Causal Effect Estimation on Restricted Mean Survival Time in Case-Cohort Studies via a Matching Design

TL;DR

This paper introduces a novel causal effect estimation method for restricted mean survival time in case-cohort studies using a flexible template matching design, improving efficiency and applicability.

Contribution

The paper develops a new marginal causal effect estimator for RMST difference under stratified case-cohort design with template matching, including asymptotic theory and bootstrap variance estimation.

Findings

Template matching offers greater flexibility than conventional matching.

Simulation studies show the proposed estimators perform well in finite samples.

Application to ARIC Study illustrates practical utility in epidemiological research.

Abstract

In large observational studies, the case-cohort design is commonly used to reduce the cost associated with covariate measurement. For survival outcomes, literature has suggested that the restricted mean survival time (RMST) be a more appropriate marginal causal effect measure than the hazard ratio. In this paper, we develop a marginal causal effect estimation method for RMST difference under the stratified case-cohort design. We adjust for measured confounders using an innovative template matching design. Compared with conventional matching designs, template matching allows greater flexibility in the sample sizes of the exposed and unexposed groups. We establish the asymptotic properties of the proposed causal effect estimators and develop a bootstrap procedure to estimate their variances. By conducting comprehensive simulation studies, we evaluate the finite sample performance of the proposed estimators, demonstrate the advantage of template matching over conventional matching, and compare between matching on propensity score and matching on covariates. Finally, we apply the proposed methods to the Atherosclerosis Risk in Communities (ARIC) Study to estimate the marginal causal effect of serum hs-CRP level on the coronary heart disease-free survival.