ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2015
Optimal retesting configurations for hierarchical group testing.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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
20 citing papers in PubMed.
- Reagent efficiency and analytical sensitivity optimization for a reliable SARS-CoV-2 pool-based testing strategy.Heliyon · 2025Article
- ExploringFrontiers in public health · 2025Article
- Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.Statistics in medicine · 2024Article
- binGroup2: Statistical Tools for Infection Identification via Group Testing.The R journal · 2023Article
- Optimal group testing with heterogeneous risks.Economic theory · 2023Article
- Leveraging network structure to improve pooled testing efficiency.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- Simulation of group testing scenarios can boost COVID-19 screening power.Scientific reports · 2022Article
- Smart pooling: AI-powered COVID-19 informative group testing.Scientific reports · 2022Article
- Group testing via hypergraph factorization applied to COVID-19.Nature communications · 2022Article
- Network-Informed Constrained Divisive Pooled Testing Assignments.Frontiers in big data · 2022Article
- Safe and effective pool testing for SARS-CoV-2 detection.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2021Article
- Nested pool testing strategy for the diagnosis of infectious diseases.Scientific reports · 2021Article
- Discussion on "Is group testing ready for prime-time in disease identification".Statistics in medicine · 2021Article
- Pooling for SARS-CoV2 Surveillance: Validation and Strategy for Implementation in K-12 Schools.Frontiers in public health · 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
- Sample pooling is a viable strategy for SARS-CoV-2 detection in low-prevalence settings.Pathology · 2020Article
- Multi-Stage Group Testing Improves Efficiency of Large-Scale COVID-19 Screening.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2020Article
- The objective function controversy for group testing: Much ado about nothing?Statistics in medicine · 2019Article
- Informative group testing for multiplex assays.Biometrics · 2019Article
- Bayesian regression for group testing data.Biometrics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Hierarchical group testing is widely used to test individuals for diseases. This testing procedure works by first amalgamating individual specimens into groups for testing. Groups testing negatively have their members declared negative. Groups testing positively are subsequently divided into smaller subgroups and are then retested to search for positive individuals. In our paper, we propose a new class of informative retesting procedures for hierarchical group testing that acknowledges heterogeneity among individuals. These procedures identify the optimal number of groups and their sizes at each testing stage in order to minimize the expected number of tests. We apply our proposals in two settings: 1) HIV testing programs that currently use three-stage hierarchical testing and 2) chlamydia and gonorrhea screening practices that currently use individual testing. For both applications, we show that substantial savings can be realized by our new procedures.
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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.