  The inverse probability weighting approach is popular for evaluating
treatment effects in observational studies, but extreme propensity scores could
bias the estimator and induce excessive variance. Recently, the overlap
weighting approach has been proposed to alleviate this problem, which smoothly
down-weighs the subjects with extreme propensity scores. Although advantages of
overlap weighting have been extensively demonstrated in literature with
continuous and binary outcomes, research on its performance with time-to-event
or survival outcomes is limited. In this article, we propose two weighting
estimators that combine propensity score weighting and inverse probability of
censoring weighting to estimate the counterfactual survival functions. These
estimators are applicable to the general class of balancing weights, which
includes inverse probability weighting, trimming, and overlap weighting as
special cases. We conduct simulations to examine the empirical performance of
these estimators with different weighting schemes in terms of bias, variance,
and 95% confidence interval coverage, under various degree of covariate overlap
between treatment groups and censoring rate. We demonstrate that overlap
weighting consistently outperforms inverse probability weighting and associated
trimming methods in bias, variance, and coverage for time-to-event outcomes,
and the advantages increase as the degree of covariate overlap between the
treatment groups decreases.
