Use of Expansion Cohorts in Phase I Trials and Probability of Success in Phase II for 381 Anticancer Drugs
Bibliographic record
Abstract
Abstract Purpose: Evaluate the association between the use of phase I expansion cohorts (ECs) and drug performance in phase II as well as time to approval by the FDA. Experimental Design: We performed a systematic search of MEDLINE for single-agent dose-finding adult oncology phase I trials published in 2006 to 2011 and subsequent phase II trials. Successful phase II trials were those that met their primary endpoints. Dates of approval were obtained from the Drugs@FDA website in April 2014. A logistic regression model was used to determine the associations between variables and success in phase II. Results: We identified 533 phase I trials evaluating 381 drugs; 112 drugs had at least one phase I trial with an expansion cohort. Phase I trials with expansion cohorts of two to 20 patients were associated with a higher rate of successful phase II trials than those with no expansion cohort [48% vs. 27%; OR, 2.1; 95% confidence interval (CI), 1.1–4.0; P = 0.037]. Phase II success rates were the same for expansion cohort with two to 20 and more than 20 patients (48% vs. 52%). Other positive associations were disease-specific trials (OR, 1.7; 95% CI, 1.0–2.9; P = 0.037), industry sponsorship (OR, 2.9; 95% CI, 1.5–5.7; P = 0.0024), and response rate of 6% to 20% (OR, 2.89; 95% CI, 1.6–5.2; P = 0.0007). Drugs tested in phase I trials with expansion cohorts had a higher rate of 5-year approval (19% vs. 5%; HR, 4.4; 95% CI, 2.2–8.8; P < 0.001). Conclusions: The use of expansion cohorts in phase I trials was associated with success of subsequent phase II trials. However, confounders may play a role in this association. Clin Cancer Res; 23(15); 4020–6. ©2017 AACR.
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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 | 0.105 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".