ArticleBiometrical journal. Biometrische Zeitschrift2013
Group testing regression model estimation when case identification is a goal.
Article in Biometrical journal. Biometrische Zeitschrift, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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
15 citing papers in PubMed, 30 citations in OpenAlex.
- Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.Statistics in medicine · 2024Article
- Bayesian group testing regression models for spatial data.Spatial and spatio-temporal epidemiology · 2024Article
- Prevalence and risk factors for avian influenza virus (H5 and H9) contamination in peri-urban and rural live bird markets in Bangladesh.Frontiers in public health · 2023Article
- Generalized additive regression for group testing data.Biostatistics (Oxford, England) · 2021Article
- Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling.Ecology and evolution · 2021Article
- Semiparametric isotonic regression modelling and estimation for group testing data.The Canadian journal of statistics = Revue canadienne de statistique · 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
- Incorporating retesting outcomes for estimation of disease prevalence.Statistics in medicine · 2020Article
- Adaptive elastic net for group testing.Biometrics · 2019Article
- Group testing regression models with dilution submodels.Statistics in medicine · 2017Article
- Bayesian regression for group testing data.Biometrics · 2017Article
- A general framework for the regression analysis of pooled biomarker assessments.Statistics in medicine · 2017Article
- Estimating the prevalence of multiple diseases from two-stage hierarchical pooling.Statistics in medicine · 2016Article
- Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments.Biometrical journal. Biometrische Zeitschrift · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Group testing is frequently used to reduce the costs of screening a large number of individuals for infectious diseases or other binary characteristics in small prevalence situations. In many applications, the goals include both identifying individuals as positive or negative and estimating the probability of positivity. The identification aspect leads to additional tests being performed, known as "retests", beyond those performed for initial groups of individuals. In this paper, we investigate how regression models can be fit to estimate the probability of positivity while also incorporating the extra information from these retests. We present simulation evidence showing that significant gains in efficiency occur by incorporating retesting information, and we further examine which testing protocols are the most efficient to use. Our investigations also demonstrate that some group testing protocols can actually lead to more efficient estimates than individual testing when diagnostic tests are imperfect. The proposed methods are applied retrospectively to chlamydia screening data from the Infertility Prevention Project. We demonstrate that significant cost savings could occur through the use of particular group testing protocols.
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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.