1 resultado para Linear Attention,Conditional Language Model,Natural Language Generation,FLAX,Rare diseases
em Brunel University
Filtro por publicador
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Resumo:
BACKGROUND: In many countries, decisions about the public funding of drugs are preferentially based on the results of randomized trials. For truly rare diseases, such trials are not typically available, and approaches by public payers are highly variable. In view of this, a policy framework intended to fairly evaluate these drugs was developed by the Drugs for Rare Diseases Working Group (DRDWG) at the request of the Ontario Public Drug Programs. OBJECTIVE: To report the initial experience of applying a novel evaluation framework to funding applications for drugs for rare diseases. METHODS: Retrospective observational cohort study. MEASURES: Clinical effectiveness, costs, funding recommendations, funding approval. KEY RESULTS: Between March 2008 and February 2013, eight drugs were evaluated using the DRDWG framework. The estimated average annual drug cost per patient ranged from 28,000 to 1,200,000 Canadian dollars (CAD). For five drugs, full evaluations were completed, specific funding recommendations were made by the DRDWG, and funding was approved after risk-sharing agreements with the manufacturers were negotiated. For two drugs, the disease indications were determined to be ineligible for consideration. For one drug, there was insufficient natural history data for the disease to provide a basis for recommendation. For the five drugs fully evaluated, 32 patients met the predefined eligibility criteria for funding, and five were denied based on predefined exclusion criteria. CONCLUSIONS: The framework improved transparency and consistency for evaluation and public funding of drugs for rare diseases in Ontario. The evaluation process will continue to be iteratively refined as feedback on actual versus expected clinical and economic outcomes is incorporated. © 2014 Society of General Internal Medicine.