Evidence syntheses and meta-analyses are used to inform clinical practice guidelines and health economic evaluations. However, heterogeneity of treatment effects poses a significant challenge. Conventional meta-analysis addresses heterogeneity through random-effect assumptions, which are not supported by design and lead to estimates that may not apply to any real-world population. Causally-interpretable meta-analysis (CIMA) offers a rigorous framework for specification, identification, and estimation of causal effects when combining information from multiple randomized trials. Initial development of CIMA focused on using individual data from randomized trials, but such data are often unavailable in practice. Here, we propose a new version of CIMA that only requires aggregate data from trials, addressing the limitations of traditional meta-analysis methods while relying only on aggregate data. The method leverages the trials' reported estimates of marginal and one-at-a-time subgroup treatment effects and descriptive statistics for baseline covariates to build moment equations for identifying and estimating a parametric conditional average treatment effect (CATE) function. The average treatment effect in a new target population is obtained by marginalizing the CATE function over the individual covariate data that defines the target population. The method can also be used to obtain causally-interpretable indirect treatment comparisons in the target population. We establish the asymptotic properties of the method, assess its finite-sample performance in simulation studies, and illustrate the application of the method by re-analyzing a published meta-analysis for SGLT2 inhibitors in patients with heart failure.

翻译：证据综合和荟萃分析用于指导临床实践指南和卫生经济学评估。然而，治疗效果的异质性带来了重大挑战。传统荟萃分析通过随机效应假设来处理异质性，但这些假设缺乏设计依据，得到的估计值可能不适用于任何真实世界人群。因果可解释的荟萃分析（CIMA）为在合并多个随机试验信息时规范说明、识别和估计因果效应提供了严谨框架。CIMA的初步发展侧重于使用随机试验的个体数据，但在实践中此类数据往往难以获取。本文提出了仅需试验汇总数据的新版CIMA，在仅依赖汇总数据的同时解决了传统荟萃分析方法的局限性。该方法利用试验报告的边际效应和逐次单亚组治疗效果估计值，以及基线协变量的描述性统计量，构建矩方程以识别和估计参数化条件平均治疗效果（CATE）函数。通过将CATE函数对定义目标人群的个体协变量数据边缘化，即可获得新目标人群中的平均治疗效果。该方法还可用于获取目标人群中具有因果解释性的间接治疗比较。我们建立了该方法的渐近性质，通过模拟研究评估其有限样本性能，并通过重新分析一项关于心力衰竭患者使用SGLT2抑制剂的已发表荟萃分析来展示该方法的应用。