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Constructing inverse probability weights for institutional comparisons in healthcare.
Statistics in Medicine ( IF 1.8 ) Pub Date : 2020-06-24 , DOI: 10.1002/sim.8657
Thai-Son Tang 1 , Peter C Austin 2, 3 , Keith A Lawson 4 , Antonio Finelli 4 , Olli Saarela 1
Affiliation  

In comparing quality of care between hospitals, disease‐specific quality indicators measure structural, process, or outcome elements related to the care of a particular condition. Such comparisons can be framed in terms of causal contrasts, answering the question of whether a patient (or a population of patients on average) would receive different care if treated at the care level of a different hospital. Fair comparisons have to be adjusted for patient case‐mix, which is equivalent to controlling for confounding by the patient‐level factors, including demographic factors, comorbidities, and disease progression. The methodological choice for such comparisons is usually between direct and indirect standardization methods. In this article, we discuss the alternative of inverse probability weighting as a tool for standardization in hospital comparisons. This involves fitting multinomial logistic hospital assignment models and using these to construct the inverse probability weights. The challenge in the present context is the presence of large number of hospitals being compared, many of which have a small patient volume. We propose methods to include small categories in the weighted analysis, as well as metrics and visualizations for checking the positivity/overlap and covariate balance in constructing such weights. The methods are illustrated in a running example using linked administrative data on surgical treatment of kidney cancer patients in Ontario.

中文翻译:

构建用于医疗机构比较的逆概率权重。

在比较医院之间的护理质量时,特定疾病的质量指标可衡量与特定疾病护理相关的结构,过程或结果要素。可以根据因果对比来构筑这种比较,回答如果以不同医院的护理水平进行治疗的患者(或平均患者群体)是否会得到不同的护理的问题。必须针对患者的病例组合调整公平比较,这等效于控制由患者水平因素(包括人口统计学因素,合并症和疾病进展)引起的混淆。这种比较的方法选择通常在直接和间接标准化方法之间。在这篇文章中,我们讨论了逆概率加权作为医院比较中标准化工具的替代方法。这涉及拟合多项式物流医院分配模型,并使用它们来构建逆概率权重。在当前情况下的挑战是存在大量正在比较的医院,其中许多医院的病人量很小。我们提出了在加权分析中包括小类别的方法,以及用于在构建此类权重时检查阳性/重叠和协变量平衡的指标和可视化方法。在一个运行示例中使用链接的行政数据对安大略省肾癌患者的手术治疗进行了说明。这涉及拟合多项式物流医院分配模型,并使用它们来构建逆概率权重。在当前情况下的挑战是存在大量正在比较的医院,其中许多医院的病人量很小。我们提出了在加权分析中包括小类别的方法,以及用于在构建此类权重时检查阳性/重叠和协变量平衡的指标和可视化方法。在一个运行示例中使用链接的行政数据对安大略省肾癌患者的手术治疗进行了说明。这涉及拟合多项式物流医院分配模型,并使用它们来构建逆概率权重。当前的挑战是要比较的医院数量众多,其中许多医院的病人数量很少。我们提出了在加权分析中包括小类别的方法,以及用于在构建此类权重时检查阳性/重叠和协变量平衡的指标和可视化方法。在一个运行示例中使用链接的行政数据对安大略省肾癌患者的手术治疗进行了说明。以及用于在构建此类权重时检查阳性/重叠和协变量平衡的指标和可视化。在一个运行示例中使用链接的行政数据对安大略省肾癌患者的手术治疗进行了说明。以及用于在构建此类权重时检查阳性/重叠和协变量平衡的指标和可视化。在一个运行示例中使用链接的行政数据对安大略省肾癌患者的手术治疗进行了说明。
更新日期:2020-06-24
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