1 Department of Biostatistics, University of Texas School of Public Health, Houston, TX, USA.
2 Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Stat Methods Med Res. 2019 Aug;28(8):2439-2454. doi: 10.1177/0962280218781171. Epub 2018 Jun 19.
Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.
逆概率加权（Inverse probability weighting，简称 IPS）可用于倾向评分分析中的平均处理效应估计。当比较组之间的倾向评分分布缺乏重叠时，一些权重可能过大，导致点估计和方差估计的数值不稳定和有偏差。我们研究了一类可用于避免此问题的修正逆概率加权估计量。这些权重会导致估计值偏离平均处理效应。我们从处理效应发现的角度为这种偏差提供了一些理由。我们表明，当缺乏重叠时，与逆概率加权和其他倾向评分方法相比，修正权重可以在统计功效方面取得实质性的提高。我们开发了分析方差估计量，适当地调整了估计倾向评分的抽样变异性，并通过为提高效率的结果模型来增强修正逆概率加权估计量，这一特性类似于双重稳健性。广泛的模拟和实际数据应用的结果支持了我们的结论。所提出的方法学在 R 包 PSW 中实现。
