Evidence-based planning of clinical research (EBAR)

This project aims to develop a framework of efficient and sustainable research planning that minimizes cost and has the potential to direct resource allocation through optimal planning of future clinical studies based on existing evidence. A circular updating process is suggested: before a trial is planned, the existing evidence (a network meta-analysis) about the effectiveness of all competing interventions is considered and its conclusiveness is statistically evaluated. If further experiments are needed these are designed considering patients’ preferences and with an aim to render the existing evidence conclusive. The new data is then used to update the existing knowledge. This innovative approach to planning clinical studies requires methodological developments in various fields (biostatistics, operational research, epidemiology). Its feasibility will be evaluated in answering a real clinical question about the best intervention for a given condition and in designing a new trial. The circular updating process will be of great interest to public research funding bodies, guideline developers and pharmaceutical companies. The project has a strong public engagement component, as patients and major healthcare organisations are involved in many of the planned activities.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 703254.

  • Bagg MK, Salanti G, McAuley JH. Comparing interventions with network meta-analysis. J Physiother. 2018 Apr;64(2):128-132. doi: 10.1016/j.jphys.2018.02.014.
  • Nikolakopoulou A, Mavridis D, Furukawa TA, Cipriani A, Tricco AC, Straus SE, Siontis GCM, Egger M, Salanti G. Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study. BMJ. 2018 Feb 28;360:k585.
  • Siontis GC, Mavridis D, Greenwood JP, Coles B, Nikolakopoulou A, Jüni P, Salanti G, Windecker S. Outcomes of non-invasive diagnostic modalities for the detection of coronary artery disease: network meta-analysis of diagnostic randomised controlled trials. BMJ. 2018 Feb 21;360:k504. doi: 10.1136/bmj.k504.
  • Pompoli A, Furukawa TA, Efthimiou O, Imai H, Tajika A, Salanti G. Dismantling  cognitive-behaviour therapy for panic disorder: a systematic review and component network meta-analysis. Psychol Med. 2018 Jan 25:1-9.
  • Zwahlen M, Salanti G. Causal inference from experiment and observation. Evid Based Ment Health. 2018 Feb;21(1):34-38.
  • Riley RD, Jackson D, Salanti G, Burke DL, Price M, Kirkham J, White IR. Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples. BMJ. 2017 Sep 13;358:j3932.
  • Petropoulou M, Nikolakopoulou A, Veroniki AA, Rios P, Vafaei A, Zarin W, Giannatsi M, Sullivan S, Tricco AC, Chaimani A, Egger M, Salanti G. Bibliographic study showed improving statistical methodology of network meta-analyses published between 1999 and 2015. J Clin Epidemiol. 2017 Feb;82:20-28.
  • Furukawa TA, Cipriani A, Atkinson LZ, Leucht S, Ogawa Y, Takeshima N, Hayasaka Y, Chaimani A, Salanti G. Placebo response rates in antidepressant trials: asystematic review of published and unpublished double-blind randomised controlled studies. Lancet Psychiatry. 2016 Nov;3(11):1059-1066.
  • Nikolakopoulou A, Mavridis D, Egger M, Salanti G. Continuously updated networkmeta-analysis and statistical monitoring for timely decision-making. Stat Methods Med Res. 2018 May;27(5):1312-1330.