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Influence diagnostics and outlier detection for meta-analysis of diagnostic test accuracy.

Authors
  • Matsushima, Yuki1, 2
  • Noma, Hisashi3
  • Yamada, Tomohide4
  • Furukawa, Toshi A5
  • 1 Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, Tokyo, Japan. , (Japan)
  • 2 Department of Biometrics, Otsuka Pharmaceutical Co Ltd, Tokyo, Japan. , (Japan)
  • 3 Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan. , (Japan)
  • 4 Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. , (Japan)
  • 5 Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan. , (Japan)
Type
Published Article
Journal
Research synthesis methods
Publication Date
Mar 01, 2020
Volume
11
Issue
2
Pages
237–247
Identifiers
DOI: 10.1002/jrsm.1387
PMID: 31724796
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Meta-analyses of diagnostic test accuracy (DTA) studies have been gaining prominence in research in clinical epidemiology and health technology development. In these DTA meta-analyses, some studies may have markedly different characteristics from the others and potentially be inappropriate to include. The inclusion of these "outlying" studies might lead to biases, yielding misleading results. In addition, there might be influential studies that have notable impacts on the results. In this article, we propose Bayesian methods for detecting outlying studies and their influence diagnostics in DTA meta-analyses. Synthetic influence measures based on the bivariate hierarchical Bayesian random effects models are developed because the overall influences of individual studies should be simultaneously assessed by the two outcome variables and their correlation information. We propose four synthetic measures for influence analyses: (a) relative distance, (b) standardized residual, (c) Bayesian p-value, and (d) influence statistic on the area under the summary receiver operating characteristic curve. We also show that conventional univariate Bayesian influential measures can be applied to the bivariate random effects models, which can be used as marginal influential measures. Most of these methods can be similarly applied to the frequentist framework. We illustrate the effectiveness of the proposed methods by applying them to a DTA meta-analysis of ultrasound in screening for vesicoureteral reflux among children with urinary tract infections. © 2019 John Wiley & Sons, Ltd.

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