Using decision modeling to determine pricing of new pharmaceuticals: The case of neurokinin-1 receptor antagonist antiemetics for cancer chemotherapy
Bibliographic record
Abstract
OBJECTIVES: Decision analysis is commonly used to perform economic evaluations of new pharmaceuticals. The outcomes of such studies are often reported as an incremental cost per quality-adjusted life year (QALY) gained with the new agent. Decision analysis can also be used in the context of estimating drug cost before market entry. The current study used neurokinin-1 (NK-1) receptor antagonists, a new class of antiemetics for cancer patients, as an example to illustrate the process using an incremental cost of dollars Can20,000 per QALY gained as the target threshold. METHODS: A decision model was developed to simulate the control of acute and delayed emesis after cisplatin-based chemotherapy. The model compared standard therapy with granisetron and dexamethasone to the same protocol with the addition of an NK-1 before chemotherapy and continued twice daily for five days. The rates of complete emesis control were abstracted from a double-blind randomized trial. Costs of standard antiemetics and therapy for breakthrough vomiting were obtained from hospital sources. Utility estimates characterized as quality-adjusted emesis-free days were determined by interviewing twenty-five oncology nurses and pharmacists by using the Time Trade-Off technique. These data were then used to estimate the unit cost of the new antiemetic using a target threshold of dollars Can20,000 per QALY gained. RESULTS: A cost of dollars Can6.60 per NK-1 dose would generate an incremental cost of dollars Can20,000 per QALY. The sensitivity analysis on the unit cost identified a range from dollars Can4.80 to dollars Can10.00 per dose. For the recommended five days of therapy, the total cost should be dollars Can66.00 (dollars Can48.00-dollars Can100.00) for optimal economic efficiency relative to Canada's publicly funded health-care system. CONCLUSIONS: The use of decision modeling for estimating drug cost before product launch is a powerful technique to ensure value for money. Such information can be of value to both drug manufacturers and formulary committees, because it would facilitate negotiations for optimal pricing in a given jurisdiction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".