Using the target trial framework for combining information: external comparator analyses and other applications

2026-05-22

AI Summary

This study addresses the challenge of ensuring rigor in causal inference under multi-source heterogeneous data fusion by proposing a structured design paradigm grounded in the target trial framework. The approach explicitly incorporates the target population and its sampling model into the causal analysis, systematically integrating external controls, generalizability, and transportability assessments through data element alignment, transparent assumption articulation, and emulation of the target trial. Its key innovation lies in anchoring the entire framework to a precise definition of the target population, thereby identifying and mitigating irreconcilable conflicts across data sources. This strategy enhances both the reliability and interpretability of causal conclusions derived from complex, real-world data ecosystems.

Abstract

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.

Problem

• target trial framework
• external comparator analyses
• causal inference
• data integration
• transportability

Innovation

• target trial emulation
• causal inference
• external comparator analysis
• data integration
• transportability