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ESTIMATING PREVALENCE FROM THE RESULTS OF A SCREENING TEST
Abstract
This paper deals with some basis properties of screening tests. Such tests purport to separate people with disease from people without. Minimal criteria for such a process to be a test are discussed. Various ways of judging the goodness of a test are examined. A common use of tests is to estimate prevalence of disease; frequency of positive tests is shown to be a bad estimate, and the necessary adjustmants are given.
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“Similarity is scored by an LLM judge (GPT-5.4;92.7% binary accuracy, sensitivity 87.1%, specificity 96.8%) and rates are Rogan-Gladen-corrected [Rogan and Gladen, 1978] within stratum, computed per paper, and reported with 95% cluster-bootstrap CIs (10,000 paper-level resamples).”
From On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists (2026) · cites this paper as Rogan and Gladen, 1978
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Walter, S. D. (1988) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(88)90110-2 · Cited by 372
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Hardy, G. E., Shapiro, D. A., Haynes, C. E., & Rick, J. (1999) · Psychological Assessment · doi:10.1037/1040-3590.11.2.159 · Cited by 193
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Metadata
- Authors
- ROGAN, W. J., & GLADEN, B.
- Affiliations
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- ROGAN, WALTER J.
- ORCID
- ROGAN, WALTER J.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 1978-01
- Volume
- 107
- Issue
- 1
- Pages
- 71-76
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2271596670
- Cited by
- 1,001
- References
- 0
- Citations by year
- 2026: 73 · 2025: 45 · 2024: 53 · 2023: 52 · 2022: 72 · 2021: 95 · 2020: 59 · 2019: 43 · 2018: 50 · 2017: 33 · 2016: 27 · 2015: 33 · 2014: 30 · 2013: 28 · 2012: 35
- FWCI
- 4.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- screening test, disease prevalence estimation, diagnostic accuracy, positive predictive value, false positive rate
- MeSH terms
- Epidemiologic Methods, Female, Humans, Mass Screening, Morbidity, Population Surveillance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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