ArticlePLoS medicine2021
A framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials.
Article in PLoS medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effect and safety of intravenous iron compared to oral iron for treatment of iron deficiency anaemia in pregnancy.The Cochrane database of systematic reviews · 2024Pooled it
- Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework.Research synthesis methods · 2023Pooled it
- Pooled it
- Intermediate- versus prophylactic-dose heparin for hospitalised patients with COVID-19: an updated meta-analysis of randomised trials.Intensive care medicine · 2026Article
- Dental Research in the Digital Age: The Registry-Based Clinical Trial.Clinical oral implants research · 2026Article
- Evidence-based practice of exercise during dialysis in maintenance hemodialysis patients.International urology and nephrology · 2025Article
- Article
- Practical methods for incorporating summary time-to-event data into meta-analysis: updated guidance.Systematic reviews · 2025Article
- How can we make sound replication decisions?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Closing the gap in our understanding of infectious diseases.BMC infectious diseases · 2023Article
- Article
- Seroconversion rates in kidney transplant recipients following SARS-CoV-2 vaccination and its association with immunosuppressive agents: a systematic review and meta-analysis.Clinical and experimental vaccine research · 2023Review
- Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review.BMJ medicine · 2022Article
- ALL-IN meta-analysis: breathing life into living systematic reviews and prospective meta-analyses.F1000Research · 2022Article
- Article
- What works and for whom? Individual patient data meta-analyses in global nutrition research.The American journal of clinical nutrition · 2021Article
- Review
- Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues.Frontiers in psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundThe 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.
conclusionsFAME can reduce the potential for bias, and produce more timely, thorough and reliable systematic reviews of aggregate data.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.