Framework for the synthesis of non-randomised studies and randomised controlled trials: a guidance on conducting a systematic review and meta-analysis for healthcare decision making. Issue 2 (9th December 2020)

• Record Type: Journal Article
• Authors: Sarri, Grammati; Patorno, Elisabetta; Yuan, Hongbo; Guo, Jianfei (Jeff); Bennett, Dimitri; Wen, Xuerong; Zullo, Andrew R; Largent, Joan; Panaccio, Mary; Gokhale, Mugdha; Moga, Daniela Claudia; Ali, M Sanni; Debray, Thomas P A
• Is Part Of: BMJ evidence-based medicine. Volume 27:Issue 2(2022)
• Page Start: 109
• Page End: 119
• Publication Date: 2020-12-09
• Subjects: evidence-based practice -- health care economics and organisations -- health services research; Evidence-based medicine -- Periodicals; 616.005
• DOI: 10.1136/bmjebm-2020-111493
• Languages: English
• ISSNs: 2515-446X

Abstract

Abstract : Introduction: High-quality randomised controlled trials (RCTs) provide the most reliable evidence on the comparative efficacy of new medicines. However, non-randomised studies (NRS) are increasingly recognised as a source of insights into the real-world performance of novel therapeutic products, particularly when traditional RCTs are impractical or lack generalisability. This means there is a growing need for synthesising evidence from RCTs and NRS in healthcare decision making, particularly given recent developments such as innovative study designs, digital technologies and linked databases across countries. Crucially, however, no formal framework exists to guide the integration of these data types. Objectives and Methods: To address this gap, we used a mixed methods approach (review of existing guidance, methodological papers, Delphi survey) to develop guidance for researchers and healthcare decision-makers on when and how to best combine evidence from NRS and RCTs to improve transparency and build confidence in the resulting summary effect estimates. Results: Our framework comprises seven steps on guiding the integration and interpretation of evidence from NRS and RCTs and we offer recommendations on the most appropriate statistical approaches based on three main analytical scenarios in healthcare decision making (specifically, 'high-bar evidence' when RCTs are the preferred source of evidence, 'medium, ' and 'low' when NRS is the main source of inference). Conclusion: Our framework augments existing guidance on assessing the quality of NRS and their compatibility with RCTs for evidence synthesis, while also highlighting potential challenges in implementing it. This manuscript received endorsement from the International Society for Pharmacoepidemiology.