A framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials.
Open accessAbstract
Background: The vast majority of systematic reviews are planned retrospectively, once most eligible trials have completed and reported, and are based on aggregate data that can be extracted from publications. Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases. We present a collaborative framework for prospective, adaptive meta-analysis (FAME) of aggregate data to provide results that are less prone to bias. Also, with FAME, we monitor how evidence from trials is accumulating, to anticipate the earliest opportunity for a potentially definitive meta-analysis. Methodology: We developed and piloted FAME alongside 4 systematic reviews in prostate cancer, which allowed us to refine the key principles. These are to: (1) start the systematic review process early, while trials are ongoing or yet to report; (2) liaise with trial investigators to develop a detailed picture of all eligible trials; (3) prospectively assess the earliest possible timing for reliable meta-analysis based on the accumulating aggregate data; (4) develop and register (or publish) the systematic review protocol before trials produce results and seek appropriate aggregate data; (5) interpret meta-analysis results taking account of both available and unavailable data; and (6) assess the value of updating the systematic review and meta-analysis. These principles are illustrated via a hypothetical review and their application to 3 published systematic reviews. Conclusions: FAME can reduce the potential for bias, and produce more timely, thorough and reliable systematic reviews of aggregate data.
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Researchers should also be aware of the potential for bias arising from the appearance of false patterns due to prior knowledge of the data.
“Researchers should also be aware of the potential for bias arising from the appearance of false patterns due to prior knowledge of the data (Tierney et al. 2021).”
From Dental Research in the Digital Age: The Registry-Based Clinical Trial. (2026) · cites this paper as Tierney et al. 2021
The PICO framework, outcome definitions, and subgroups of interest were agreed before outcome data were collected.
“The PICO (patient problem or population, intervention, comparison or control, and outcome) framework, definitions of outcomes, and subgroups of interest were agreed upon prior to collection of outcome data.6”
From Association Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19: A Meta-analysis (2021) · cites this paper as [6]
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References 41 · Cited by 31
- Authors
- Tierney, J. F., Fisher, D. J., Vale, C. L., Burdett, S., Rydzewska, L. H., Rogozińska, E., Godolphin, P. J., White, I. R., & Parmar, M. K. B.
- Affiliations
- MRC Clinical Trials Unit at UCL (GB)
- Corresponding author
- Tierney, Jayne F.
- ORCID
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-05-06
- Volume
- 18
- Issue
- 5
- Pages
- e1003629
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC8115774
- OpenAlex
- W3159909500
- Cited by
- 31
- References
- 41
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 7 · 2021: 5
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Update notices
- new_version (2021-05-12)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- UKRI Medical Research Council (MC_UU_12023/24)
- Prostate Cancer UK (RIA 16-ST2-020)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- aggregated data, randomised trials, reporting bias, publication bias, prostate cancer
- MeSH terms
- Humans, Male, Prostatic Neoplasms, Meta-Analysis as Topic
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