Hypothesis testing for detecting outlier evaluators

Xu Li, Zucker David M. and Wang Molin ()
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Xu Li: Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Zucker David M.: Department of Statistics and Data Science, Hebrew University of Jerusalem, Mt. Scopus, Jerusalem, Israel
Wang Molin: Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

The International Journal of Biostatistics, 2024, vol. 20, issue 2, 419-431

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.

Keywords: outlier detection; evaluator outliers; audiometric data; quality control (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/ijb-2023-0004

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