An Efficient Estimation Method for Additive Subdistribution Hazards Model With Left-Truncated Competing Risks Data...

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.