ArticleBiostatistics (Oxford, England)2013
Regression models for group testing data with pool dilution effects.
Article in Biostatistics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Bayesian Additive Regression Trees for Group Testing Data.Statistics in medicine · 2025Article
- Bayesian group testing regression models for spatial data.Spatial and spatio-temporal epidemiology · 2024Article
- Semi-quantitative group testing for efficient and accurate qPCR screening of pathogens with a wide range of loads.BMC bioinformatics · 2024Article
- Capturing the pool dilution effect in group testing regression: A Bayesian approach.Statistics in medicine · 2022Article
- Simulation of group testing scenarios can boost COVID-19 screening power.Scientific reports · 2022Article
- Optimal uses of pooled testing for COVID-19 incorporating imperfect test performance and pool dilution effect: An application to congregate settings in Los Angeles County.Journal of medical virology · 2021Article
- Modeling and computation of multistep batch testing for infectious diseases.Biometrical journal. Biometrische Zeitschrift · 2021Article
- Group testing can improve the cost-efficiency of prospective-retrospective biomarker studies.BMC medical research methodology · 2021Article
- Bias Correction in Estimating Proportions by Imperfect Pooled Testing.Journal of agricultural, biological, and environmental statistics · 2021Article
- Nonparametric estimation of distributions and diagnostic accuracy based on group-tested results with differential misclassification.Biometrics · 2020Article
- Incorporating retesting outcomes for estimation of disease prevalence.Statistics in medicine · 2020Article
- Adaptive elastic net for group testing.Biometrics · 2019Article
- Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents.Statistics in medicine · 2018Article
- Group testing case identification with biomarker information.Computational statistics & data analysis · 2018Article
- Group testing regression models with dilution submodels.Statistics in medicine · 2017Article
- Bayesian regression for group testing data.Biometrics · 2017Article
- Hierarchical group testing for multiple infections.Biometrics · 2017Article
- Misclassified group-tested current status data.Biometrika · 2016Article
- Estimating the prevalence of multiple diseases from two-stage hierarchical pooling.Statistics in medicine · 2016Article
- Pooling Bio-Specimens in the Presence of Measurement Error and Non-Linearity in Dose-Response: Simulation Study in the Context of a Birth Cohort Investigating Risk Factors for Autism Spectrum Disorders.International journal of environmental research and public health · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Group testing is widely used to reduce the cost of screening individuals for infectious diseases. There is an extensive literature on group testing, most of which traditionally has focused on estimating the probability of infection in a homogeneous population. More recently, this research area has shifted towards estimating individual-specific probabilities in a regression context. However, existing regression approaches have assumed that the sensitivity and specificity of pooled biospecimens are constant and do not depend on the pool sizes. For those applications, where this assumption may not be realistic, these existing approaches can lead to inaccurate inference, especially when pool sizes are large. Our new approach, which exploits the information readily available from underlying continuous biomarker distributions, provides reliable inference in settings where pooling would be most beneficial and does so even for larger pool sizes. We illustrate our methodology using hepatitis B data from a study involving Irish prisoners.
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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.