Assessment of diagnostic tests when disease verification is subject to selection bias
Abstract
In the assessment of the statistical properties of a diagnostic test, for example the sensitivity and specificity of the test, it is common to derive estimates from a sample limited to those cases for whom subsequent definitive disease verification is obtained. Omission of nonverified cases can seriously bias the estimates. In order to adjust the estimates it is necessary to make assumptions about the mechanism for selecting cases for verification. Methods for making the necessary adjustments can then be derived.
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We did not encounter any situations in which a subset of participants received the reference standard but others did not, so there was no need to make corrections for verification bias.
“We did not encounter any situations in which a subset of participants in a study received the reference standard but others did not. Hence, there was no need to make corrections for verification bias in the statistical analysis (Begg 1983).”
From Xpert MTB/RIF Ultra and Xpert MTB/RIF assays for extrapulmonary tuberculosis and rifampicin resistance in adults (2021) · cites this paper as Begg 1983
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- Authors
- Begg, C. B., & Greenes, R. A.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1983-03
- Volume
- 39
- Issue
- 1
- Pages
- 207
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W1982890131
- Cited by
- 708
- References
- 5
- Citations by year
- 2026: 39 · 2025: 14 · 2024: 10 · 2023: 9 · 2022: 24 · 2021: 10 · 2020: 11 · 2019: 10 · 2018: 24 · 2017: 16 · 2016: 20 · 2015: 25 · 2014: 22 · 2013: 36 · 2012: 20
- FWCI
- 5.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. National Library of Medicine
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- diagnostic test evaluation, sensitivity and specificity, selection bias, case selection, bias correction, verification bias
- MeSH terms
- Biometry, Diagnosis, Humans, Models, Biological, Probability, Research Design
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