Proportion of patients in phase 2 oncology trials receiving treatments that are ultimately approved
Bibliographic record
Abstract
BACKGROUND: Many patients enroll in phase 1 dose expansion cohorts or phase 2 clinical trials (together referred to below as "phase 2") seeking access to novel treatments. Little is known about the extent to which they benefit by enrolling. Herein, we use a novel metric of benefit-therapeutic proportion-to assess the probability that patients in phase 2 trials receive treatment that eventually advances to FDA (Food and Drug Administration) approval for their condition. METHODS: We randomly sampled 400 trials identified in a search of Clinicaltrials.gov for cancer phase 2 trials initiated between November 1, 2012 and November 1, 2015. We determined whether the drug/dose/indication tested in each trial advanced to FDA approval within 7.5 years. We determined whether the drug/dose/indication presented substantial clinical benefit using the ESMO-MCBS (European Society for Medical Oncology - Magnitude of Clinical Benefit Scale), or whether it received off-label recommendation in NCCN (National Comprehensive Cancer Network) guidelines. RESULTS: Collectively, trials in our sample enrolled 25 002 patient-participants in 608 specific treatment cohorts. A total of 4045 patients received a treatment that advanced to FDA approval (16.2%; 95% CI = 10.3 to 22.7). The therapeutic proportion increased to 19.4% (95% CI = 14.1 to 25.8) when considering NCCN off-label recommendations and decreased to 9.3% (95% CI = 4.7 to 14.6) for FDA-approved regimens considered being of substantial clinical benefit by ESMO-MCBS. Bootstrap test of mean difference showed no statistical difference in proportions based on drug class, trial phase, or sponsorship. CONCLUSION: One in 6 patients in phase 2 clinical trials receives treatments that are eventually approved. This represents a higher therapeutic value than phase 1 trials.
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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 | 0.047 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".