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Get Free AccessOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.
Zhipeng Cao, Matthew F. McCabe, Peter Callas, Renata B. Cupertino, Jonatan Ottino‐González, Alistair Murphy, Devarshi Pancholi, Nathan Schwab, Orr Catherine, Kent E. 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 Martín‐Santos, Reza Momenan, Martin P. Paulus, Lianne Schmaal, Rajita Sinha, Nadia Solowij, Dan Joseph Stein, Elliot A. Stein, Anne Uhlmann, Ruth J. van Holst, Dick J. Veltman, Reínout W. Wiers, Murat Yücel, Sheng Zhang, Patricia Conrod, Scott Mackey, Hugh Garavan (2023). Recalibrating single-study effect sizes using hierarchical Bayesian models. , 2, DOI: https://doi.org/10.3389/fnimg.2023.1138193.
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Type
Article
Year
2023
Authors
38
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.3389/fnimg.2023.1138193
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