified proportional subdistribution hazards model with covariate‐adjusted censoring weight for case‐cohort studies
Author(s) -
Kim Soyoung,
Xu Yayun,
Zhang MeiJie,
Ahn KwangWoo
Publication year - 2020
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12461
Subject(s) - covariate , censoring (clinical trials) , estimator , statistics , proportional hazards model , cohort , econometrics , mathematics , survival analysis
The case‐cohort study design is widely used to reduce cost when collecting expensive covariates in large cohort studies with survival or competing risks outcomes. A case‐cohort study dataset consists of two parts: (a) a random sample and (b) all cases or failures from a specific cause of interest. Clinicians often assess covariate effects on competing risks outcomes. The proportional subdistribution hazards model directly evaluates the effect of a covariate on the cumulative incidence function under the non‐covariate‐dependent censoring assumption for the full cohort study. However, the non‐covariate‐dependent censoring assumption is often violated in many biomedical studies. In this article, we propose a proportional subdistribution hazards model for case‐cohort studies with stratified data with covariate‐adjusted censoring weight. We further propose an efficient estimator when extra information from the other causes is available under case‐cohort studies. The proposed estimators are shown to be consistent and asymptotically normal. Simulation studies show (a) the proposed estimator is unbiased when the censoring distribution depends on covariates and (b) the proposed efficient estimator gains estimation efficiency when using extra information from the other causes. We analyze a bone marrow transplant dataset and a coronary heart disease dataset using the proposed method.