Propensity score weighting is a common method for estimating treatment effects with survey data. The method is applied to minimize confounding using measured covariates that are often different between individuals in treatment and control. However, existing literature does not reach a consensus on the optimal use of survey weights for population-level inference in the propensity score weighting analysis. Under the balancing weights framework, we provided a unified solution for incorporating survey weights in both the propensity score of estimation and the outcome regression model. We derived estimators for different target populations, including the combined, treated, controlled, and overlap populations. We provide a unified expression of the sandwich variance estimator and demonstrate that the survey-weighted estimator is asymptotically normal, as established through the theory of M-estimators. Through an extensive series of simulation studies, we examined the performance of our derived estimators and compared the results to those of alternative methods. We further carried out two case studies to illustrate the application of the different methods of propensity score analysis with complex survey data. We concluded with a discussion of our findings and provided practical guidelines for propensity score weighting analysis of observational data from complex surveys.

翻译：倾向得分加权是利用调查数据估计处理效应的常用方法。该方法通过使用处理组和对照组个体间通常存在差异的已测量协变量，以最小化混杂效应。然而，现有文献对于倾向得分加权分析中如何最优使用调查权重进行总体水平推断尚未达成共识。在平衡权重框架下，我们提出了在倾向得分估计和结局回归模型中纳入调查权重的统一解决方案。我们推导了针对不同目标总体（包括合并总体、处理组总体、对照组总体和重叠总体）的估计量。我们给出了三明治方差估计量的统一表达式，并通过M估计量理论证明了调查加权估计量具有渐近正态性。通过一系列广泛的模拟研究，我们检验了所推导估计量的性能，并将结果与其他方法进行了比较。我们进一步开展了两个案例研究，以说明不同倾向得分分析方法在复杂调查数据中的应用。最后，我们讨论了研究结果，并为复杂调查观察性数据的倾向得分加权分析提供了实用指南。