Accounting for improved outcomes in budget impact analyses: Adjuvant trastuzumab in HER2/neu-positive breast cancer
Bibliographic record
Abstract
6573 Background: Adjuvant trastuzumab is associated with considerable acquisition costs, but it is important to consider net budget impact after accounting for improved outcomes. We estimated the net budget impact of adjuvant trastuzumab (aTZ) in early-stage HER2/neu-positive breast cancer from the perspective of the Canadian healthcare system. Methods: The budget impact analysis (BIA) built upon a cost-effectiveness model (Skedgel et al, ASCO 2007) that considered the costs of drug acquisition and delivery, cardiotoxicities and recurrent disease, as well as improved disease-free and overall survival outcomes derived from the HERA trial of aTZ vs. primary treatment alone, over a 5-year horizon. The BIA took a population-level approach based on estimated 2007 breast cancer incidence rates, population projections for 2007–2011 and estimated eligibility rates from our previous work (Drucker et al, ASCO 2006). All costs are reported in 2007 Canadian dollars (CDN$). Results: aTZ had an upfront cost of CDN$49,965 per patient, but reductions in cancer recurrence over the 5-year analysis horizon reduced the net cost to CDN$44,998 per patient. Net impact declined over the initial 5 years (2007- 2011) as savings due to recurrences avoided accrued (start-up phase) and the ratio of net to gross annual budget impact fell from 95% to 85%. The cumulative net impact over the initial 5-year period (2007–2011) was roughly 10% less than the gross impact based on acquisition costs alone. In the second 5-year period (2012–2016), net and gross budget impact increased with population growth and increasing incidence in an ageing population (‘steady-state’ phase). Net budget impact in 2007 was projected to be CDN$91.3 million. Budget impact was driven primarily by the aTZ eligibility rate (8.5% in the baseline analysis). Conclusions: Annual net budget impact declined over the start- up phase as upfront costs were offset in part by savings due to recurrences avoided. Net budget impact in the steady-state phase grew at the same rate as the gross impact, but started from a lower baseline. While the projected net budget impact of aTZ was substantial, this analysis demonstrates the importance of accounting for differences in outcomes when conducting BIA. No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".