YSPH Biostatistics Seminar: “Improving Estimation Efficiency for Left-truncated Competing Risks Regression under the Case-cohort Design”

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Soyoung Kim, PhD, Associate Professor, Department of Biostatistics, Medical College of Wisconsin

TITLE: “Improving Estimation Efficiency for Left-truncated Competing Risks Regression under the Case-cohort Design"

ABSTRACT: The case-cohort study design provides a cost-effective study design for a large cohort study with competing risks outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risks data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risks data not only have not addressed left truncation, but also are inefficient in parameter estimation for baseline covariates. We propose an augmented inverse probability weighted estimating equation for left-truncated competing risks data to address these limitations of the current literature. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to parameter estimation efficiency gain for the baseline covariates. We analyze the data for coronary heart diseases using the proposed methods.