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 greater reproduci...

Whakaahuatanga katoa

Ngā taipitopito rārangi puna kōrero

Ngā kaituhi matua:	Christopher E. Gillies, Theodore S. Jennaro, Michael A. Puskarich, Ruchi Sharma, Kevin R. Ward, Xudong Fan, Alan E. Jones, Kathleen A. Stringer
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	MDPI AG 2020-08-01
Rangatū:	Metabolites
Ngā marau:	hierarchical modeling nuclear magnetic resonance spectroscopy Bayesian statistics missing values imputation multiple test corrections
Urunga tuihono:	https://www.mdpi.com/2218-1989/10/8/319
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!