An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay

DOI10.1111/biom.12489zbMath1390.62292OpenAlexW2294174180WikidataQ38768886 ScholiaQ38768886MaRDI QIDQ2827220

Enrique F. Schisterman, Clarice R. Weinberg, Emily M. Mitchell, Robert H. Lyles, David M. Umbach

Publication date: 12 October 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5014596

zbMATH Keywords

bootstrappoolingefficiencylogistic regressionepidemiologystudy designdesign strategy

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Optimal statistical designs (62K05) Generalized linear models (logistic models) (62J12) Numerical analysis or methods applied to Markov chains (65C40)

Uses Software

• SAS
• SAS/STAT
• bootstrap

Cites Work

• Unnamed Item
• Logistic disease incidence models and case-control studies
• Analysis of Multistage Pooling Studies of Biological Specimens for Estimating Disease Incidence and Prevalence
• Using Pooled Exposure Assessment to Improve Efficiency in Case‐Control Studies
• Bias reduction of maximum likelihood estimates

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