In evidence synthesis, multilevel modeling approaches (MMAs) are commonly employed to combine aggregate data (AD) and individual participant data (IPD). These approaches rely on an aggregate outcome model that is ideally obtained by integrating the prespecified individual- level outcome model over the covariate distribution observed in each eligible study. In non- linear settings, such an integration may however be analytically intractable and requires ap- proximations. In this paper, we propose a novel method for incorporating AD into causal meta-analysis of IPD studies that can overcome this challenge. Rather than relying on an ag- gregate outcome model that is difficult to be correctly formulated, we propose modeling the trial membership as a function of baseline covariates. This model allows one to estimate the individual-level outcome model in each AD study by leveraging IPD available in other trials, and then to transport the treatment effects estimated from both AD and IPD trials to an external target population, even when only aggregate covariate data are available for that population. Unlike previous proposals, we do not require pseudo-IPD to be generated from the aggregate data, which helps minimize bias due to incomplete information on the covariate distribution in each AD trial and in the target population.

翻译：在证据合成中，多水平建模方法通常被用于整合汇总数据与个体参与者数据。这些方法依赖于一个理想的汇总结局模型，该模型通过对每个合格研究中观察到的协变量分布积分预先设定的个体水平结局模型而获得。然而在非线性场景中，此类积分可能在解析上难以处理，需要采用近似方法。本文提出了一种将汇总数据纳入个体参与者数据研究因果元分析的新方法，能够克服这一挑战。我们不再依赖难以正确构建的汇总结局模型，而是提出将试验成员资格建模为基线协变量的函数。该模型允许研究者利用其他试验中可用的个体参与者数据来估计每个汇总数据研究中的个体水平结局模型，进而将汇总数据与个体参与者数据试验中估计的治疗效应迁移至外部目标人群——即使该人群仅能获得汇总协变量数据。与先前方案不同，本方法无需从汇总数据生成伪个体参与者数据，这有助于最小化因各汇总数据试验及目标人群中协变量分布信息不完整而导致的偏倚。