Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: A Monte Carlo study
Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: A Monte Carlo study
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DOI：
10.1002/sim.2618
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发表时间：
2007-02-20
影响因子：
2
通讯作者：
Anderson, Geoffrey M.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.;Grootendorst, Paul;Anderson, Geoffrey M.
在医学文献中，倾向评分法越来越多地被用于估计因果治疗效果。对倾向得分的调节导致对观察到的两种治疗反应的预期差异的无偏估计。在观察性研究中，条件优势比或条件风险比经常被用作治疗效果的衡量标准，但对倾向得分的调节在多大程度上引入了对条件优势比或条件风险比的偏倚，尚未得到广泛的研究。我们进行了蒙特卡罗模拟，以确定倾向得分匹配的程度，倾向得分的五分位数分层，以及使用倾向得分进行协变量调整，导致条件优势比、风险比和比率比的偏倚估计。我们发现，对倾向得分的调节导致对真实条件优势比和真实条件风险比的有偏估计。在所有测试的场景中，治疗效果偏向于零治疗效果。然而，对倾向得分的调节并没有导致对真实条件比率的有偏估计。相比之下，传统的回归方法允许在回归模型中包含与结果相关的所有变量时，对真实条件处理效果进行无偏估计。倾向评分方法中观察到的偏差是由于回归模型允许人们估计条件处理效果，而倾向评分方法允许人们估计边际处理效果。在一些具有非线性处理效果的设置中，边际处理效果和条件处理效果不一致。版权所有(c) 2006约翰威利父子有限公司
Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide. Copyright (c) 2006 John Wiley & Sons, Ltd.