MRC CTU at UCL announces the first prospective and collaborative approach to aggregate data meta-analysis
13 May 2021
FAME, our new framework for prospective, adaptive meta-analysis (FAME), can help produce more timely, thorough, and reliable systematic reviews of aggregate data.
We demonstrate how FAME can produce evaluations of treatment effects that are less prone to bias than conventional retrospective approaches, and potentially definitive meta-analysis results months or years ahead of all trial results being available. We show how FAME has provided timely, reliable and thorough evaluations of prostate cancer therapies.
Additionally we work with trial investigators, the advantage being that we gain access to better quality aggregate data, allowing more consistent, reliable, and thorough analyses than are usually possible. It also allows meta-analysis us to coordinate publication of meta-analysis and trials results potentially increasing the visibility and impact of each.
To our knowledge, FAME represents the first prospective and collaborative approach to aggregate data meta-analysis. Similar prospective meta-analyses are being used to balance speed with rigour in the evaluation of COVID-19 therapies.
By contrast, 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 Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases.
Professor Jayne Tierney, who led the research, said: "Less bias, better data and more timely results. I really believe that a collaborative and prospective approach should be the standard for aggregate data meta-analysis. I wish we had started doing it a long time ago!".