  While the inverse probability of treatment weighting (IPTW) is a commonly
used approach for treatment comparisons in observational data, the resulting
estimates may be subject to bias and excessively large variance when there is
lack of overlap in the propensity score distributions. By smoothly
down-weighting the units with extreme propensity scores, overlap weighting (OW)
can help mitigate the bias and variance issues associated with IPTW. Although
theoretical and simulation results have supported the use of OW with continuous
and binary outcomes, its performance with right-censored survival outcomes
remains to be further investigated, especially when the target estimand is
defined based on the restricted mean survival time (RMST)-a clinically
meaningful summary measure free of the proportional hazards assumption. In this
article, we combine propensity score weighting and inverse probability of
censoring weighting to estimate the restricted mean counterfactual survival
times, and propose computationally-efficient variance estimators. We conduct
simulations to compare the performance of IPTW, trimming, and OW in terms of
bias, variance, and 95% confidence interval coverage, under various degrees of
covariate overlap. Regardless of overlap, we demonstrate the advantage of OW
over IPTW and trimming methods in bias, variance, and coverage when the
estimand is defined based on RMST.
