The impact of cancer drug wastage on economic evaluations
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine the impact of modeling cancer drug wastage in economic evaluations because wastage can result from single-dose vials on account of body surface area- or weight-based dosing. METHODS: Intravenous chemotherapy drugs were identified from the pan-Canadian Oncology Drug Review (pCODR) program as of January 2015. Economic evaluations performed by drug manufacturers and pCODR were reviewed. Cost-effectiveness analyses and budget impact analyses were conducted for no-wastage and maximum-wastage scenarios (ie, the entire unused portion of the vial was discarded at each infusion). Sensitivity analyses were performed for a range of body surface areas and weights. RESULTS: Twelve drugs used for 17 indications were analyzed. Wastage was reported (ie, assumptions were explicit) in 71% of the models and was incorporated into 53% by manufacturers; this resulted in a mean incremental cost-effectiveness ratio increase of 6.1% (range, 1.3%-14.6%). pCODR reported and incorporated wastage for 59% of the models, and this resulted in a mean incremental cost-effectiveness ratio increase of 15.0% (range, 2.6%-48.2%). In the maximum-wastage scenario, there was a mean increase in the incremental cost-effectiveness ratio of 24.0% (range, 0.0%-97.2%), a mean increase in the 3-year total incremental budget costs of 26.0% (range, 0.0%-83.1%), and an increase in the 3-year total incremental drug budget cost of approximately CaD $102 million nationally. Changing the mean body surface area or body weight caused 45% of the drugs to have a change in the vial size and/or quantity, and this resulted in increased drug costs. CONCLUSIONS: Cancer drug wastage can increase drug costs but is not uniformly modeled in economic evaluations. Cancer 2017;123:3583-90. © 2017 American Cancer Society.
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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 | 0.117 | 0.347 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".