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The Relative Importance of Clinical, Economic, Patient Values and Feasibility Criteria in Cancer Drug Reimbursement in Canada: A Revealed Preferences Analysis of Recommendations of the Pan-Canadian Oncology Drug Review 2011–2017

Chris Skedgel (), Dominika Wranik and Min Hu
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Chris Skedgel: University of East Anglia
Dominika Wranik: Dalhousie University
Min Hu: Dalhousie University

PharmacoEconomics, 2018, vol. 36, issue 4, 467-475

Abstract: Abstract Background Most Canadian provinces and territories rely on the pan-Canadian Oncology Drug Review (pCODR) to provide recommendations regarding public reimbursement of cancer drugs. The pCODR review process considers four dimensions of value—clinical benefit, economic evaluation, patient-based values and adoption feasibility—but they do not define weights for individual decision criteria or an acceptable threshold for any of the criteria. Given this implicit review process, it is of interest to understand which factors appear to carry the most weight in pCODR recommendations using a revealed preferences approach. Methods Using publicly available decision summaries (n = 91) describing submissions and resulting recommendations 2011–2017, we extracted ten attributes that characterized each submission. Using logistic regression, we identified statistically significant attributes and estimated their relative impact in final recommendations. Results Clinical aspects appear to carry the greatest weight in the decision to reject or not reject, along with aspects of patient value (treatments with no alternatives were less likely to be rejected). Cost effectiveness does not appear to play a role in the initial decision to reject or not reject but is critical in full versus conditional approvals. There is evidence of a maximum acceptable threshold of around $Can140,000 per quality-adjusted life-year (QALY) gained. Conclusion A set of factors driving pCODR recommendations is identifiable, supporting the consistency of the review process. However, the implicit nature of the review process and the difficulty of extracting and interpreting some of the attribute levels used in the analysis suggests that the process may still lack full transparency.

Date: 2018
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