OBJECTIVES: To identify challenges that affect the feasibility and rigor of economic models in rare diseases and strategies that manufacturers have employed in health technology assessment submissions to demonstrate the value of new orphan products that have limited study data.
METHODS: Targeted reviews of PubMed, the National Institute for Health and Care Excellence's (NICE's) Highly Specialised Technologies (HST), and the Scottish Medicines Consortium's (SMC's) ultra-orphan submissions were performed.
RESULTS: A total of 19 PubMed studies, 3 published NICE HSTs, and 11 ultra-orphan SMC submissions were eligible for inclusion. In rare diseases, a number of different factors may affect the model's ability to comply with good practice recommendations. Many products for the treatment of rare diseases have an incomplete efficacy and safety profile at product launch. In addition, there is often limited available natural history and epidemiology data. Information on the direct and indirect cost burden of an orphan disease also may be limited, making it difficult to estimate the potential economic benefit of treatment. These challenges can prevent accurate estimation of a new product's benefits in relation to costs. Approaches that can address such challenges include using patient and/or clinician feedback to inform model assumptions; data from disease analogues; epidemiological techniques, such as matching-adjusted indirect comparison; and long-term data collection.
CONCLUSIONS: Modeling in rare diseases is often challenging; however, a number of approaches are available to support the development of model structures and the collation of input parameters and to manage uncertainty.