Individualized empirical null estimation for exact tests of healthcare quality.

Individualized empirical null estimation for exact tests of healthcare quality.
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用于精确测试医疗质量的个性化经验零估计。

DOI:
10.1002/sim.10074
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发表时间:
2024
影响因子:
2
通讯作者:
He,Kevin
He,Kevin
中科院分区:
医学3区
文献类型:
--
作者:
Hartman,Nicholas;He,Kevin

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美国联邦机构对医疗保健提供者进行评估,以识别、标记并可能惩罚那些提供低于国家期望的低质量护理的医疗保健提供者。在实践中,评估指标不可避免地受到未观察到的混杂因素的影响,这降低了标记的准确性,并导致统计数据相对于理论零分布过度分散。针对这一问题,一些作者提出了个性化的经验空(IEN)的方法来估计一个适当的空分布为每个供应商的评估统计,同时考虑到供应商的有效规模。然而,现有的IEN方法要求统计量渐近地遵循正态分布,这在具有小供应商或错误指定的模型的应用中通常不成立。在这篇文章中,我们开发了一个用于精确假设检验的IEN框架,该框架考虑了未观察到的混杂因素的影响,而不做任何渐近假设。仿真结果表明,该方法具有更高的标记精度相比,传统的方法。我们应用这些方法来评估由医疗保险和医疗补助服务中心监测的透析设施和移植中心。
United States federal agencies evaluate healthcare providers to identify, flag, and potentially penalize those that deliver low‐quality care compared to national expectations. In practice, evaluation metrics are inevitably impacted by unobserved confounding factors, which reduce flagging accuracy and cause the statistics to be overdispersed relative to the theoretical null distributions. In response to this issue, several authors have proposed individualized empirical null (IEN) methods to estimate an appropriate null distribution for each provider's evaluation statistic while taking into account the provider's effective size. However, existing IEN methods require that the statistics asymptotically follow normal distributions, which often does not hold in applications with small providers or misspecified models. In this article, we develop an IEN framework for exact hypothesis tests that accounts for the impact of unobserved confounding without making any asymptotic assumptions. Simulations show that the proposed IEN method has greater flagging accuracy compared to conventional approaches. We apply these methods to evaluate dialysis facilities and transplant centers that are monitored by the Centers for Medicare and Medicaid Services.
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