Causally-interpretable meta-analysis using aggregate data
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AI 根据所提供的论文材料归类：摘要明确围绕条件平均治疗效应、目标人群效应与跨随机试验的因果识别展开。
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利用汇总数据开展具有因果解释的元分析
- 研究问题
- 无法获取各随机试验的个体数据时，能否利用试验汇总信息估计一个明确目标人群的治疗效应及间接治疗比较？
- 方法与思路
- 作者用试验边际效应、逐项亚组效应与基线描述统计构造矩条件，估计参数化 CATE，再对目标人群个体协变量平均。
- 材料陈述的贡献
- 摘要提出仅需试验汇总数据的因果元分析版本，建立渐近性质，并通过模拟和心衰患者 SGLT2 抑制剂案例展示应用。
- 阅读关注点
- 应核对汇总矩能否识别 CATE 参数、跨试验向目标人群迁移的条件，以及目标个体协变量数据和间接比较的要求。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未列出识别条件，且目标人群仍需个体协变量数据。
原文摘要
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.