Evaluating the Impact of Different Natural History Modeling Methods on Cost-Effectiveness Decisions: A Case Study in Duchenne Muscular Dystrophy

Introduction. Cost-effectiveness analyses are vital in guiding decisions on treatment reimbursement. Natural history models are central to these, enabling the estimation of long-term costs and quality-adjusted life-years (QALYs) in the absence of lifetime trial data. Rare disease data are often scarce, resulting in disease progression being estimated through clinical assumptions. This study aims to evaluate how different modeling approaches influence cost-effectiveness estimates in rare disease health technology assessments (HTAs), using Duchenne muscular dystrophy (DMD) as a case study. Methods. A published economic model was used to compare 2 approaches for estimating disease progression: an assumption-based method relying on clinical plausibility and data-driven methods using data from 1,005 patients with DMD across 8 studies. Transition probabilities were estimated assuming increasing flexibility of study heterogeneity and compared with a simulated treatment cohort. Models were evaluated by comparing incremental cost-effectiveness ratios (ICERs) across approaches. No gold standard exists, so the plausibility of predictions was evaluated by comparing survival and disease progression estimates to published milestones. Results. Results showed that although the assumption-based model was clinically plausible, it predicted higher QALY gains (0.77) and lower ICERs (£1.96M per QALY) than data-driven methods did, which estimated QALY gains of 0.25, 0.26, 0.27, and 0.28 and ICERs of £6.2M, £6.2M, £5.8M, and £5.7M per QALY for the least to most flexible models, respectively. Limitations. No covariate effects or updated cost and utility data were incorporated, as the study purpose was a methodological comparison between approaches. Analyses were deterministic not probabilistic. Conclusions and Implications. This study emphasizes the critical role of model selection for HTA in rare diseases, showing that cost-effectiveness estimates from robust data-driven approaches can differ from clinically plausible assumption-based models. Highlights The choice of a natural history modeling method can drastically alter the cost-effectiveness results in rare disease evaluations. A case study in Duchenne muscular dystrophy demonstrates how different modeling approaches yield divergent cost-effectiveness outcomes. Assumption-based models, even when clinically plausible, may underestimate measures of cost-effectiveness and result in less reliable guidance for decision makers. Data-driven models using real-world patient data provide more reliable estimates for health technology assessment (HTA). This study offers practical guidance for analysts and HTA bodies on selecting robust modeling approaches in rare disease contexts.