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首页> 外文期刊>Epidemiologic Perspectives and Innovations >Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients
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Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients

机译：心力衰竭患者β受体阻滞剂治疗的贝叶斯倾向得分分析中的协变量平衡

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摘要

Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. We study the balancing properties of the estimated propensity scores. Traditional Frequentist propensity scores focus attention on balancing covariates that are strongly associated with treatment. In contrast, we demonstrate that Bayesian propensity scores can be used to balance the association between covariates and the outcome. This balancing property has the effect of reducing confounding bias because it reduces the degree to which covariates are outcome risk factors.
机译：倾向得分的回归调整是一种统计方法，可以减少观测数据中测量变量的混淆。贝叶斯倾向得分分析通过同时估计倾向得分和治疗效果来扩展了这一思想。在本文中，我们在对β受体阻滞剂治疗心力衰竭患者的有效性进行观察研究的背景下，对贝叶斯倾向得分的表现进行了实证研究。我们研究了倾向得分的平衡性质。传统的频繁性倾向评分将注意力集中在与治疗密切相关的协变量上。相反，我们证明了贝叶斯倾向得分可以用来平衡协变量和结果之间的关联。这种平衡特性具有减少混杂偏差的效果，因为它降低了协变量是结果风险因素的程度。

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