Open AccessA framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials
Author(s) -
Jayne Tierney,
David J. Fisher,
Claire Vale,
Sarah Burdett,
Larysa Rydzewska,
Ewelina Rogozińska,
Peter J. Godolphin,
Ian R. White,
Mahesh Parmar
Publication year - 2021
Publication title -
plos medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.847
H-Index - 228
eISSN - 1549-1676
pISSN - 1549-1277
DOI - 10.1371/journal.pmed.1003629
Subject(s) - meta analysis , systematic review , publication bias , protocol (science) , aggregate data , clinical trial , publication , data science , medline , computer science , medicine , medical physics , alternative medicine , pathology , political science , advertising , law , business
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 Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases.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.