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Investigating the causal relationship between exposure and the time-to-eventoutcome is an important topic in biomedical research. Previous literature hasdiscussed the potential issues of using the hazard ratio as a marginal causaleffect measure due to its noncollapsibility property. In this paper, weadvocate using the restricted mean survival time (RMST) difference as themarginal causal effect measure, which is collapsible and has a simpleinterpretation as the difference of area under survival curves over a certaintime horizon. To address both measured and unmeasured confounding, a matcheddesign with sensitivity analysis is proposed. Matching is used to pair similartreated and untreated subjects together, which is more robust to outcome modelmisspecification. Our propensity score matched RMST difference estimator isshown to be asymptotically unbiased and the corresponding variance estimator iscalculated by accounting for the correlation due to matching. The simulationstudy also demonstrates that our method has adequate empirical performance andoutperforms many competing methods used in practice. To assess the impact ofunmeasured confounding, we develop a sensitivity analysis strategy by adaptingthe E-value approach to matched data. We apply the proposed method to theAtherosclerosis Risk in Communities Study (ARIC) to examine the causal effectof smoking on stroke-free survival.

Matched Design for Marginal Causal Effect on Restricted Mean Survival Time in Observational Studies