Choosing an Appropriate Outlier-Detection Method When Comparing Institutional Performances in Clinical Audits.
- 1Department of Statistical Science, UCL, London, UK.
- 2NHS Arden and Greater East Midlands Commissioning Support Unit, Derby, UK.
Abstract
Clinical audits are essential for monitoring and improving the quality of healthcare with statistical outlier detection used to identify healthcare units whose performance deviates from expected standards. Two commonly used outlier-detection approaches are Common Mean Model (CMM) and Random Effects Logistic Regression (RELR). However, these methods can identify different healthcare units as outliers when applied to the same dataset. Incorrectly flagging a unit as an outlier can have serious consequences, including reputational harm, decreased staff morale, unnecessary regulatory scrutiny, diversion of resources, and loss of public trust. This article investigates the reasons for these discrepancies through a simulation study evaluating the performance of the CMM and RELR under a range of scenarios. The results show that differences in outlier detection are primarily driven by the degree to which a normality assumption holds for each method. When these assumptions are violated, for one or both methods, the two approaches may produce markedly different outlier detection. To choose the most appropriate approach for a given dataset, we recommend assessing each normality assumption, by combining visual diagnostics, such as QQ plots, with quantitative measures, such as Lin's concordance correlation coefficient (CCC).
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BibTeX
@article{sui2026choosing,
title = {{Choosing an Appropriate Outlier-Detection Method When Comparing Institutional Performances in Clinical Audits.}},
author = {Anqi Sui and Gareth Ambler and Rumana Z Omar and Mark de Belder and Menelaos Pavlou},
journal = {Biometrical journal. Biometrische Zeitschrift},
year = {2026},
month = oct,
doi = {10.1002/bimj.70196},
url = {https://europepmc.org/article/MED/42908499},
note = {CC BY},
}
RIS
TY - JOUR
TI - Choosing an Appropriate Outlier-Detection Method When Comparing Institutional Performances in Clinical Audits.
AU - Anqi Sui
AU - Gareth Ambler
AU - Rumana Z Omar
AU - Mark de Belder
AU - Menelaos Pavlou
PY - 2026
DA - 2026/10/30
JO - Biometrical journal. Biometrische Zeitschrift
DO - 10.1002/bimj.70196
UR - https://europepmc.org/article/MED/42908499
AB - Clinical audits are essential for monitoring and improving the quality of healthcare with statistical outlier detection used to identify healthcare units whose performance deviates from expected standards. Two commonly used outlier-detection approaches are Common Mean Model (CMM) and Random Effects Logistic Regression (RELR). However, these methods can identify different healthcare units as outliers when applied to the same dataset. Incorrectly flagging a unit as an outlier can have serious consequences, including reputational harm, decreased staff morale, unnecessary regulatory scrutiny, diversion of resources, and loss of public trust. This article investigates the reasons for these discrepancies through a simulation study evaluating the performance of the CMM and RELR under a range of scenarios. The results show that differences in outlier detection are primarily driven by the degree to which a normality assumption holds for each method. When these assumptions are violated, for one or both methods, the two approaches may produce markedly different outlier detection. To choose the most appropriate approach for a given dataset, we recommend assessing each normality assumption, by combining visual diagnostics, such as QQ plots, with quantitative measures, such as Lin's concordance correlation coefficient (CCC).
SN - 0323-3847
SN - 1521-4036
ER -