A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World Data
Open accessAbstract
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.
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The selection process for control data plays a key role in the quality and overall validity of any RBCT.
“The selection process for control data, therefore, plays a key role in the quality and overall validity of any RBCT (Gray et al. 2020).”
From Dental Research in the Digital Age: The Registry-Based Clinical Trial. (2026) · cites this paper as Gray et al. 2020
In “Augmented Randomised Controlled Trials” (page unavailable), the paper says evidence quality depends on assessing exchangeability between trial patients and real-world-data external comparators. “Step 1: Identification of Key Factors Affecting Bias” also identifies selection bias as a major concern. These passages support the importance of selecting suitable external control data, but the paper addresses trials using real-world-data external comparators, not necessarily every RBCT. (LLM, conf=0.80)
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- Authors
- Gray, C. M., Grimson, F., Layton, D., Pocock, S., & Kim, J.
- Affiliations
- IQVIA (United Kingdom) (GB)
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Gray, Christen M.
- Venue
- Drug Safety
- Published
- 2020-05-21
- Volume
- 43
- Issue
- 7
- Pages
- 623-633
- ISSN
- 0114-5916 (ISSN-L)
- 1179-1942
- PMC
- PMC7305259
- OpenAlex
- W3027856580
- Cited by
- 65
- References
- 59
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 18 · 2023: 10 · 2022: 8 · 2021: 3 · 2020: 2
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Safety (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (submittedVersion, cc-by-nc)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- real-world data, exchangeability, evidence hierarchy, bias assessment, covariate balancing, observational bias, trial design
- MeSH terms
- Clinical Trials as Topic, Humans, Product Surveillance, Postmarketing, Research Design, Selection Bias, Evidence-Based Medicine
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