Presentation (CTI)

OBJECTIVES: Structural uncertainty limits comparability in health technology assessments (HTAs) and is a common source of External Assessment Group (EAG) critique, particularly in rare diseases. In Duchenne muscular dystrophy (DMD), successive submissions have adopted shared natural history data and aligned model structures. This study examined whether increasing reference model maturity was associated with a shift from structural to parameter related HTA uncertainty.
METHODS: A targeted review of published DMD HTA appraisals was conducted using the INAHTA database and supplementary searches of HTA agency websites, restricted to English language reports. Included appraisals were NICE HST3, TA1031, TA1157, and the CDA-AMC vamorolone review. Reference model maturity was defined and assessed using common health states, shared natural history data, and identifiable structural lineage across submissions. Critique themes were independently classified as structural or parameter related and summarized descriptively.
RESULTS: Increasing structural alignment was observed in health state design, time horizon, and use of natural history data. HST3 employed a relatively simplified partitioned survival model, and the EAG highlighted the need for greater ambulatory health state granularity. Subsequent appraisals (TA1031 and TA1157) adopted the Project HERCULES framework, which was considered appropriate by the EAG. Analysis of critique themes showed that structural concerns declined from approximately 40% in HST3 to 20% in TA1031 and 10% in TA1157, while parameter and extrapolation related concerns became increasingly prominent. A similar trend was observed in the CDA-AMC vamorolone review, where critique focused primarily on extrapolation and long-term modelling assumptions.
CONCLUSIONS: As the DMD reference framework matured, HTA uncertainty shifted from model structure toward model inputs and extrapolation assumptions. Shared natural history-based reference frameworks may reduce structural heterogeneity, improve comparability, and support more consistent HTA evaluation. Continued investment in robust natural history data is essential to enable development of such reference frameworks in rare diseases.