The impact of pricing strategy on the costs of oral anti‐cancer drugs
Bibliographic record
Abstract
BACKGROUND: The soaring costs of anti-cancer drugs pose a threat to the sustainability of cancer care. The pricing strategy chosen by manufacturers can impact the costs of oral anti-cancer drugs during dose modifications, but this issue remains under-recognized in the literature. In general, with the flat pricing strategy, there is a single fixed price for each tablet regardless of dosage strength, whereas with linear pricing, the price of each tablet increases with its dose. We hypothesize that flat pricing will have increased drug costs compared to linear pricing during dose reductions since the cost remains fixed despite decreased dose requirements. This practice may have significant financial implications considering the high costs, extensive utilization, and frequent occurence of dose reductions with anti-cancer drugs. METHODS: Oral anti-cancer drugs reviewed by the pan-Canadian Oncology Drug Review program between 2011 and 2018 were identified. The cost per mg and cost per 28-day cycle were calculated for dose levels -2 to +2. The percent change in cost per mg and cost per cycle during dose modifications from the standard dose were calculated. We conducted Mann-Whitney U and Fisher-exact tests to compare the association between drug costs during dose reductions and pricing strategy. RESULTS: In this study, 30 oral anti-cancer drugs for use in 41 indications were analyzed; 44% of drugs used linear pricing and 56% used flat pricing. Dose reductions increased the mean cost per mg for drugs with linear pricing by 14.7% (range: 0%-50%) at dose level -1 and 17.2% (range: 0%-50%) at dose level -2 and flat pricing by 60.8% (range: 19%-100%) at dose level -1 and 99.1% (range: 0%-300%) at dose level -2. The cost per mg was significantly increased in drugs using flat pricing compared to linear pricing when dose reduction to either level -1 (P = 0.010) or level -2 (P = 0.006) occurred. The mean cost per cycle was decreased for drugs using linear pricing by 20.9% (range: -40% to 0%) at dose level -1 and 48.7% (range: -60% to -25%) at dose level -2 and flat pricing by 0.8% (range -6% to 0%) at dose level -1 and 11.0% (range: -50% to 100%) at dose level -2. The cost per cycle was significantly decreased in drugs with linear pricing compared to flat pricing when the standard dose is reduced to either dose level -1 (P = 0.005) or dose level -2 (P = 0.026). CONCLUSIONS: Overall, flat pricing had significantly greater costs compared to linear pricing during dose reductions of anti-cancer drugs. While there is a general expectation that the cost of drugs should decrease with dose reduction, drugs with flat pricing were generally found to have increased cost per mg and no change in the cost per cycle. The resultant increased spending on drug acquisition (despite purchasing lower doses) lead to financial wastage, which has significant implications on cost-effectiveness considerations and budgets. Future economic evaluations should take into consideration the hidden costs associated with dose reductions of flat priced drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".