Magnitude of Clinical Benefit of Cancer Drugs Approved by the US Food and Drug Administration
Bibliographic record
Abstract
Background: It is uncertain whether drugs approved by the US Food and Drug Administration (FDA) have clinically meaningful benefit as determined by validated scales such as the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). Methods: We searched the Drugs@FDA website for applications of anticancer drugs from January 2006 to December 2016. Study characteristics, outcomes, and regulatory pathways were collected from drug labels and reports of registration trials. For randomized controlled trials (RCTs), ESMO-MCBS grades were applied. Meaningful benefit was defined as a grade of A or B for (neo)adjuvant intent and 4 or 5 for palliative intent. All statistical tests were two-sided. Results: We identified 63 individual drugs for 118 indications. These were supported by 135 studies, among which were 105 RCTs for which ESMO-MCBS could be applied. Only 46 (43.8%) met the ESMO-MCBS meaningful benefit threshold (100% of (neo)adjuvant trials and 38.8% of palliative trials). In palliative therapy trials, meaningful ESMO-MCBS grades were associated with phase III trials (compared with phase II; odds ratio [OR] = 38.45, 95% confidence interval [CI] = 3.27 to 452.00, P = .004), those with overall survival as their primary end point (compared with intermediate end points; OR = 8.28, 95% CI = 2.49 to 27.50, P = .001) and trials of targeted drugs with companion diagnostics (OR = 11.62, 95% CI = 2.95 to 45.78, P < .001). Over time, there has been an increase in the number of trials meeting the ESMO-MCBS threshold (Ptrend = .04). There were insufficient (neo)adjuvant studies to perform statistical analysis. Conclusions: The number of trials meeting the ESMO-MCBS threshold for clinical benefit has improved over time. However, fewer than half of RCTs supporting FDA approval meet the threshold for clinically meaningful benefit.
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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 | 0.071 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".