Group testing regression model estimation when case identification is a goal

DOI10.1002/bimj.201200168zbMath1441.62548OpenAlexW1595405559WikidataQ37344078 ScholiaQ37344078MaRDI QIDQ4917512

Boan Zhang, Christopher R. Bilder, Joshua M. Tebbs

Publication date: 30 April 2013

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3841014

zbMATH Keywords

EM algorithmgeneralized linear modelbinary responseprevalence estimationpooled testinglatent response

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

groupTesting: an R package for group testing estimation ⋮ Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments ⋮ Nonparametric estimation of distributions and diagnostic accuracy based on group‐tested results with differential misclassification ⋮ Regression models for group testing: identifiability and asymptotics ⋮ Optimizing pooled testing for estimating the prevalence of multiple diseases

Uses Software

• binGroup

Cites Work

• Unnamed Item
• Confidence intervals for the difference of two proportions estimated from pooled samples
• Three-Dimensional Array-Based Group Testing Algorithms
• Optimality of group testing in the presence of misclassification
• Group testing with a new goal, estimation
• Screening for the Presence of a Disease by Pooling Sera Samples
• Maximally Efficient Two‐Stage Screening
• Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples
• Bias and efficiency loss due to misclassified responses in binary regression
• Informative Retesting
• Bayesian Methods for Predicting Interacting Protein Pairs Using Domain Information

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