Budget impact analysis of breast cancer medications: a systematic review
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) is the most common cancer globally among women, with 2,261,419 new cases in 2020; systemic treatment may be neo-adjuvant, adjuvant, or both. BC subtype guides the standard systemic therapy administered, which consists of endocrine therapy for all HR + tumors, trastuzumab-based HER2-directed antibody therapy plus chemotherapy for all HER2 + tumors (with endocrine therapy given in addition, if concurrent HR positivity), and chemotherapy alone for the triple-negative subtype. This study aimed to identify, evaluate, and systematically review all budget impact analyses (BIAs) of BC medications worldwide. METHODS: PubMed, Scopus, and Web of Science Core Collection databases were thoroughly searched up to 26th March 2022 to identify original published studies which evaluate BIA of BC medications. ISPOR Task Force guidelines were used to assess the quality of included studies. This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: In total, 17 BIAs were included in the study. About half of the studies were conducted in Europe. The results of the BIAs showed that most of the included BIAs are conducted from the payer's perspective; they have different methodological frameworks for recommended chemotherapy, targeted therapy, and immunotherapy agents to treat BC. For the same medications, the results of budgetary effects are not consistent in diverse countries. Nine out of the 17 studies were focused on trastuzumab, in which the biosimilar form reduced costs, but the brand form increased costs, especially in a 52-week treatment period. CONCLUSION: Researchers should conduct the budget impact analysis of high-value medications such as anti-tumor drugs more objectively, and the accuracy of parameters needs to be more strictly guaranteed. Furthermore, it is worthy of declaring that the budgetary impact of the same drug is not always consistent over time, so the researchers should measure access to medication in the long run.
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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 | 0.033 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".