医疗保健机构比较的因果方差分解
Causal variance decompositions for institutional comparisons in healthcare
摘要：
人们越来越感兴趣的是比较机构提供医疗服务的疾病特异性质量指标（QIs），捕捉过程或结果显示所提供的护理的变化。这种比较可以用因果模型来构建，其中调整患者病例组合类似于控制混杂因素，例如，在给定的医院中治疗暴露。我们的目标是帮助识别良好的QI，而不是根据已经选择的QI来比较医院，因此我们关注医院之间护理的整体差异的存在和大小，而不是任何两家医院之间的成对差异。我们考虑如何观察到的变化，在病人层面上收到的护理可以被分解成因果关系解释的医院绩效调整的情况下组合，组合本身，和剩余的变化。为此，我们推导出一个三向方差分解，特别注意其因果解释的潜在结果变量。我们提出了基于模型的估计的分解，适应不同的链接功能，无论是固定或随机效应模型。我们在模拟研究中评估它们的性能，并在实际数据应用中演示它们的使用。
英文摘要：
There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variations in the care provided. Such comparisons can be framed in terms of causal models, where adjusting for patient case-mix is analogous to controlling for confounding, and exposure is being treated in a given hospital, for instance. Our goal here is to help identifying good QIs rather than comparing hospitals in terms of an already chosen QI, and so we focus on the presence and magnitude of overall variation in care between the hospitals rather than the pairwise differences between any two hospitals. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance adjusting for the case-mix, the case-mix itself, and residual variation. For this purpose, we derive a three-way variance decomposition, with particular attention to its causal interpretation in terms of potential outcome variables. We propose model-based estimators for the decomposition, accommodating different link functions and either fixed or random effect models. We evaluate their performance in a simulation study and demonstrate their use in a real data application.
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