Reassessing the net health benefit (NHB) of FDA approved cancer drugs with evolution of evidence using the American Society of Clinical Oncology Value Framework (ASCO-VF).
Bibliographic record
Abstract
7011 Background: Regulatory approval of oncology drugs are often based on data presented in the primary publication of clinical trials (CT). However, clinically relevant data, such as long-term overall survival (OS) and quality of life (QOL), are often reported in subsequent publications. Therefore, this study aimed to evaluate the ASCO-VF NHB at the time of drug approval and over time as further evidence is published. Methods: All FDA approved oncology drug indications from 01/06-12/16 were reviewed to identify CTs that were scorable using the ASCO-VF version 2. Subsequent publications of included CTs relevant for scoring were identified from Web of Science with a follow-up time of 3 years from approval. Using ASCO-defined threshold scores of ≤40 for low benefit and ≥45 for substantial benefit, changes in classification of benefit were assessed at 3-years post-FDA approval. Results: We identified 57 FDA approved indications (40.4% OS, 59.6% progression-free survival (PFS) as primary endpoints) with scorable ASCO-VF CTs. Among those 57 indications, 36.8% at the time of FDA approval demonstrated substantial benefit, 10.5% demonstrated intermediate benefit, and 52.6% demonstrated low benefit. We then identified 96 subsequent publications relevant to scoring within 3-years of FDA approval, consisting of primary endpoint updates (29.2%; 14.6% OS, 12.5% PFS), secondary endpoint updates (44.8%; 16.7% OS, 7.3% PFS), new reporting of secondary endpoint (4.2% OS), safety updates (28.1%), and QOL reporting (43.8%). Upon reassessment of the NHB in subsequent publications, there was an overall change from initial classification of benefit in 36.8% of trials (17.5% became substantial, 8.8% became low, and 10.5% became intermediate). Changes in scores were mainly the result of an updated hazard ratio (35.1%), change in scoring endpoints from PFS to OS as per ASCO-VF endpoint hierarchy (8.8%), toxicity updates (57.9%), new tail of the curve bonus (12.3%), palliation bonus (14.0%), or QOL bonus (22.8%). Overall, at reassessment at 3 years post-FDA approval, 42.1% were substantial, 10.5% were intermediate, and 47.3% were low benefit. Conclusions: Only a modest proportion of FDA approved drugs have demonstrated substantial NHB at time of approval. As further evidence was published, a substantial proportion of indications have a change in classification of NHB, resulting in a small increase in the overall proportion of indications being deemed to have substantial benefit at 3 years post-approval.
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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 | 0.082 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".