Tierney, JF;
Fisher, DJ;
Vale, CL;
Burdett, S;
Rydzewska, LH;
Rogozińska, E;
Godolphin, PJ;
... Parmar, MKB; + view all
(2021)
A framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials.
PLoS Medicine
, 18
(5)
, Article e1003629. 10.1371/journal.pmed.1003629.
(In press).
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Abstract
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 Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases.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.
| Type: | Article |
|---|---|
| Title: | A framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pmed.1003629 |
| Publisher version: | https://doi.org/10.1371/journal.pmed.1003629 |
| Language: | English |
| Additional information: | © 2021 Tierney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10127559 |
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