Pirracchio R, Carone M, Rigon M Resche, Caruana E, Mebazaa A, Chevret S
Department of Biostatistics, INSERM UMR-S717; Hôpital Saint Louis, AP-HP; Université Paris Diderot, Sorbonne Paris Cité; Paris, France Department of Anesthesiology & Critical Care, Hôpital Européen Georges Pompidou, AP-HP; Université Paris Descartes, Sorbonne Paris Cité; Paris, France Division of Biostatistics, School of Public Health, University of California at Berkeley, Berkeley, USA
Division of Biostatistics, School of Public Health, University of California at Berkeley, Berkeley, USA.
Stat Methods Med Res. 2016 Oct;25(5):1938-1954. doi: 10.1177/0962280213507034. Epub 2013 Nov 6.
Propensity score matching is typically used to estimate the average treatment effect for the treated while inverse probability of treatment weighting aims at estimating the population average treatment effect. We illustrate how different estimands can result in very different conclusions.
We applied the two propensity score methods to assess the effect of continuous positive airway pressure on mortality in patients hospitalized for acute heart failure. We used Monte Carlo simulations to investigate the important differences in the two estimates.
Continuous positive airway pressure application increased hospital mortality overall, but no continuous positive airway pressure effect was found on the treated. Potential reasons were (1) violation of the positivity assumption; (2) treatment effect was not uniform across the distribution of the propensity score. From simulations, we concluded that positivity bias was of limited magnitude and did not explain the large differences in the point estimates. However, when treatment effect varies according to the propensity score (E[Y(1)-Y(0)|g(X)] is not constant, Y being the outcome and g(X) the propensity score), propensity score matching ATT estimate could strongly differ from the inverse probability of treatment weighting-average treatment effect estimate. We show that this empirical result is supported by theory.
Although both approaches are recommended as valid methods for causal inference, propensity score-matching for ATT and inverse probability of treatment weighting for average treatment effect yield substantially different estimates of treatment effect. The choice of the estimand should drive the choice of the method.
倾向得分匹配通常用于估计接受治疗者的平均治疗效果，而治疗权重的逆概率旨在估计总体平均治疗效果。我们说明了不同的估计量如何导致截然不同的结论。
我们应用两种倾向得分方法来评估持续气道正压通气对因急性心力衰竭住院患者死亡率的影响。我们使用蒙特卡罗模拟来研究两种估计方法的重要差异。
总体而言，应用持续气道正压通气会增加医院死亡率，但在接受治疗者中未发现持续气道正压通气的效果。潜在原因是：（1）违反了阳性假设；（2）治疗效果在倾向得分分布中不一致。通过模拟，我们得出结论，阳性偏倚的程度有限，无法解释点估计中的巨大差异。然而，当治疗效果根据倾向得分而变化时（E[Y(1)-Y(0)|g(X)]不是常数，Y为结局，g(X)为倾向得分），倾向得分匹配的接受治疗者平均治疗效果估计可能与治疗权重的逆概率-平均治疗效果估计有很大差异。我们表明这一实证结果得到了理论支持。
尽管两种方法都被推荐为因果推断的有效方法，但倾向得分匹配法用于估计接受治疗者平均治疗效果，而治疗权重的逆概率法用于估计平均治疗效果，二者得出的治疗效果估计值有很大差异。估计量的选择应驱动方法的选择。
