Observational data with clustered structure may have confounding at one or more levels which when combined critically undermine result validity. We propose using multilevel models in Bayesian propensity score analysis to account for cluster and individual level confounding in the estimation of both propensity score and in turn treatment effect. In addition, our approach includes confounders in the outcome model for more flexibility to model outcome-covariate surface, minimizing the influence of feedback effect in Bayesian joint modeling of propensity score model and outcome model. In an extensive simulation study, we compare several propensity score analysis approaches with varying complexity of multilevel modeling structures. With each of proposed propensity score model, random intercept outcome model augmented with covariates adjustment well maintains the property of propensity score as balancing score and outperforms single level outcome model. To illustrate the proposed models, a case study is considered, which investigates the impact of lipid screening on lipid management in youth from three different health care systems.

中文翻译：

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聚类观测数据的贝叶斯倾向得分分析

具有聚类结构的观测数据可能在一个或多个级别上存在混淆，当将其严重合并时会破坏结果的有效性。我们建议在贝叶斯倾向得分分析中使用多级模型，以在倾向得分和治疗效果的估计中考虑群集和个体水平的混淆。此外，我们的方法在结果模型中包括混杂因素，以提供更大的灵活性来对结果-协变量表面进行建模，从而最大程度地减少了倾向得分模型和结果模型的贝叶斯联合建模中反馈效应的影响。在广泛的仿真研究中，我们比较了具有不同复杂度的多层建模结构的几种倾向得分分析方法。对于每个提议的倾向得分模型，随机变量截获结果模型通过协变量调整得到增强，很好地保持了倾向得分作为平衡得分的性能，并且优于单级结果模型。为了说明所提出的模型，考虑了一个案例研究，该案例研究了脂质筛查对来自三个不同卫生保健系统的年轻人中脂质管理的影响。