The relative importance of clinical, economic, social and organizational criteria in cancer drug reimbursement in Canada: A revealed preferences analysis of recommendations of the pan Canadian Oncology Drug Review 2011-2016 Dr. Christopher Skedgel (University of East Anglia) Dr. W. Dominika Wranik (Dalhousie University) Min Hu (Dalhousie University)

Wranik, Katz, Levy, Korchagina, Edwards Canadian Health System 11/28/2018 CANADA HEALTH ACT - FEDERAL Publicly administered; Comprehensive; Accessible; Transferrable; Universal; PUBLIC HEALTH CARE BUDGET Provincial/ Territorial Federal transfer (ca. 15%) Provincial taxation (ca. 85%) PRIVATE SPENDING 30% of total spending Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Health Expenditures in Canada Health Expenditures in Canada in 2015 (‘000,000 CAD) Category Amount Federal Health Transfer 34,026 Total Health Expenditure (All Sectors) 219,144 Total Health Expenditure (Public) 155,000 Total Drug Expenditure (All Sectors) 34,452 Total Drug Expenditure (Public) 12,589 Public to Total Drug Expenditure (%) 37 Per Capita Drug Expenditure (Public) ($) 350 1 Canadian Institute for Health Information. National Health Expenditure Database. October 28,2015. www.cihi.ca 2 Statistics Canada, CANSIM Tables, 2015 Estimates http://www.statcan.gc.ca/daily-quotidien/151216/dq151216e-cansim-eng.htm 3 Federal Support to Provinces. 2015/16 Budget Estimates. http://www.fin.gc.ca/fedprov/mtp-eng.asp Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences

Wranik, Katz, Levy, Korchagina, Edwards Canadian Formulary Review Process Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 Canadian Agency for Drugs and Technologies in Health pan-Canadian Oncology Drug Review Review and formulary recommendation for non-cancer drugs (CDR) Review and formulary recommendation for cancer drugs (pCODR) Provincial formulary decision for all drugs Rationale for Centralized Review Support consistency across Provinces and Territories Speed up review process Cancer drugs have unique features Review Criteria Clinical Benefit Economic Evaluation Patient-based Values Adoption Feasibility Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards pan-Canadian Oncology Drug Review (pCODR) Process Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 Step 1 – Screen request for review and initiate review process Step 2 – Collect patient advocacy group information Step 3 – Collect registered clinician information Step 4 – Conduct clinical review Step 5 – Conduct economic review * Step 6 – Clarify information with the submitter of drug review * Step 7 – Summarize and review with pERC * Step 8 – Post initial recommendation, receive feedback, and review with pERC Step 9 – Post final recommendation including all feedback (REPORT) RECOMMENDATION Approve Conditionally approve Reject FUNDING DECISION Provincial/ Territorial Ministry of Health Possible for manufacturer to offer different prices to different Provinces/ Territories Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 GOAL To characterize challenges with the use of several types of evidence (with focus on economics) in recommendations made by the pCODR. RATIONALE Ongoing challenges with the production and use of economic evidence in the context of formulary committees. REVEALED PREFERENCES What do pCODR decisions reveal about the relative importance of decision criteria assigned by the committee? STATED PREFERENCES What do individuals involved in the pCODR process say is the relative importance of decision criteria? ROLE OF PATIENT VALUES How are patient values defined and used to contribute to the decision process? QUALITATIVE INTERVIEWS How to individuals involved in the pCODR process describe their approach to balancing competing criteria? Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 REVEALED PREFERENCES How are the criteria (clinical benefit, economic evaluation, patient-based values, and adoption feasibility weighted by the pCODR expert review committee, as revealed by the recommendations made. Variable Values (% frequencies) Recommendation Approve (16) Conditional (58) Reject (26) Clinical Benefit Yes (66) Maybe (13) No (21) Relative Survival Gain Yes (78) No (22) OS Flag Yes (54) No (46) Quality of Clinical Evidence High (77) Low (23) ICER ($150,000) High (62) Low (38) ICER reported (flag) Yes (91) No (9) ICER uncertainty High (83) Low (17) Severity of side effects High (17) Low (83) Unmet need (no alternatives) Yes (26) No (74) Type of drug Oral (50) IV (50) Infrastructure High (56) Low (44) Budget impact High (79) Low (21) VARIABLE CODING 94 publically available pCODR Expert Review Committee reports, each including the recommendation and description of deliberation about each criterion. Independently coded by two reviewers, conflicts discussed, third reviewer with pivotal vote when necessary. Initial sample of 10 used to validate code. Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 RESEARCH QUESTIONS Model 1 – What are the factors associated with a rejection as compared to an approval (full or conditional)? Model 2 – What is the ICER threshold between full and conditional approval? STATISTICAL APPROACH Model 1 (n=94) Binary dependent variable: Rejection and Not Rejection. Tested all possible combinations of variables and plausible interaction terms. Selected preferred specification based on the highest predictive power and lowest Aikake’s Information Criterion (AIC). Calculated marginal effects for each variable included in the preferred specification to estimate its impact on the odds of rejection. Model 2 (n=70) Binary dependent variable: Approval and Conditional Approval. Preferred model selection same as in Model 1. Derived predicted probability of full approval by ICER. Identified ICER inflection points and threshold for full approval greater than 50%. Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 Model 1 Results Rejection versus Approval (F/C) Variable (reference level) Coefficient Standard Error p-value Marginal Effects Intercept -0.1402 0.6028 0.82 -- Quality of clinical evidence (high) -1.5458 0.6585 0.02 -24.5% Budget impact (low) -1.0153 0.7219 0.16 -16.1% Relative survival X adverse events (low) -0.8207 0.3965 0.04 -13.0% AIC 83.346 Predictive power (all decisions) 81 % Drugs with higher quality clinical evidence and low budget impact are less likely to be rejected. Drugs with greater survival advantage combined with low side effects are less likely to be rejected. Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 Model 2 Results Full Approval versus Conditional Approval Variable (reference level) Coefficient Standard Error p-value Marginal Effects Intercept 1.4240 2.2689 0.53 -- ICER x 10K -0.0509 0.0169 0.00 -3.0% OS Flag (yes) 5.5410 3.8028 0.15 33.3% Relative survival X OS Flag -3.6641 2.3300 0.12 -22.0% Relative survival X AE (low) 2.0529 1.1599 0.08 12.3% AIC 26.39 Predictive power (all decisions) 70 % Drugs with higher relative survival combined with low side effects are more likely to be fully approved. Drugs with higher ICER are less likely to gain full approval. Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 Model 2 Results ICER threshold ICER ($/QALY) Full Approval Conditional Rejection Total 0 – 50,000 5 1 6 50,001 – 100,000 4 2 11 100,001 – 150,000 3 19 150,001 – 200,000 14 8 22 200,001 and up 28 Unreported 15 55 24 94 Above $150,000 there are no full approvals. Above $100,000, the probability of conditional approval (versus full) increases. Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.

Wranik, Katz, Levy, Korchagina, Edwards AFFORD – Affordable eFfective eFficient Oncology Reimbursement Decisions Wranik, Katz, Levy, Korchagina, Edwards 11/28/2018 Discussion First to estimate relative weights of decision criteria assigned in oncology drug funding recommendations in Canada. Decision to reject a submission is driven primarily by its clinical profile. This is consistent with studies from Belgium, Canada (CADTH), Poland, the United Kingdom and Wales that highlight importance of clinical superiority and clinical uncertainty. Economic criteria play lesser role in the decision to reject, but play a key role in the decision to approve fully or conditionally. Feasibility (budget impact) plays a small role in the rejection decision. Patient based values do not appear to play a role (subject of a separate analysis). Skedgel, Wranik, Hu (2017) – pCODR Revealed preferences Working draft - Please DO NOT CITE without permission of the authors.