医疗保健机构和提供者比较的分层因果方差分解
Hierarchical causal variance decomposition for institution and provider comparisons in healthcare
摘要：
疾病特异性质量指标（QIs）用于比较机构和医疗保健提供者在与特定疾病治疗相关的过程或结果方面的差异。在外科癌症治疗的背景下，性能变化可能是由于医院和/或外科医生水平的差异，从而产生分层聚类。我们考虑如何观察到的变化，在病人接受的护理水平可以分解成因果关系解释的医院的性能，医院内的外科医生的性能，病人的病例组合，和无法解释的（残留）的变化。为此，我们推导出一个四向方差分解，特别注意的因果解释的组件。为了进行估计，我们使用了嵌套随机医院/外科医生特定效应的混合效应模型的输入，以及医院/外科医生特定患者人群的多项logistic模型。我们调查我们的方法在模拟研究的性能。
英文摘要：
Disease-specific quality indicators (QIs) are used to compare institutions and health care providers in terms processes or outcomes relevant to treatment of a particular condition. In the context of surgical cancer treatments, the performance variations can be due to hospital and/or surgeon level differences, creating a hierarchical clustering. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance, surgeon performance within hospital, patient case-mix, and unexplained (residual) variation. For this purpose, we derive a four-way variance decomposition, with particular attention to the causal interpretation of the components. For estimation, we use inputs from a mixed-effect model with nested random hospital/surgeon-specific effects, and a multinomial logistic model for the hospital/surgeon-specific patient populations. We investigate the performance of our methods in a simulation study.
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