The Economic Impact of the Transition from Branded to Generic Oncology Drugs
Bibliographic record
Abstract
Background: Economic evaluations are an integral component of many clinical trials. Costs used in those analyses are based on the prices of branded drugs when they first enter the market. The effect of genericization on the cost-effectiveness (CE) or cost–utility (CU) of an intervention is unknown because economic analyses are rarely updated using the costs of generic drugs. Methods: We re-examined the CE or CU of regimens previously evaluated in Canadian Cancer Trials Group (CCTG) studies that included prospective economic evaluations and where genericization has occurred or is anticipated in Canada. We incorporated the new costs of generic drugs to characterize changes in CE or CU. We also determined acceptable cost levels of generic drugs that would make regimens reimbursable in a publicly funded health care system. Results: The four randomized controlled trials included (representing 1979 patients) were CCTG BR.10 (early lung cancer, adjuvant vinorelbine–cisplatin vs. observation, n = 172), CCTG BR.21 (metastatic lung cancer, erlotinib vs. placebo, n = 731), CCTG CO.17 (metastatic colon cancer, cetuximab vs. best supportive care, n = 557), and CCTG LY.12 (relapsed or refractory lymphoma, gemcitabine–dexamethasone–cisplatin vs. cytarabine–dexamethasone–cisplatin, n = 619). Since the initial publication of those trials, the genericization of vinorelbine, erlotinib, cetuximab, and cisplatin has taken place or is expected in Canada. Costs of generics improved the ces and cus of treatment significantly. For example, genericization of erlotinib ($1460.25 per 30 days) resulted in an incremental cost-effectiveness ratio (ICER) of $45,746 per life-year gained compared with $94,638 for branded erlotinib. Likewise, genericization of cetuximab ($275.80 per 100 mg) produced an icer of $261,126 per quality-adjusted life-year (QALY) gained compared with $299,613 for branded cetuximab. Decreases in the cost of generic cetuximab to $129.39 and $63.51 would further improve the icer to $150,000 and $100,000 per QALY respectively. Conclusions: Genericization of a costly oncology drug can modify the CE and CU of a regimen significantly. Failure to revisit economic analyses with the costs of generics could be a missed opportunity for funding bodies to optimize value-based allocation of health care resources. At current levels, the costs of generics might not be sufficiently low to sustain publicly funded health care systems.
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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 | 0.042 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".