Session (CTI)

PURPOSE: This workshop aims to provide participants with a practical understanding of how surrogate endpoint meta-analysis and matching techniques can be used to expand the evidence base in network meta-analyses (NMA). At the end of the session, attendees will learn how these approaches enhance the relevance and robustness of indirect comparisons in health technology assessment (HTA), with direct implications for reimbursement and regulatory decision-making. DESCRIPTION: Traditional NMA often relies solely on randomised controlled trials (RCTs), which may not fully represent real-world treatment pathways or populations. There are also cases where there may be very limited RCTs evidence. To address evidence gaps, innovative methods like surrogate endpoint meta-analysis and matching of treatment arms techniques can help integrate data from diverse sources, including observational studies and single-arm studies. This workshop explores how these methods improve decision-making in the absence of comprehensive head-to-head evidence. Raju will chair the session and introduce the topic, outlining the challenges in current evidence synthesis and setting the stage for methodological innovation (5 minutes). Janharpreet will present a summary and application of surrogate endpoint meta-analysis and matching of treatment arms techniques (15 minutes). Madhu will present a real-world case study demonstrating the application of these methods in an HTA setting from industry perspective (15 minutes). Kate will provide an overview of the acceptance of these methodologies from the perspective of a NICE External Assessment Group member (10 minutes). Audience engagement will include live polling, interactive scenario discussions, and Q&A (15 minutes). Participants will evaluate example data scenarios, vote on methodological choices, and engage in discussion with panellists, promoting practical understanding and application of the concepts presented. This session will benefit HTA professionals and industry stakeholders involved in generating or evaluating comparative effectiveness evidence for decision-making in healthcare.