Gianmario Candore

Objectives: To evaluate the feasibility and applicability of the studyathon format within a pharmaceutical company setting for generating reliable real-world evidence rapidly for a business-relevant research question. Materials and methods: Conducted at Bayer, the studyathon assessed recurrent events following a first-ever transient ischemic attack. It involved an 8-week preparatory phase, a 1-week execution phase, and a 3-week post-event phase, using multiple real-world data sources. Results: The studyathon concluded successfully with a conference abstract submission. Key learnings included the importance of defining precise research questions, organizational alignment, thorough preparation, and leveraging diverse expertise and skill sets. Discussion: The studyathon format proved suited for a pharmaceutical setting and revealed insights into its application. Our learnings highlight differences from multi-stakeholder community studyathons. Conclusion: The studyathon demonstrated its potential as a valuable research approach within a pharmaceutical company. It improved study timelines and cross-functional collaboration while preserving scientific rigor.

Introduction: Despite substantial investments in analytical infrastructure and scientific research related to the development and analysis of real‐world evidence in support of signal management, the impact on routine pharmacovigilance activities has been limited. Most organizations still rely largely on analyses of individual case reports and pre‐existing evidence – especially during signal detection and validation. Objective: This paper presents a set of recommendations for efforts to enable broader use of real‐world evidence throughout pharmacovigilance signal management, in the future. Outcome: The recommendations regard streamlined data access, data harmonization and use of reproducible analytical workflows to enable rapid and robust evidence generation. They emphasize the need for cross‐disciplinary collaboration and for organizational adaptations to ensure adequate competence and supporting processes, including principles for how to integrate new types of evidence in decision‐making. The execution of pilot studies under realistic conditions and the dissemination of their findings are highlighted as important steps toward defining the proposed change and driving progress in this area. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Real‐World Evidence (RWE) is increasingly used in submissions to regulatory agencies and health technology assessment bodies (HTAbs) to support the efficacy and effectiveness of new medicines and indications. However, there is limited information on the RWE characteristics that impact its role in approval and reimbursement decisions. To investigate these characteristics, we developed FRAME: a Framework for Real‐world evidence Assessment to Mitigate Evidence uncertainties for efficacy/effectiveness. We compiled a list of medicinal product indications where RWE supported the efficacy of interventional trials or assessed effectiveness in observational settings. FRAME was applied to a prioritized subset of these submissions to authorities from North America, Europe, and Australia. For each product indication, we extracted information on characteristics describing the submission, clinical context, strength of evidence, and process factors from publicly available assessment reports. Of the 87 identified medicinal product indications, 15 were prioritized, covering 68 submissions and 76 RWE studies across 11 authorities in scope. Four main results emerged: (i) low granularity within assessment reports on the analyzed variables, limiting the learnings from analyzing them; (ii) variability in how RWE was assessed within and across regulatory agencies and HTAbs; (iii) a positive association between the proportion of positive comments from authorities on RWE studies and their impact on decision making. Particularly, a large effect size was consistently noted when RWE was considered primary evidence; and (iv) limited use of advanced RWE study designs. These findings support five recommendations to enhance shared learning on RWE, clarify its evidentiary value, and generate evidence to better support authorities' decision making.

Although postmarketing studies conducted in population‐based databases often contain information on patients in the order of millions, they can still be underpowered if outcomes or exposure of interest is rare, or the interest is in subgroup effects. Combining several databases might provide the statistical power needed. A multi‐database study (MDS) uses at least two healthcare databases, which are not linked with each other at an individual person level, with analyses carried out in parallel across each database applying a common study protocol. Although many MDSs have been performed in Europe in the past 10 years, there is a lack of clarity on the peculiarities and implications of the existing strategies to conduct them. In this review, we identify four strategies to execute MDSs, classified according to specific choices in the execution: (A) local analyses, where data are extracted and analyzed locally, with programs developed by each site; (B) sharing of raw data, where raw data are locally extracted and transferred without analysis to a central partner, where all the data are pooled and analyzed; (C) use of a common data model with study‐specific data, where study‐specific data are locally extracted, loaded into a common data model, and processed locally with centrally developed programs; and (D) use of general common data model, where all local data are extracted and loaded into a common data model, prior to and independent of any study protocol, and protocols are incorporated in centrally developed programs that run locally. We illustrate differences between strategies and analyze potential implications.