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An Efficient Estimation Method for Additive Subdistribution Hazards Model With Left-Truncated Competing Risks Data Under the Case-Cohort Study Design. Stat Med 2025 Jul;44(15-17):e70183

Date

07/15/2025

Pubmed ID

40662850

Pubmed Central ID

PMC12620030

DOI

10.1002/sim.70183

Scopus ID

2-s2.0-105010958231 (requires institutional sign-in at Scopus site)

Abstract

The case-cohort study design provides a cost-effective approach for large cohort studies with competing risks outcomes. The additive subdistribution hazards model assesses direct covariate effects on cumulative incidence when investigating risk differences among different groups instead of relative risk. The presence of left truncation, which commonly occurs in biomedical studies, introduces additional complexities to the analysis. Existing inverse-probability-weighting methods for case-cohort studies on competing risks are inefficient in parameter estimation of coefficients for baseline covariates. In addition, their methods do not address left truncation. To improve the efficiency of parameter estimation of coefficients for baseline covariates and account for left-truncated competing risks data, we propose an augmented-inverse-probability-weighted estimating equation for left-truncated competing risks data with additive subdistribution models under the case-cohort study design. For multiple case-cohort studies, we further improve parameter estimation efficiency by incorporating extra information from the other causes. We study large sample properties of the proposed estimators. Simulation studies demonstrate the unbiasedness of our proposed estimator and the superior efficiency in regression parameter estimation. We apply the proposed methods to analyze data from the Atherosclerosis Risk in Communities study.

Author List

Fang X, Woo Ahn K, Cai J, Kim S

Authors

Xi 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




MESH terms used to index this publication - Major topics in bold

Atherosclerosis
Case-Control Studies
Cohort Studies
Computer Simulation
Humans
Models, Statistical
Proportional Hazards Models
Research Design
Risk Assessment