Assessing treatment effect moderation is critical in biomedical research and many other fields, as it guides personalized intervention strategies to improve participant's outcomes. Individual participant-level data meta-analysis (IPD-MA) offers a robust framework for such assessments by leveraging data from multiple trials. However, its performance is often compromised by challenges such as high between-trial variability. Traditional Bayesian shrinkage methods have gained popularity, but are less suitable in this context, as their priors do not discern heterogeneous studies. In this paper, we propose the calibrated mixtures of g-priors methods in IPD-MA to enhance efficiency and reduce risk in the estimation of moderation effects. Our approach incorporates a trial-level sample size tuning function, and a moderator-level shrinkage parameter in the prior, offering a flexible spectrum of shrinkage levels that enables practitioners to evaluate moderator importance, from conservative to optimistic perspectives. Compared with existing Bayesian shrinkage methods, our extensive simulation studies demonstrate that the calibrated mixtures of g-priors exhibit superior performances in terms of efficiency and risk metrics, particularly under high between-trial variability, high model sparsity, weak moderation effects and correlated design matrices. We further illustrate their application in assessing effect moderators of two active treatments for major depressive disorder, using IPD from four randomized controlled trials.

翻译：评估治疗效应调节作用在生物医学研究及诸多其他领域中至关重要，因其能够指导个性化干预策略以改善参与者的结局。个体参与者水平数据荟萃分析为此类评估提供了稳健的框架，通过整合多项试验的数据进行分析。然而，其性能常因高试验间异质性等挑战而受到影响。传统的贝叶斯收缩方法虽应用广泛，但在该情境下适用性有限，因其先验分布无法有效区分异质性研究。本文提出在个体参与者水平数据荟萃分析中采用校准混合g先验方法，以提升调节效应估计的效能并降低风险。我们的方法在先验分布中引入了试验水平样本量调节函数与调节变量水平收缩参数，提供了一种灵活的收缩水平谱，使研究者能够从保守至乐观的不同视角评估调节变量的重要性。与现有贝叶斯收缩方法相比，大量模拟研究表明，校准混合g先验方法在效能与风险指标方面均表现出更优性能，尤其在高试验间异质性、高模型稀疏性、弱调节效应及相关设计矩阵条件下。我们进一步通过四项随机对照试验的个体参与者数据，展示了该方法在评估两种主要抑郁症活性治疗方案效应调节因素中的实际应用。