Bayesian Random-Effects Meta-Analysis Integrating Individual Participant Data and Aggregate Data
提出一种贝叶斯随机效应框架，整合个体参与者数据和汇总数据，克服数据可用性偏差，通过估计方程和自助法近似条件分布，减少均方误差和偏差，应用于妊娠期体重管理研究。
Meta-analysis using individual participant data (IPD) offers many benefits, including greater analytical flexibility, compared to conventional analyses based on aggregate data (AD). However, it is often hindered by restricted access to IPD. Relying solely on available IPD may introduce “data availability bias,” compromising external validity. Integrating IPD with relevant AD addresses this concern, but existing methods are restrictive, requiring precise knowledge of the IPD-to-AD parameter mapping or relying on fixed-effect models that fail to account for study-level heterogeneity. We propose a Bayesian random-effects framework to overcome these limitations. Building on existing methods, we use estimating equations to derive the conditional distributions of AD parameters, given the corresponding IPD model parameters. We then apply the multiplier bootstrap method and density ratio models to approximate these conditional distributions based on the observed data, without requiring homogeneity in the covariate distributions. Both theoretical and empirical results demonstrate that our method reduces mean squared error compared to IPD-only analysis when IPD availability is independent of the data, and reduces bias when data availability is dependent. We apply this integrated approach to complement the IPD-only analysis in the International Weight Management in Pregnancy (i-WIP) Collaborative Group study.