Associations With Definitive Outcomes and Clinical Benefit of Cancer Drugs at the Time of Marketing Approval and in the Postmarketing Period
Bibliographic record
Abstract
BACKGROUND: Most anticancer drugs are approved by regulatory agencies based on surrogate measures. This article explores the variables associated with overall survival (OS), quality of life (QoL), and substantial clinical benefit among anticancer drugs at the time of approval and in the postmarketing period. METHODS: Anticancer drugs approved by the FDA between January 2006 and December 2015 and with postmarketing follow-up until April 2019 were identified. We evaluated trial-level data supporting approval and any updated OS and/or QoL data. We applied the ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) and the ASCO Value Framework (ASCO-VF) to initial and follow-up studies. RESULTS: We found that 58 drugs were approved for 96 indications based on 96 trials. At registration, approval was based on improved OS in 39 trials (41%) and improved QoL in 16 of 45 indications (36%). Postmarketing data showed an improvement in OS for 28 of 59 trials (47%) and in QoL for 22 of 48 indications (46%). At the time of approval, 25 of 94 (27%) and 26 of 80 scorable trials (33%) met substantial benefit thresholds using the ESMO-MCBS and ASCO-VF, respectively. In the postmarketing period, 37 of 69 (54%) and 35 of 65 (54%) trials met the substantial benefit thresholds. Drugs with companion diagnostics and immune checkpoint inhibitors were associated significantly with substantial clinical benefit. CONCLUSIONS: Compared with the time of approval, more anticancer drugs showed improved OS and QoL and met the ESMO-MCBS or ASCO-VF thresholds for substantial benefit over the course of postmarketing time. However, only approximately half of the trials met the threshold for substantial benefit. Companion diagnostic drugs and immunotherapy seemed to be associated with greater clinical 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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".