The impact of pricing strategy on the cost of oral anti-cancer drugs during dose reductions.
Bibliographic record
Abstract
e18312 Background: The pricing strategy of oral medications can affect their costs. The strategy of flat pricing per tablet may increase drug costs in the event of dose reductions requiring more tablets, as there is a single price for different tablet strengths, but the impact is largely unknown. With the strategy of linear pricing, the tablet price increases with its strength. We sought to determine the impact of pricing strategy on the cost of oral anti-cancer drugs during dose reductions. Methods: Oral anti-cancer drugs reviewed by the pan-Canadian Oncology Drug Review were identified between July 2011 to January 2015. The pricing strategy of these drugs was reviewed. We examined the percentage change in cost per mg and cost per 28 days as a result of dose reduction from dose level 0 to -1 and -2 for each drug. Results: Seventeen drugs for use in 20 indications were included in the analysis. There were 3 drugs for hematological malignancies and 14 drugs for solid cancers. Fifty-nine percent (10/17) of these drugs were available in multiple strengths; five of them utilized fixed pricing per tablet strategy and the other 5 utilized linear pricing. The remaining drugs (7/17) were available in a single strength tablet. Dose reductions generally increased the cost per mg for drugs using flat pricing per tablet, with a mean increase of 82% (range: 25%-200%) at dose level -1 and 100% (range: 0%-200%) at dose level -2. Dose reduction had no effect on the cost per mg of drug for drugs using linear pricing apart from lenalidomide, which had increased costs due to minimal price variation between the highest and lowest tablet strengths. In general, dose reduction did not decrease the cost per 28 days of drug for drugs using flat pricing per tablet, but was proportionally reduced in drugs using linear pricing. Conclusions: While there is a general expectation that the cost of drugs should decrease with dose reduction, oral anti-cancer drugs using flat pricing per tablet have increased cost per mg and no decrease in cost per 28 days despite dose reduction. Future economic evaluations should account for the impact of dose reductions for oral drugs using the flat pricing per tablet strategy on cost-effectiveness and budget.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".