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.
翻译:在观察研究中,偏差加权法在评估治疗效果方面很受欢迎,但极端偏好分数可能会偏向估计偏差,并造成过度差异。最近,提出了重叠加权法,以缓解这一问题,使问题平稳地向下调整,使问题具有极端偏差分。虽然在文献中以连续和二进制结果广泛展示了重叠加权的优点,但用时间对活动或生存结果来评估其表现的研究却很有限。在本篇文章中,我们提议了两个加权估计因素,这些估计因素结合了偏差得分的加权数和审查权重估计反事实生存功能的加权数的逆差概率。这些估计因素适用于平衡加权数的一般类别,其中包括偏差加权数、三进制和作为特例的重叠加权数。我们进行模拟,审查这些估计因素在偏差、差异和95%的置信度间间隔范围方面,在治疗组和检查率的不同程度下,我们表明,加权数始终超过时间,而偏差的加权和三进制处理结果之间,在偏差的幅度和递增幅度方面,我们进行了模拟研究这些估计。