Using the target trial framework for combining information: external comparator analyses and other applications
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利用目标试验框架整合信息：外部对照分析及其他应用
- 研究问题
- 将试验与其他来源的数据结合回答因果问题时，如何明确目标人群、抽样机制和研究设计，并识别数据之间难以协调的差异？
- 方法与思路
- 作者建议在目标试验框架中加入目标人群及其抽样模型，将多个数据源逐项映射到同一试验的资格、分配与处理接受等要素。
- 材料陈述的贡献
- 摘要提出加强多来源因果分析设计与报告的框架，说明人群定义和数据错配如何影响识别策略，必要时需更换或前瞻获取数据。
- 阅读关注点
- 应核对新增抽样模型与因果模型的衔接，以及外部对照、推广和迁移分析各自如何处理资格、处理定义和时间安排的错配。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实。摘要主要提供设计框架，未给出统一估计量或保证数据错配可被消除的条件。
原文摘要
We describe how the target trial framework can be used to plan and report analyses that attempt to answer causal questions by combining information from multiple, diverse sources. Such analyses may involve comparisons of treatments evaluated in different populations, for example when an index trial is combined with other data sources in external comparator analyses, or when extending causal inferences from a randomized trial to a new target population in generalizability and transportability analyses. When planning such analyses, the specification of the target trial supports the explicit definition of the target population with an associated sampling model. We propose this as an additional component for the target trial framework, especially relevant for analyses that combine information, because it influences the choice of eligibility criteria, the specification of the causal model, the choice of causal contrasts, and reasoning about identification strategies. Furthermore, the framework encourages careful mapping of data elements from multiple data sources to a single target trial. This mapping process can highlight potentially irreconcilable misalignments between data sources with respect to specific components of the framework -- for example, in the definitions of eligibility criteria, treatment assignment, and treatment receipt. Such misalignments can arise when attempts to specify a target trial that aligns with a specific data source introduce or worsen misalignments with other proposed data sources. The extent of such misalignments may warrant switching to other data sources, or prospectively obtaining data, to emulate the proposed target trial. We conclude that the target trial framework promotes transparent discussion about the design of and assumptions made in analyses that answer causal questions by combining information from diverse sources.