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.
- evidence-based practice
- health care economics and organisations
- health services research
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Collaborators Recommendations from the Working Group of the International Society for Pharmacoepidemiology (ISPE) Comparative Effectiveness Research Special Interest Group for the cross-design synthesis of evidence and endorsed by the ISPE Board of Directors.
Contributors All authors conceived and developed the framework described in this manuscript. GS and TPAD drafted the manuscript and responded to other authors’ comments. All authors reviewed, contributed to revisions and approved the final version of the manuscript.
Funding Funding to support this manuscript development was provided by ISPE (https://www.pharmacoepi.org/ISPE/assets/File/General/FINAL20Call20for20Manuscripts204-24-19.pdf).
Disclaimer This article reflects the views and opinions of the authors and does not necessarily represent the views of the organisations where they are employed.
Competing interests We have read and understood BMJ Evidence-Based Medicine policy on declaration of interests and declare the following interests: GS is employed by Visible Analytics, Ltd; DB is employed by Takeda; ARZ holds a grant from Sanofi Pasteur (direct to institution); MP owns stocks from Merck, Sanofi, and Johnson & Johnson; MG is employed by GSK; and TD is an advisor to pharma industry.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement N/A.
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