Session (CTI)

Health state utility (HSU) estimation underpins cost utility analysis, yet methodological choices are often poorly aligned with the underlying analytic objective. This workshop brings together academic and industry perspectives to examine how HSUs are conceptualised and estimated, and the implications for health technology assessment (HTA). We begin by outlining the importance of clearly framing HSU estimation as a descriptive, predictive, or causal task (Dr?Franklin). This framing has major implications for study design, data needs, interpretation, and use within decision models. Using philosophical arguments and directed acyclic graphs (DAGs), we show how lack of clarity leads to inconsistent practice and potentially biased model inputs. We will also present findings from an umbrella review of applied HSU estimation approaches, synthesising methods used across disease areas and data sources. From 185 primary studies identified across 19 systematic reviews, we highlight substantial variability in methodological choices, justification, and reporting standards. We then examine current HTA practice and guidance gaps, drawing on a review of HTA submissions and findings from a Delphi study conducted by a NICE External Assessment Group (Prof?Lee). We find that inconsistent, poorly reported, and methodologically variable analyses of patient level utility data in NICE appraisals create avoidable uncertainty, and we summarise consensus based recommendations to support more robust and standardised modelling. Finally, we discuss practical challenges from an industry perspective (Dr?Lloyd), including concerns that EQ 5D may not fully capture lived experience of many conditions, leading companies to feel that important aspects of treatment value are overlooked. We also highlight the persistent tension between methodological rigour, feasibility, timelines, and cost; particularly when early real world data collection is encouraged, despite the high likelihood that many products will not reach the market. The session will give attendees a clearer conceptual framework for HSU estimation and practical insights to improve methodological consistency and decision relevance.