Recent work has made important contributions in the development of causally-interpretable meta-analysis. These methods transport treatment effects estimated in a collection of randomized trials to a target population of interest. Ideally, estimates targeted toward a specific population are more interpretable and relevant to policy-makers and clinicians. However, between-study heterogeneity not arising from differences in the distribution of treatment effect modifiers can raise difficulties in synthesizing estimates across trials. The existence of such heterogeneity, including variations in treatment modality, also complicates the interpretation of transported estimates as a generic effect in the target population. We propose a conceptual framework and estimation procedures that attempt to account for such heterogeneity, and develop inferential techniques that aim to capture the accompanying excess variability in causal estimates. This framework also seeks to clarify the kind of treatment effects that are amenable to the techniques of generalizability and transportability.
翻译:最近的工作对发展因果解释的元分析作出了重要贡献,这些方法将随机试验的收集所估计的处理效果运到感兴趣的目标人群中,理想的情况是,针对特定人群的估计数较易解释,而且与决策者和临床医生有关,然而,由于治疗效果改变者分布不同,在研究之间没有因治疗效果改变者的不同而产生的异质性可能会在综合各种试验的估计数方面造成困难。这种异质性,包括治疗方式的变异性,也使将运输估计数解释为对目标人群的一般影响更为复杂。我们提出了一个概念框架和估计程序,试图说明这种异质性,并发展旨在捕捉到伴随的因果估计的超常变性的推断技术。这个框架还力求澄清可适用通用性和可迁移性技术的治疗效果类型。