Real-world outcomes associated with new cancer medicines approved by the Food and Drug Administration and European Medicines Agency: A retrospective cohort study
Bibliographic record
Abstract
PURPOSE: Real-World Data (RWD) studies are increasingly used to support regulatory approvals, reimbursement decisions, and changes in clinical practice for novel cancer drugs. However, few studies have systematically appraised their quality or compared outcomes to pivotal trials. METHODS: All RWD studies (2010-2019) for drugs approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA) from 2010 to 2015 for solid organ tumours in the non-curative setting were identified. Quality assessment was undertaken using the Newcastle Ottawa Scale. Survival differences between each RWD study and the pivotal trial were determined using a related sample Wilcoxon signed-rank test. RESULTS: 293 RWD studies for 45 of the 57 drug indications approved by the FDA/EMA were identified. The most common tumour types were prostate cancer (29%, n = 86) and melanoma (15%, n = 43). A quarter of the studies had industry funding. No high-quality studies were identified, and 78% were low quality. Comparative survival analysis between RWD and pivotal trials was possible for 224 studies (37 drug indications). Differences in median survival between the RWD studies and their corresponding trial ranged from -32 months to 21 months (IQR -4·2 months to 1·6 months). Low-quality studies were more likely to report superior survival outcomes (23%) compared to higher quality studies (8%) (p = 0.02). CONCLUSION: RWD study quality for novel cancer drugs is low and of insufficient rigour to inform reimbursement decisions and clinical practice. RWD studies seeking publication should provide a completed quality assessment tool on submission. Greater investment in properly designed RWD studies is required.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".