Propensity Score Methods and Unobserved Covariate Imbalance: Comments on "Squeezing the Balloon"
Propensity Score Methods and Unobserved Covariate Imbalance: Comments on "Squeezing the Balloon"
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DOI：
10.1111/1475-6773.12152
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发表时间：
2014-06-01
影响因子：
3.4
通讯作者：
Klungel, Olaf H.
中科院分区：
文献类型：
--
作者：
Ali, M. Sanni;Groenwold, Rolf H. H.;Klungel, Olaf H.
在最近的卫生服务研究文章《挤压气球：倾向分数和未测量的协变量平衡》中，Brooks和OhsFeldt(2013)讨论了倾向分数(PS)相对于不可测量的协变量的平衡性质的一个重要主题。他们的结论是，PS方法在治疗和未治疗的受试者之间平衡测量的协变量，加剧了与测量的协变量无关的未测量协变量的失衡。此外，他们强调，对于PS算法，治疗对象和未治疗对象之间未测量协变量的不平衡是在两组之间实现测量协变量平衡的必要条件。我们认为这些结论是他们对治疗分配机制假设的结果。此外，我们还讨论了PS方法的基本假设，它们与多元回归方法相比的优势，以及PS方法的效果估计的解释。
In their recent Health Services Research article titled Squeezing the Balloon: Propensity Scores and Unmeasured Covariate Balance, Brooks and Ohsfeldt (2013) addressed an important topic on the balancing property of the propensity score (PS) with respect to unmeasured covariates. They concluded that PS methods that balance measured covariates between treated and untreated subjects exacerbate imbalance in unmeasured covariates that are unrelated to measured covariates. Furthermore, they emphasized that for PS algorithms, an imbalance on unmeasured covariates between treatment and untreated subjects is a necessary condition to achieve balance on measured covariates between the groups. We argue that these conclusions are the results of their assumptions on the mechanism of treatment allocation. In addition, we discuss the underlying assumptions of PS methods, their advantages compared with multivariate regression methods, as well as the interpretation of the effect estimates from PS methods.