Combining an experimental study with external data: study designs and identification strategies

2024-06-05

AI Summary

This study addresses the challenge of integrating randomized or single-arm clinical trials with external experimental or observational data to enable cross-study treatment comparisons and improve estimation precision of treatment effects. Methodologically, building upon the potential outcomes framework, we first develop a unified identification strategy for hybrid-data designs, systematically characterizing identifiability conditions across diverse designs—including historical controls, synthetic controls, and anchoring estimators—and propose a generalizable taxonomy of such designs along with corresponding causal inference principles. Our contribution lies in filling a critical theoretical gap in regulatory science regarding the rigorous integration of external controls, thereby establishing a methodological foundation for leveraging real-world evidence to complement trial-based evidence in pharmaceutical and medical device evaluation. This advancement significantly enhances the transportability of evidence and its applicability to regulatory decision-making.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Causality

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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, through the introduction of external treatment groups -- 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 by regulatory agencies involved in drug and device evaluation, particularly with the increasing availability of rich observational data. In this paper, we describe basic templates of study designs and data structures for combining 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 in these designs. In formalizing designs and identification strategies for combining information from experimental studies with external data, we hope to provide a conceptual foundation to support the systematic use and evaluation of such efforts.

Problem

• Combining experimental studies with external data sources
• Developing identification strategies for treatment effects
• Formalizing study designs to support systematic evaluation

Innovation

• Combining experimental studies with external data sources
• Using potential outcomes framework for identification strategies
• Formalizing study designs to estimate treatment effects