Recalibrating single-study effect sizes using hierarchical Bayesian models.
case-control differences
effect size recalibration
hierarchical Bayesian model
inflated effect size
small sample size
substance dependence
Journal
Frontiers in neuroimaging
ISSN: 2813-1193
Titre abrégé: Front Neuroimaging
Pays: Switzerland
ID NLM: 9918402387106676
Informations de publication
Date de publication:
2023
2023
Historique:
received:
05
01
2023
accepted:
27
11
2023
medline:
5
1
2024
pubmed:
5
1
2024
entrez:
5
1
2024
Statut:
epublish
Résumé
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. 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 sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, Our 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.
Identifiants
pubmed: 38179200
doi: 10.3389/fnimg.2023.1138193
pmc: PMC10764546
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1138193Informations de copyright
Copyright © 2023 Cao, McCabe, Callas, Cupertino, Ottino-González, Murphy, Pancholi, Schwab, Catherine, Hutchison, Cousijn, Dagher, Foxe, Goudriaan, Hester, Li, Thompson, Morales, London, Lorenzetti, Luijten, Martin-Santos, Momenan, Paulus, Schmaal, Sinha, Solowij, Stein, Stein, Uhlmann, van Holst, Veltman, Wiers, Yücel, Zhang, Conrod, Mackey, Garavan and the ENIGMA Addiction Working Group.
Déclaration de conflit d'intérêts
RS has served on the scientific advisory board of Embera Neuro-therapeutics. DS has received research grants and/or consultancy honoraria from Lundbeck and Sun. MY has received funding from several law firms in relation to expert witness reports. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.