The preferred evidence of a large randomized controlled trial is difficult to adopt in scenarios, such as rare conditions or clinical subgroups with high unmet needs, and evidence from external sources, including real-world data, is being increasingly considered by decision makers. Real-world data originate from many sources, and identifying suitable real-world data that can be used to contextualize a single-arm trial, as an external control arm, has several challenges. In this viewpoint article, we provide an overview of the technical challenges raised by regulatory and health reimbursement agencies when evaluating comparative efficacy, such as identification, outcome, and time selection challenges. By breaking down these challenges, we provide practical solutions for researchers to consider through the approaches of detailed planning, collection, and record linkage to analyze external data for comparative efficacy.

UNSTRUCTURED When the preferred evidence of a large, randomized controlled trial is difficult to run in scenarios such as rare conditions or clinical subgroups with high unmet need, evidence from external sources including real world data is increasingly being considered by decision-makers. Identifying suitable real-world data that can be used to contextualize a single-arm trial, as an external control arm, presents with several challenges. In this viewpoint, we provide an overview of the common issues raised by regulatory and health reimbursement agencies when evaluating comparative efficacy and present some solutions.