Department of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, North Carolina.
Department of Statistical Science, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina.
Am J Epidemiol. 2019 Jan 1;188(1):250-257. doi: 10.1093/aje/kwy201.
The popular inverse probability weighting method in causal inference is often hampered by extreme propensity scores, resulting in biased estimates and excessive variance. A common remedy is to trim patients with extreme scores (i.e., remove them from the weighted analysis). However, such methods are often sensitive to the choice of cutoff points and discard a large proportion of the sample. The implications for bias and the precision of the treatment effect estimate are unclear. These problems are mitigated by a newly developed method, the overlap weighting method. Overlap weights emphasize the target population with the most overlap in observed characteristics between treatments, by continuously down-weighting the units in the tails of the propensity score distribution. Here we use simulations to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance, and 95% confidence interval coverage. A range of propensity score distributions are considered, including settings with substantial nonoverlap and extreme values. To facilitate practical implementation, we further provide a consistent estimator for the standard error of the treatment effect estimated using overlap weighting.
在因果推断中，常用的反概率加权方法常常受到极端倾向得分的阻碍，导致有偏估计和方差过大。一种常见的补救方法是对极端得分的患者进行修剪（即从加权分析中删除他们）。然而，这种方法往往对截止点的选择很敏感，并且会丢弃很大一部分样本。对于处理效果估计的偏差和精度的影响尚不清楚。这些问题通过一种新开发的方法得到缓解，即重叠加权法。重叠权重通过在处理之间观察到的特征最重叠的目标人群中不断降低倾向得分分布尾部的单位权重，来强调目标人群。在这里，我们使用模拟来比较重叠权重与带有修剪的标准逆概率加权在偏差、方差和 95%置信区间覆盖方面的差异。考虑了一系列倾向得分分布，包括具有大量非重叠和极端值的情况。为了便于实际实施，我们进一步提供了使用重叠加权法估计处理效果的标准误差的一致估计量。
