论文标题
用于机构和医疗保健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
论文作者
论文摘要
特定于疾病的质量指标（QIS）用于用与特定疾病治疗相关的过程或结果来比较机构和医疗保健提供者。在手术癌症治疗的背景下，性能变化可能是由于医院和/或外科医生水平的差异引起的，从而产生了分层聚类。我们考虑如何将观察到的患者水平的护理变化分解为因果，医院的表现，医院内的外科医生表现，患者病例 - 混合和无法解释的（残留）变异。为此，我们得出了四向方差分解，特别注意组件的因果解释。为了进行估计，我们使用具有嵌套随机医院/外科医生特异性效应的混合效应模型的输入，以及用于医院/外科医生特异性患者人群的多项式逻辑模型。我们在仿真研究中研究了方法的性能。
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.
