Describing Sources of Uncertainty in Cancer Drug Formulary Priority Setting across Canada.
Kristina JeneiStuart PeacockMichael BurgessCraig MittonPublished in: Current oncology (Toronto, Ont.) (2021)
Over the years, there have been significant advances in oncology. However, the rate that therapeutics come to market has increased, while the strength of evidence has decreased. Currently, there is limited understanding about how these uncertainties are managed in provincial funding decisions for cancer therapeutics. We conducted qualitative interviews with six senior officials from four different Canadian provinces (British Columbia, Alberta, Quebec, and Ontario) and a document review of the uncertainties found in submissions to the pan-Canadian Oncology Drug Review (pCODR). Participants reported considerable uncertainty related to a lack of solid clinical evidence (early-phase clinical trials: generalizability, immature data, and the use of unvalidated surrogate outcomes). Proposed strategies to deal with the uncertainty included risk-sharing agreements, collection of real-world evidence (RWE), and ongoing collaboration between federal groups and provinces. The document review added to the reported uncertainties by classifying them into five main categories: trial validity, population, comparators, outcomes, and intervention. This study highlights how decision makers must deal with significant amounts of uncertainty in funding decisions for cancer drugs, most of which stems from methodological limitations in clinical trials. There is a critical need for transparent priority-setting processes and mechanisms to reevaluate drugs to ensure benefit given the high level of uncertainty of novel therapeutics.
Keyphrases
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- papillary thyroid
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- squamous cell
- randomized controlled trial
- phase ii
- drug induced
- lymph node metastasis
- study protocol
- palliative care
- squamous cell carcinoma
- phase iii
- emergency department
- insulin resistance
- machine learning
- healthcare
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- childhood cancer
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- metabolic syndrome
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- weight loss
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- health insurance
- artificial intelligence