Recalibrating single-study effect sizes using hierarchical Bayesian models

Z Cao; M McCabe; P Callas; RB Cupertino; J Ottino-González; A Murphy; D Pancholi; N Schwab; O Catherine; K Hutchison; J Cousijn; A Dagher; JJ Foxe; AE Goudriaan; R Hester; CSR Li; WK Thompson; AM Morales; ED London; V Lorenzetti; M Luijten; R Martin-Santos; R Momenan; MP Paulus; L Schmaal; R Sinha; N Solowij; DJ Stein; EA Stein; A Uhlmann; RJ van Holst; DJ Veltman; RW Wiers; M Yücel; S Zhang; P Conrod; S Mackey; H Garavan

Journal article

Recalibrating single-study effect sizes using hierarchical Bayesian models

Z Cao, M McCabe, P Callas, RB Cupertino, J Ottino-González, A Murphy, D Pancholi, N Schwab, O Catherine, K Hutchison, J Cousijn, A Dagher, JJ Foxe, AE Goudriaan, R Hester, CSR Li, WK Thompson, AM Morales, ED London, V Lorenzetti Show all

Frontiers in Neuroimaging | Frontiers Media SA | Published : 2023

DOI: 10.3389/fnimg.2023.1138193

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Abstract

Introduction: There 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 sizes while incorporating a tailored estimation of sampling variance. Methods: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect size..

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