Comparative Effectiveness and Safety of Trastuzumab Biosimilars to Herceptin for Adjuvant Treatment of HER2+ Breast Cancer
Bibliographic record
Abstract
Background: Ontario publicly funds reference trastuzumab (Herceptin) and four biosimilar trastuzumab products for adjuvant treatment of HER2+ breast cancer. We assessed the real-world safety and effectiveness of biosimilar trastuzumab compared to Herceptin for adjuvant treatment of patients with HER2+ breast cancer. Methods: This was a population-based, retrospective study comparing the safety and effectiveness of biosimilar trastuzumab and Herceptin for neoadjuvant/adjuvant treatment of HER2+ breast cancer from 2016 to 2021. Treatment patients started biosimilar trastuzumab from November 2019 to June 2021; historical comparator patients started Herceptin from June 2016 to October 2019. Safety outcomes death within 30 days of last dose of trastuzumab, direct hospitalization, emergency department visit leading to hospitalization, early treatment discontinuation, and in-patient admission for congestive heart failure were measured using logistic/negative binomial regression. Overall survival (OS) was measured using Kaplan–Meier methods and Cox proportional hazards regression. Propensity score matching was applied. Results: From June 2016 to 2021, 5071 patients with breast cancer were treated with neoadjuvant/adjuvant trastuzumab. The rate of direct hospitalization (RR: 0.85, 95% CI: 0.74–0.98, p-value: 0.032) was significantly lower in biosimilar compared to Herceptin patients. OS (log-rank test p = 0.98) and risk of mortality (HR: 1.29, 95% CI: 0.72–2.30, p-value = 0.39) did not significantly differ between treatment groups. Conclusions: Biosimilar trastuzumab demonstrated similar safety and effectiveness to Herceptin. The findings can help improve confidence in and use of biosimilars and demonstrate the value of real-world evidence generation for supporting biosimilar implementations and reassessments.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".