Considerations for the Integration of Randomized Controlled Trials and Real-World Data

TL;DR

This paper discusses how to effectively combine randomized controlled trials and real-world data using causal frameworks to improve clinical decision-making and treatment recommendations.

Contribution

It provides a structured approach to integrating RCTs and real-world data, emphasizing design, analysis, and practical considerations for credible evidence.

Findings

Highlights the importance of explicit causal frameworks for data integration.

Illustrates key design and analytic choices through example estimands.

Addresses practical issues like data relevance, comparability, and sensitivity analysis.

Abstract

As clinical decision-making increasingly moves toward individualized and context-specific treatment recommendations, reliance on any single evidence source, randomized or observational, may be insufficient. Principled integration of randomized controlled trials and real-world data, grounded in explicit causal frameworks, offers a path toward evidence that is both internally credible and externally relevant. In this article, we describe distinct objectives for the integration of randomized controlled trials and real-world data and discuss how these objectives shape key design and analytic considerations, illustrating the resulting choices through example estimands. We highlight practical issues that commonly arise in applied settings, including data relevance and curation, cross-source comparability, estimand specification, and sensitivity analysis. We aim for this article to help readers evaluate and implement principled approaches to integrating randomized controlled trials and real-world data in ways that can support more reliable treatment recommendations while maintaining regulatory-grade evidentiary standards.