A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty

A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the <i>p</i>-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to improved study design and great...

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Bibliographic Details
Main Authors: Christopher E. Gillies, Theodore S. Jennaro, Michael A. Puskarich, Ruchi Sharma, Kevin R. Ward, Xudong Fan, Alan E. Jones, Kathleen A. Stringer
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Metabolites
Subjects:
hierarchical modeling
nuclear magnetic resonance spectroscopy
Bayesian statistics
missing values
imputation
multiple test corrections
Online Access:https://www.mdpi.com/2218-1989/10/8/319

Internet

https://www.mdpi.com/2218-1989/10/8/319

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