Rott Kollin W, Clark Justin M, Murad M Hassan, Hodges James S, Huling Jared
Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, United States.
Evidence-Based Practice Center, Mayo Clinic, Rochester, MN, United States.
Am J Epidemiol. 2024 Sep 20. doi: 10.1093/aje/kwae371.
Recent work in causally-interpretable meta-analysis (CIMA) has bridged the gap between traditional meta-analysis and causal inference. While traditional meta-analysis results generally do not apply to any well-defined population, CIMA approaches specify a target population to which meta-analytic treatment effect estimates are transported. While theoretically attractive, these approaches currently have some practical limitations. Most assume that all studies in the meta-analysis have individual participant data (IPD), which is rare in practice because most trials share only aggregate data. We propose a method to perform CIMA using a combination of aggregate data and IPD. This method borrows information from studies with IPD to augment the aggregate data and create aggregate-matched synthetic IPD (AMSIPD), which can be used readily in the existing CIMA framework. By allowing use of both aggregate data and IPD, the method opens CIMA to more applications and can avoid biases arising from using only studies with IPD. We present a case study and simulations showing the AMSIPD approach is promising and merits further investigation as an advancement of CIMA.
因果可解释元分析（CIMA）领域的最新研究填补了传统元分析与因果推断之间的空白。虽然传统元分析的结果通常不适用于任何明确界定的人群，但CIMA方法指定了一个目标人群，元分析治疗效果估计值将被应用于该人群。虽然从理论上讲很有吸引力，但这些方法目前存在一些实际限制。大多数方法假定元分析中的所有研究都有个体参与者数据（IPD），但在实际中这很少见，因为大多数试验只共享汇总数据。我们提出了一种结合汇总数据和IPD来进行CIMA的方法。该方法从有IPD的研究中借用信息来扩充汇总数据，并创建汇总匹配的合成IPD（AMSIPD），它可以很容易地在现有的CIMA框架中使用。通过允许同时使用汇总数据和IPD，该方法使CIMA能够应用于更多领域，并可以避免仅使用有IPD的研究而产生的偏差。我们展示了一个案例研究和模拟，表明AMSIPD方法很有前景，作为CIMA的一项进展值得进一步研究。
