Department of Biostatistics and Data Science, University of Texas School of Public Health, Houston, TX, USA.
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Stat Med. 2020 Jul 10;39(15):2017-2034. doi: 10.1002/sim.8526. Epub 2020 Mar 17.
In propensity score analysis, the frequently used regression adjustment involves regressing the outcome on the estimated propensity score and treatment indicator. This approach can be highly efficient when model assumptions are valid, but can lead to biased results when the assumptions are violated. We extend the simple regression adjustment to a varying coefficient regression model that allows for nonlinear association between outcome and propensity score. We discuss its connection with some propensity score matching and weighting methods, and show that the proposed analytical framework can shed light on the intrinsic connection among some mainstream propensity score approaches (stratification, regression, kernel matching, and inverse probability weighting) and handle commonly used causal estimands. We derive analytic point and variance estimators that properly take into account the sampling variability in the estimated propensity score. Extensive simulations show that the proposed approach possesses desired finite sample properties and demonstrates competitive performance in comparison with other methods estimating the same causal estimand. The proposed methodology is illustrated with a study on right heart catheterization.
在倾向得分分析中，常用的回归调整方法涉及将结局变量回归到估计的倾向得分和处理指示变量上。当模型假设成立时，这种方法可以非常高效，但当假设被违反时，可能会导致有偏的结果。我们将简单的回归调整扩展到一个变系数回归模型，该模型允许结局变量和倾向得分之间存在非线性关联。我们讨论了它与一些倾向得分匹配和加权方法的联系，并表明所提出的分析框架可以揭示一些主流倾向得分方法（分层、回归、核匹配和逆概率加权）之间的内在联系，并处理常用的因果估计量。我们推导出了适当考虑估计的倾向得分抽样变异性的分析点和方差估计量。广泛的模拟表明，该方法在有限样本中具有理想的性质，并在估计相同因果估计量方面与其他方法相比具有竞争性能。该方法通过右心导管检查研究进行了说明。
