Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

DOI10.1080/01621459.2019.1671197zbMath1441.62471arXiv1708.05508OpenAlexW2980499623WikidataQ100433992 ScholiaQ100433992MaRDI QIDQ5120652

Jen Jen Yeh, Naim U. Rashid, Quefeng Li, Joseph G. Ibrahim

Publication date: 15 September 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1708.05508

zbMATH Keywords

predictiongeneralized linear mixed modelsRNA-seqmicroarraypenalized likelihood

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items

Defining replicability of prediction rules, Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing

Uses Software

• tspair
• Salmon
• fRMA

Cites Work

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• Bayesian sensitivity analysis of statistical models with missing data
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• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors