There is increasing interest in combining information from experimental studies, including randomized and single-group trials, with information from external experimental or observational data sources. Such efforts are usually motivated by the desire to compare treatments evaluated in different studies -- for instance, by constructing external comparator groups for some index study -- or to estimate treatment effects with greater precision. Proposals to combine experimental studies with external data were made at least as early as the 1970s, but in recent years have come under increasing consideration within clinical practice and by regulatory agencies involved in drug and device evaluation, particularly with the increasing availability of trial and observational data. In this paper, we describe basic study templates that combine information from experimental studies with external data, and use the potential (counterfactual) outcomes framework to elaborate identification strategies for potential outcome means and average treatment effects. We argue that these identification strategies inherit ideas relevant to the study of causation in single-source studies and the related literature on combining information (e.g., generalizability and transportability methods), but merit consideration as a separate class of causal problems because they differ in terms of their scientific motivations, definitions of the target population, sampling, data structures, and identifiability conditions. In formalizing identification strategies for the analyses described herein, we hope to provide a conceptual foundation to support the systematic use and evaluation of such efforts.

翻译：将实验研究（包括随机试验和单组试验）中的信息与外部实验或观察性数据来源中的信息相结合，这一做法日益受到关注。此类努力通常出于比较不同研究中评估的疗法之目的（例如，为某项指数研究构建外部对照组），或旨在更精确地估计治疗效果。将实验研究与外部数据相结合的提议至少可追溯至20世纪70年代，但近年来在临床实践以及参与药物和器械评估的监管机构中受到越来越多的考虑，尤其是在试验和观察性数据日益可及的背景下。本文描述了将实验研究信息与外部数据相结合的基本研究模板，并利用潜在（反事实）结果框架来阐述潜在结果均值与平均治疗效果的识别策略。我们认为，这些识别策略继承了与单源研究中的因果关系研究以及相关信息结合文献（例如，可推广性和可迁移性方法）相关的思想，但由于其在科学动机、目标人群定义、抽样、数据结构和可识别性条件方面的差异，应将其视为一类独立的因果问题。通过形式化本文所述分析的识别策略，我们希望为支持此类努力的系统性应用和评估提供概念基础。