ArticleStatistics in medicine2020
Incorporating retesting outcomes for estimation of disease prevalence.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Multiple Summary Knowledge-Based Disease Prevalence Estimation for Group Testing Data.Statistics in medicine · 2026Article
- Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.Statistics in medicine · 2024Article
- Using group testing in a two-phase epidemiologic design to identify the effects of a large number of antibody reactions on disease risk.BMC medical research methodology · 2022Article
- Nested Group Testing Procedure.Communications in mathematics and statistics · 2022Article
- Heterogeneity Aware Two-Stage Group Testing.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Group testing has been widely used as a cost-effective strategy to screen for and estimate the prevalence of a rare disease. While it is well-recognized that retesting is necessary for identifying infected subjects, it is not required for estimating the prevalence. For a test without misclassification, gains in statistical efficiency are expected from incorporating retesting results in the estimation of the prevalence. However, when the test is subject to misclassification, it is not clear how much gain should be expected. There are a number of theoretical challenges in addressing this issue, including (1) enumerating the potential test results from retesting individual subjects in a group, (2) the dependence among these test results and the test result from testing at the group level, and (3) differential misclassification due to pooling of biospecimens. Overcoming some of these challenges, we show that retesting subjects in either positive or negative groups can substantially improve the efficiency of the estimation and that retesting positive groups yields higher efficiency than retesting a same number or proportion of negative groups.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.