A Systematic Review and Meta-Analysis of Bevacizumab in First-Line Metastatic Breast Cancer: Lessons for Research and Regulatory Enterprises
Bibliographic record
Abstract
BACKGROUND: The US Food and Drug Administration's accelerated approval and later withdrawal of bevacizumab in patients with metastatic breast cancer (mBC) is a seminal case for ongoing debates about the validity of using progression-free survival (PFS) as a surrogate measure for overall survival (OS) in cancer drug approvals. We systematically reviewed and meta-analyzed the evidence around bevacizumab's regulatory approval and withdrawal in mBC. METHODS: We searched for all published phase II or III clinical trials testing bevacizumab as a first-line therapy for patients with mBC. Data were extracted on trial demographics, interventions, and outcomes. Descriptive analysis was stratified by whether the trial was initiated before, during, or after the accelerated approval. We used a cumulative random-effects meta-analysis to assess the evolution of evidence of the effect of bevacizumab on PFS and OS. We estimated the association between the trial-level PFS and OS effect using a nonlinear mixed-regression model. RESULTS: Fifty-two studies were included. Trial activity dramatically dropped after the accelerated approval was withdrawn. Eight clinical trials reported hazard ratios (hazard ratios) and were meta-analyzed. The cumulative hazard ratio for PFS was 0.72 (95% CI = 0.65 to 0.79), and the cumulative hazard ratio for OS was 0.90 (95% CI = 0.80 to 1.01). The regression model showed a statistically nonsignificant association between PFS benefit and OS benefit (β = 0.43, SE = 0.81). CONCLUSION: The US Food and Drug Administration's decision-making in this case was consistent with the evolving state of evidence. However, the fact that seven clinical trials are insufficient to conclude validity (or lack thereof) for a trial-level surrogate suggests that it would be more efficient to conduct trials using the more clinically meaningful endpoints.
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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 | 0.048 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".