ArticleJournal of statistical computation and simulation2009
Bias, efficiency, and agreement for group-testing regression models.
Article in Journal of statistical computation and simulation, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- Regression analysis of group-tested current status data.Biometrika · 2024Article
- Optimizing and evaluating PCR-based pooled screening during COVID-19 pandemics.Scientific reports · 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
- Local polynomial regression for pooled response data.Journal of nonparametric statistics · 2020Article
- Determination of Varying Group Sizes for Pooling Procedure.Computational and mathematical methods in medicine · 2019Article
- Group testing regression models with dilution submodels.Statistics in medicine · 2017Article
- Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments.Biometrical journal. Biometrische Zeitschrift · 2016Article
- Regression analysis for multiple-disease group testing data.Statistics in medicine · 2013Article
- Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.Emerging themes in epidemiology · 2013Article
- Informative Retesting.Journal of the American Statistical Association · 2010Article
- Group testing regression models with fixed and random effects.Biometrics · 2009Article
- Global goodness-of-fit tests for group testing regression models.Statistics in medicine · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Group testing involves pooling individual items together and testing them simultaneously for a rare binary trait. Whether the goal is to estimate the prevalence of the trait or to identify those individuals that possess it, group testing can provide substantial benefits when compared to testing subjects individually. Recently, group-testing regression models have been proposed as a way to incorporate covariates when estimating trait prevalence. In this paper, we examine these models by comparing fits obtained from individual and group testing samples. Relative bias and efficiency measures are used to assess the accuracy and precision of the resulting estimates using different grouping strategies. We also investigate the agreement of individual and group-testing regression estimates for various grouping strategies and the effects of group size selection. Depending on how groups are formed, our results show that group-testing regression models can perform very well when compared to the analogous models based on individual observations. However, different grouping strategies can provide very different results in finite samples.
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.