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Individual participant\u2010level data meta\u2010analysis (IPD\u2010MA) offers a robust framework for such assessments by leveraging data from multiple studies. However, its performance is often compromised by real\u2010world challenges, including but not limited to high between\u2010study variability or small magnitude of moderation effect. Traditional Bayesian shrinkage methods have gained popularity in addressing these challenges, but are less suitable in MA, as their priors do not discern heterogeneous studies. In this paper, we propose calibrated mixtures of g\u2010priors in IPD\u2010MA to enhance efficiency and reduce risks in estimating moderation effects, providing a novel series of priors tailored for multiple studies by incorporating a study\u2010level calibration parameter and a moderator\u2010level shrinkage. This design offers a flexible range of shrinkage levels, allowing practitioners to evaluate moderator importance from conservative and optimistic perspectives. Compared with existing Bayesian shrinkage methods, our simulation studies demonstrate that calibrated mixtures of g\u2010priors exhibit equivalent or superior performances in estimating moderation effects. The benefits of the proposed methods are particularly pronounced in scenarios with high between\u2010study variability, high model sparsity, weak moderation effects, and correlated design matrices. We illustrate their application in assessing moderators of two treatments for major depressive disorder, using IPD from four randomized controlled trials.<\/jats:p>","DOI":"10.1002\/sim.70510","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:39:51Z","timestamp":1775115591000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Hierarchical Models With Calibrated Mixtures of g\u2010priors for Assessing Treatment Effect Moderation in Meta\u2010Analysis"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3570-9496","authenticated-orcid":false,"given":"Qiao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Public Health East Carolina University Brody School of Medicine  Greenville North Carolina 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