The impact of willingness-to-pay threshold on price reduction recommendations for oncology drugs: a review of assessments conducted by the Canadian Agency for Drugs and Technologies in Health
Bibliographic record
Abstract
Since late 2020, the Canadian Agency of Drugs and Technologies in Health (CADTH) has been using a threshold of $50,000 (CAD) per quality-adjusted life-year (QALY) for both oncology and non-oncology drugs. When used for oncology products, this threshold is hypothesized to have a higher impact on the time to access these drugs in Canada. We studied the impact of price reductions on time to engagement and negotiation with the pan-Canadian Pharmaceutical Alliance for oncology drugs reviewed by CADTH between January 2020 and December 2022. Overall, 103 assessments reported data on price reductions recommended by CADTH to meet the cost-effectiveness threshold for reimbursement. Of these assessments, 57% (59/103) recommendations included a price reduction of greater than 70% off the list price. Eight percent (8/103) were not cost-effective even at a 100% price reduction. Of the 47 assessments that had a clear benefit, in 21 (45%) CADTH recommended a price reduction of at least 70%. The median time to price negotiation (not including time to engagement) for assessments that received at least 70% vs >70% price reduction was 2.6 vs 4.8 months. This study showed that there is a divergence between drug sponsor's incremental cost-effectiveness ratio (ICER) and CADTH revised ICER leading to a price reduction to meet the $50,000/QALY threshold. For the submissions with clear clinical benefit the median length of engagement (2.5 vs 3.3 months) and median length of negotiation (3.1 vs 3.6 months) were slightly shorter compared with the submissions where uncertainties were noted in the clinical benefit according to CADTH. This study shows that using a $50,000 per QALY threshold for oncology products potentially impacts timely access to life saving medications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".