Benefit, burden, and impact for a cohort of post-approval cancer combination trials
Bibliographic record
Abstract
BACKGROUND: After approval, drug developers often pursue trials aimed at extending the uses of a new drug by combining it with other drugs. Little is known about the risk and benefits associated with such research. METHODS: To establish a historic benchmark of risk and benefit, we searched Medline and Embase for clinical trials testing anti-cancer drugs in combination within 5 years of approval by the Food and Drug Administration of 12 anti-cancer "index" drugs first licensed 2005-2007 inclusive. Risk was assessed based on grade 3 or above drug-related adverse events; benefit was assessed based on efficacy outcomes and advancement of combinations into clinical practice guidelines or approval by the Food and Drug Administration. RESULTS: We captured 323 published post-approval trials exploring combinations, including 266 unique combination-indication pairings and enrolling 29,835 patients. The pooled risk ratios for treatment-related grade 3-4 severe adverse events and deaths attributed to the study drugs for trials randomized between a combination arm and a comparator were 1.54 (1.33-1.79) and 1.51 (1.16-1.97), respectively. The pooled hazard ratios for overall survival and progression-free survival were 0.99 (0.92-1.05) and 0.85 (0.79-0.93), respectively. None of the combination-indication pairings launched after initial drug approval received approval by the Food and Drug Administration, and 13 pairings (4.9%) were recommended by the National Comprehensive Cancer Network within 5 years of the first trial within that pairing. The proportion of patients in our sample who participated in trials leading to an approval by the Food and Drug Administration or a National Comprehensive Cancer Network guideline recommendation was 12.7% with 5 years of follow-up, and 22.3% among pairings for which there were 8 years of follow-up. CONCLUSION: Patients were just as likely to benefit in the treatment arm as the control arm in terms of overall survival, but they were more likely to experience a treatment-related severe adverse event in post-approval trials of combination therapy.
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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 | 0.232 | 0.402 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".