Research, ETS, Princeton, New Jersey.
RAND, Arlingon, VA.
Stat Med. 2024 May 20;43(11):2183-2202. doi: 10.1002/sim.10039. Epub 2024 Mar 26.
Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example.
先前在因果推断方面的工作表明，在倾向评分估计阶段和二项处理的结果模型阶段使用调查抽样权重，可以更稳健地估计正在分析的二项处理的效果。然而，迄今为止，尚未探讨将这项工作扩展到连续处理和暴露的情况，也没有考虑如何在倾向评分模型中处理流失权重。尽管如此，当研究人员拥有观察数据时，广义倾向评分（GPS）分析仍被用于估计连续治疗对结果的影响，并且这些数据集通常具有需要在分析中考虑的调查或流失权重。在这里，我们扩展了先前的工作，并通过分析结果表明，在连续治疗的 GPS 估计阶段和结果模型阶段使用调查抽样或流失权重可以产生比不使用权重更稳健的估计器。模拟研究结果表明，尽管在两个估计阶段都使用权重足以进行稳健估计，但在各种使用权重的估计方法下，在某些情况下，不使用权重也可以进行无偏估计。分析人员不知道我们的模拟研究的条件是否成立，因此在两个估计阶段使用权重可能为减少潜在偏差提供了保险。我们在实证示例的背景下讨论了我们结果的含义。
