Application of a New Multinomial Phase II Stopping Rule Using Response and Early Progression
Bibliographic record
Abstract
PURPOSE: A multinomial stopping rule had previously been developed that incorporated both objective response and early progression into decisions to stop or continue phase II trials of anticancer agents. The purpose of this study was to apply the multinomial rule to two independent sets of phase II data to assess its utility in appropriately recommending early trial closure as compared with other stopping rules. MATERIALS AND METHODS: Data from completed phase II trials of the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) and European Organization for Research and Treatment of Cancer Early Clinical Studies Group (ECSG) formed the basis of the study. Based on observed results for each trial, the recommendation of the multinomial stopping rule was applied, as was the recommendation of the actual stopping rule used (Fleming or Gehan). The appropriateness of the recommendations was evaluated based on interpretation of final study results. RESULTS: The standard and multinomial rules disagreed on early stopping in one of 16 NCIC CTG trials and in seven of 23 ECSG trials. In all cases, the standard rule advised continuing to the second stage whereas the multinomial rule advised stopping early because of excessive numbers of patients experiencing early disease progression. Final trial results indicated that the multinomial recommendation was appropriate, because in no study did final results lead to conclusions of activity. CONCLUSION: In this series of trials, the multinomial stopping rule performed more efficiently than the Fleming or Gehan rules in advising early stopping of trials. These results encourage continued exploration of this approach for phase II trials of cytotoxic and noncytotoxic anticancer agents.
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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 | 0.186 | 0.403 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".