Factors associated with change in the magnitude of clinical benefit of anti-cancer drugs in the post-marketing period.
Bibliographic record
Abstract
7052 Background: Initial drug approval is often based on surrogate endpoints. Definitive outcomes like Overall Survival (OS) or Quality of life (QoL) may not be available. Here, we evaluate changes in the magnitude of clinical benefit using the American Society of Clinical Oncology Value Framework (ASCO-VF) and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) comparing the time of approval to the most recent available data for cancer drugs approved by the US Food and Drug Administration (FDA) between 2006 and 2015. Methods: We examined data on trials supporting FDA accelerated (AA) and regular (RA) cancer drug approvals between January 2006 and December 2015. We performed a systematic search of Pubmed and ClinicalTrials.gov to identify updated OS and/or QoL data, with follow up through April 2019. For AA drugs we analysed initial and confirmatory trials as follow-up. ASCO-VF and ESMO-MCBS grades were applied for trials at approval and after marketing. We explored variables associated with improved clinical benefit scores using multivariable logistic regression. Results: We identified 102 trials supporting the approval of 59 drugs for 96 solid tumour indications. Of these indications, 22 (23%) were granted AA and 21 (95%) were converted to RA. At time of approval, 38% of trials showed improved OS and 17% improved QoL. Substantial clinical benefit was observed in 26% of initial approval trials using ESMO-MCSB and in 34% using ASCO-VF. After a median post-marketing period of 3.3 years, updated results changed substantial clinical benefit in 20 trials with ESMO-MCBS (19 upgrades, 1 downgrade) and in 23 trials using ASCO-VF (19 upgrades, 4 downgrades). For 25% of trials no updated information was found. In the palliative setting, multivariable analysis showed association between improved ASCO-VF scores and initial approvals based on single-arm trials (OR 9.21, 95%CI 1.36-62.29, P=0.023), drugs with companion diagnostics (OR 4.95, 95%CI 1.01-24.22, P=0.049) and second or later lines (OR 7.80, 95%CI 1.35-45.02, P=0.022) while for ESMO-MCBS, drugs with companion diagnostics (OR 6.86, 95%CI 1.82-25.86, P=0.004) and immunotherapy drugs (OR 6.42, 95%CI 1.27-32.59, P=0.025) were associated with greater clinical benefit. Conclusions: Drugs with companion diagnostic tests, immunotherapy as well as approved based on single-arm trials were associated with increased clinical benefit after marketing approval. For a quarter of trials there were no updated data in the post-marketing period.
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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 | 0.027 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".