ArticleStatistics in medicine2016
Estimating the prevalence of multiple diseases from two-stage hierarchical pooling.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Sample pooling approaches simulated under resource scarcity, lapses in testing capacity, and rapid processing demands for surveillance testing: a data-driven performance comparison.BMC infectious diseases · 2026Article
- Article
- Youden index estimation based on group-tested data.Statistical methods in medical research · 2025Article
- Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.Statistics in medicine · 2024Article
- A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design.Statistics in medicine · 2024Article
- Estimating the prevalence of two or more diseases using outcomes from multiplex group testing.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Efficient and effective single-step screening of individual samples for SARS-CoV-2 RNA using multi-dimensional pooling and Bayesian inference.Journal of the Royal Society, Interface · 2021Article
- Considerations for Group Testing: A Practical Approach for the Clinical Laboratory.The Clinical biochemist. Reviews · 2020Review
- Regression analysis and variable selection for two-stage multiple-infection group testing data.Statistics in medicine · 2019Article
- Efficient methods for the estimation of the multinomial parameter for the two-trait group testing model.Electronic journal of statistics · 2019Article
- Revisiting Nested Group Testing Procedures: New Results, Comparisons, and Robustness.The American statistician · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Testing protocols in large-scale sexually transmitted disease screening applications often involve pooling biospecimens (e.g., blood, urine, and swabs) to lower costs and to increase the number of individuals who can be tested. With the recent development of assays that detect multiple diseases, it is now common to test biospecimen pools for multiple infections simultaneously. Recent work has developed an expectation-maximization algorithm to estimate the prevalence of two infections using a two-stage, Dorfman-type testing algorithm motivated by current screening practices for chlamydia and gonorrhea in the USA. In this article, we have the same goal but instead take a more flexible Bayesian approach. Doing so allows us to incorporate information about assay uncertainty during the testing process, which involves testing both pools and individuals, and also to update information as individuals are tested. Overall, our approach provides reliable inference for disease probabilities and accurately estimates assay sensitivity and specificity even when little or no information is provided in the prior distributions. We illustrate the performance of our estimation methods using simulation and by applying them to chlamydia and gonorrhea data collected in Nebraska. Copyright © 2016 John Wiley & Sons, Ltd.
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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.