ArticleStatistics in medicine2019
The objective function controversy for group testing: Much ado about nothing?
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Optimal pooling strategies for respiratory virus testing: A comparative cost-effectiveness analysis.PLOS global public health · 2026Article
- binGroup2: Statistical Tools for Infection Identification via Group Testing.The R journal · 2023Article
- Statistical modeling and evaluation of the impact of multiplicity classification thresholds on the COVID-19 pool testing accuracy.PloS one · 2023Article
- Simulation of group testing scenarios can boost COVID-19 screening power.Scientific reports · 2022Article
- Pooled testing of traced contacts under superspreading dynamics.PLoS computational biology · 2022Article
- Discussion on "Is group testing ready for prime-time in disease identification".Statistics in medicine · 2021Article
- Is group testing ready for prime-time in disease identification?Statistics in medicine · 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
- Cost Effectiveness of Sample Pooling to Test for SARS-CoV-2.Journal of infection in developing countries · 2020Article
- Assessment of Specimen Pooling to Conserve SARS CoV-2 Testing Resources.American journal of clinical pathology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Group testing is an indispensable tool for laboratories when testing high volumes of clinical specimens for infectious diseases. An important decision that needs to be made prior to implementation is determining what group sizes to use. In best practice, an objective function is chosen and then minimized to determine an optimal set of these group sizes, known as the optimal testing configuration (OTC). There are a few options for objective functions, and they differ based on how the expected number of tests, assay characteristics, and testing constraints are taken into account. These varied options have led to a recent controversy in the literature regarding which of two different objective functions is better. In our paper, we examine these objective functions over a number of realistic situations for infectious disease testing. We show that this controversy may be much ado about nothing because the OTCs and corresponding results (eg, number of tests and accuracy) are largely the same for standard testing algorithms in a wide variety of situations.
Indexed as
Identifiers
What OpenQuestion holds
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.