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用于检测异常值评估者的假设检验。

Hypothesis testing for detecting outlier evaluators.

作者信息

Xu Li, Zucker David M, Wang Molin

机构信息

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Department of Statistics and Data Science, Hebrew University of Jerusalem, Mt. Scopus, Jerusalem, Israel.

出版信息

Int J Biostat. 2024 Nov 4;20(2):419-431. doi: 10.1515/ijb-2023-0004. eCollection 2024 Nov 1.

Abstract

In epidemiological studies, the measurements of disease outcomes are carried out by different evaluators. In this paper, we propose a two-stage procedure for detecting outlier evaluators. In the first stage, a regression model is fitted to obtain the evaluators' effects. Outlier evaluators have different effects than normal evaluators. In the second stage, stepwise hypothesis tests are performed to detect outlier evaluators. The true positive rate and true negative rate of the proposed procedure are assessed in a simulation study. We apply the proposed method to detect potential outlier audiologists among the audiologists who measured hearing threshold levels of the participants in the Audiology Assessment Arm of the Conservation of Hearing Study, which is an epidemiological study for examining risk factors of hearing loss.

摘要

在流行病学研究中,疾病结局的测量由不同的评估者进行。在本文中,我们提出了一种用于检测异常值评估者的两阶段程序。在第一阶段,拟合回归模型以获得评估者的效应。异常值评估者与正常评估者具有不同的效应。在第二阶段,进行逐步假设检验以检测异常值评估者。在模拟研究中评估了所提出程序的真阳性率和真阴性率。我们将所提出的方法应用于在听力保护研究的听力评估组中测量参与者听力阈值水平的听力学家中检测潜在的异常值听力学家,该研究是一项用于检查听力损失危险因素的流行病学研究。

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本文引用的文献

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Analytical method for detecting outlier evaluators.用于检测离群评价者的分析方法。
BMC Med Res Methodol. 2023 Aug 1;23(1):177. doi: 10.1186/s12874-023-01988-4.
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Outlier detection for multi-network data.多网络数据的异常值检测。
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Origin, Methods, and Evolution of the Three Nurses' Health Studies.三项护士健康研究的起源、方法与演变
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