Causal effect estimation on restricted mean survival time under case-cohort design via propensity score stratification

Wei-En Lu and Ai Ni ()
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Wei-En Lu: College of Public Health,Ohio State University
Ai Ni: College of Public Health,Ohio State University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 4, No 8, 898-931

Abstract: Abstract In large observational studies with survival outcome and low event rates, the case-cohort design is commonly used to reduce the cost associated with covariate measurement. The restricted mean survival time (RMST) difference has been increasingly used as an alternative to hazard ratio when estimating the causal effect on survival outcomes. We investigate the estimation of marginal causal effect on RMST under the stratified case-cohort design while adjusting for measured confounders through propensity score stratification. The asymptotic normality of the estimator is established, and its variance formula is derived. Simulation studies are performed to evaluate the finite sample performance of the proposed method compared to several alternative methods. Finally, we apply the proposed method to the Atherosclerosis Risk in Communities study to estimate the marginal causal effect of high-sensitivity C-reactive protein level on coronary heart disease-free survival.

Keywords: ARIC study; Causal effect estimation; Propensity score stratification; Restricted mean survival time; Stratified case-cohort design; Survival analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10985-025-09667-w

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