A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World Data

Christen M. Gray (), Fiona Grimson, Deborah Layton, Stuart Pocock and Joseph Kim
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Christen M. Gray: EMEA Centre of Excellence for Retrospective Studies, IQVIA
Fiona Grimson: EMEA Centre of Excellence for Retrospective Studies, IQVIA
Deborah Layton: EMEA Centre of Excellence for Retrospective Studies, IQVIA
Stuart Pocock: London School of Hygiene and Tropical Medicine
Joseph Kim: EMEA Centre of Excellence for Retrospective Studies, IQVIA

Drug Safety, 2020, vol. 43, issue 7, No 4, 623-633

Abstract: Abstract Several approaches have been proposed recently to accelerate the pathway from drug discovery to patient access. These include novel designs such as using controls external to the clinical trial where standard randomised controls are not feasible. In parallel, there has been rapid growth in the application of routinely collected healthcare ‘real-world’ data for post-market safety and effectiveness studies. Thus, using real-world data to establish an external comparator arm in clinical trials is a natural next step. Regulatory authorities have begun to endorse the use of external comparators in certain circumstances, with some positive outcomes for new drug approvals. Given the potential to introduce bias associated with observational studies, there is a need for recommendations on how external comparators should be best used. In this article, we propose an evaluation framework for real-world data external comparator studies that enables full assessment of available evidence and related bias. We define the principle of exchangeability and discuss the applicability of criteria described by Pocock for consideration of the exchangeability of the external and trial populations. We explore how trial designs using real-world data external comparators fit within the evidence hierarchy and propose a four-step process for good conduct of external comparator studies. This process is intended to maximise the quality of evidence based on careful study design and the combination of covariate balancing, bias analysis and combining outcomes.

Date: 2020
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DOI: 10.1007/s40264-020-00944-1

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