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Support for this research was provided by Regeneron Pharmaceuticals, Tarrytown, New York, and Sanofi US, Bridgewater, New Jersey. Menzin and Lin are employees of Boston Health Economics, which received consulting fees from Sanofi. Friedman is a consultant to Boston Health Economics. Lin, Friedman, and Menzin have received research support from Sanofi US. Sung, Mallya, Panaccio, and Koren are employees of Sanofi US and also have ownership interest in Sanofi US. Sanchez is an employee of and has ownership interest in Regeneron Pharmaceuticals. Neumann has served on advisory boards for Merck & Co, Takeda Pharmaceutical Company, Genentech, Novartis, Bayer AG, UCB, Sanofi US, Robert Wood Johnson Foundation, and Cubist and serves as consultant for Boston Health Economics, Forrest, P urdue, and Smith and Nephew. This research has been presented in part at the International Society for Pharmacoeconomics and Outcomes Research, 20th Annual International Meeting, May 16-20, 2015, Philadelphia, Pennsylvania. All authors contributed to the study design, protocol development, and results interpretation. Lin and Menzin were responsible for conducting the study analyses. All authors were involved in manuscript development and approved the submitted version.

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.

Historical audiometric data combined with noise monitoring data can be used to better understand the effectiveness of HCPs. Regular collection and maintenance of quality data should be encouraged and used to monitor the effectiveness of these interventions.

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