Recalibrating single-study effect sizes using hierarchical Bayesian models

Recalibrating single-study effect sizes using hierarchical Bayesian models

IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect s...

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Bibliographic Details
Main Authors: Zhipeng Cao, Matthew McCabe, Peter Callas, Renata B. Cupertino, Jonatan Ottino-González, Alistair Murphy, Devarshi Pancholi, Nathan Schwab, Orr Catherine, Kent Hutchison, Janna Cousijn, Alain Dagher, John J. Foxe, Anna E. Goudriaan, Robert Hester, Chiang-Shan R. Li, Wesley K. Thompson, Angelica M. Morales, Edythe D. London, Valentina Lorenzetti, Maartje Luijten, Rocio Martin-Santos, Reza Momenan, Martin P. Paulus, Lianne Schmaal, Rajita Sinha, Nadia Solowij, Dan J. Stein, Elliot A. Stein, Anne Uhlmann, Ruth J. van Holst, Dick J. Veltman, Reinout W. Wiers, Murat Yücel, Sheng Zhang, Patricia Conrod, Scott Mackey, Hugh Garavan, The ENIGMA Addiction Working Group
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-12-01
Series:Frontiers in Neuroimaging
Subjects:
effect size recalibration
hierarchical Bayesian model
case-control differences
substance dependence
small sample size
inflated effect size
Online Access:https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full

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https://www.frontiersin.org/articles/10.3389/fnimg.2023.1138193/full

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