ArticleBiometrics2013
Two-stage hierarchical group testing for multiple infections with application to the infertility prevention project.
Article in Biometrics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
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
22 citing papers in PubMed.
- Factors affecting detection and quantification of Schistosoma haematobium eggs in pooled urine samples.PLoS neglected tropical diseases · 2026Article
- Assessing Simultaneous Infection with Multiple Pathogens via Group Testing with Imperfect Multiplex Assays.Journal of agricultural, biological, and environmental statistics · 2025Article
- Bayesian Additive Regression Trees for Group Testing Data.Statistics in medicine · 2025Article
- Article
- Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.Statistics in medicine · 2024Article
- Regression analysis of group-tested current status data.Biometrika · 2024Article
- Estimating the prevalence of two or more diseases using outcomes from multiplex group testing.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Bias Correction in Estimating Proportions by Imperfect Pooled Testing.Journal of agricultural, biological, and environmental statistics · 2021Article
- Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies.Frontiers in public health · 2021Article
- Nonparametric estimation of distributions and diagnostic accuracy based on group-tested results with differential misclassification.Biometrics · 2020Article
- From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data.Biometrics · 2020Article
- Array testing for multiplex assays.Biostatistics (Oxford, England) · 2020Article
- Regression analysis and variable selection for two-stage multiple-infection group testing data.Statistics in medicine · 2019Article
- Informative group testing for multiplex assays.Biometrics · 2019Article
- Revisiting Nested Group Testing Procedures: New Results, Comparisons, and Robustness.The American statistician · 2019Article
- Efficient methods for the estimation of the multinomial parameter for the two-trait group testing model.Electronic journal of statistics · 2019Article
- Group testing case identification with biomarker information.Computational statistics & data analysis · 2018Article
- Group testing regression models with dilution submodels.Statistics in medicine · 2017Article
- Hierarchical group testing for multiple infections.Biometrics · 2017Article
- Estimating the prevalence of multiple diseases from two-stage hierarchical pooling.Statistics in medicine · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Screening for sexually transmitted diseases (STDs) has benefited greatly from the use of group testing (pooled testing) to lower costs. With the development of assays that detect multiple infections, screening practices now involve testing pools of individuals for multiple infections simultaneously. Building on the research for single infection group testing procedures, we examine the performance of group testing for multiple infections. Our work is motivated by chlamydia and gonorrhea testing for the infertility prevention project (IPP), a national program in the United States. We consider a two-stage pooling algorithm currently used to perform testing for the IPP. We first derive the operating characteristics of this algorithm for classification purposes (e.g., expected number of tests, misclassification probabilities, etc.) and identify pool sizes that minimize the expected number of tests. We then develop an expectation-maximization (EM) algorithm to estimate probabilities of infection using both group and individual retest responses. Our research shows that group testing can offer large cost savings when classifying individuals for multiple infections and can provide prevalence estimates that are actually more efficient than those from individual testing.
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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.