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移植中心评估的综合评分:一种新的个体化经验性零假设方法。

COMPOSITE SCORES FOR TRANSPLANT CENTER EVALUATION: A NEW INDIVIDUALIZED EMPIRICAL NULL METHOD.

作者信息

Hartman Nicholas, Messana Joseph M, Kang Jian, Naik Abhijit S, Shearon Tempie H, He Kevin

机构信息

Department of Biostatistics, University of Michigan, Ann Arbor.

Division of Nephrology, University of Michigan, Ann Arbor.

出版信息

Ann Appl Stat. 2024 Mar;18(1):729-748. doi: 10.1214/23-aoas1809. Epub 2024 Jan 31.

Abstract

Risk-adjusted quality measures are used to evaluate healthcare providers with respect to national norms while controlling for factors beyond their control. Existing healthcare provider profiling approaches typically assume that the between-provider variation in these measures is entirely due to meaningful differences in quality of care. However, in practice, much of the between-provider variation will be due to trivial fluctuations in healthcare quality, or unobservable confounding risk factors. If these additional sources of variation are not accounted for, conventional methods will disproportionately identify larger providers as outliers, even though their departures from the national norms may not be "extreme" or clinically meaningful. Motivated by efforts to evaluate the quality of care provided by transplant centers, we develop a composite evaluation score based on a novel individualized empirical null method, which robustly accounts for overdispersion due to unobserved risk factors, models the marginal variance of standardized scores as a function of the effective sample size, and only requires the use of publicly-available center-level statistics. The evaluations of United States kidney transplant centers based on the proposed composite score are substantially different from those based on conventional methods. Simulations show that the proposed empirical null approach more accurately classifies centers in terms of quality of care, compared to existing methods.

摘要

风险调整后的质量指标用于对照国家规范评估医疗服务提供者,同时控制其无法控制的因素。现有的医疗服务提供者概况分析方法通常假定,这些指标在提供者之间的差异完全是由于护理质量的显著差异所致。然而,在实际中,提供者之间的许多差异将归因于医疗质量的微小波动或不可观察的混杂风险因素。如果不考虑这些额外的差异来源,传统方法将不成比例地将规模较大的提供者识别为异常值,即使它们与国家规范的偏离可能并非“极端”或具有临床意义。受评估移植中心所提供护理质量工作的推动,我们基于一种新颖的个体化经验性零假设方法开发了一个综合评估分数,该方法有力地考虑了因未观察到的风险因素导致的过度离散,将标准化分数的边际方差建模为有效样本量的函数,并且只需要使用公开可用的中心层面统计数据。基于提议的综合分数对美国肾脏移植中心进行的评估与基于传统方法的评估有很大不同。模拟表明,与现有方法相比,提议的经验性零假设方法在护理质量方面能更准确地对中心进行分类。

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