Phase II Stopping Rules That Employ Response Rates and Early Progression
Bibliographic record
Abstract
PURPOSE: Phase II oncology trials traditionally have used response rate (RR) as the primary end point, but newer targeted agents require the consideration of alternative end points. High rates of early progressive disease (EPD) suggest inadequate drug activity and may be useful in the early stopping of trials. This study used a simulation to define a set of rules to assess a combined end point of RR and EPD. METHODS: The simulation assumed a two-stage trial with a specified alpha error and power. It randomly generated the true response rate, r, of the agent under study and its true rate of early progressive disease, epd, for each run of the simulation. Two pairs of parameters were specified: (r(nul), epd(nul)) and (r(alt), epd(alt)). A drug was considered uninteresting for further development if r was less than or equal to r(nul) and epd was greater than or equal to epd(nul) (ie, the null hypothesis) and interesting for further development if r was greater than or equal to r(alt) or epd was less than or equal to epd(alt) (ie, the alternate hypotheses). Thresholds for the required number of patients with responses, n(r) and EPD, n(p), were generated for each set of parameters. RESULTS: Thresholds for n(r) and n(p) that satisfied the specified error rates were generated. There was at least an 89% likelihood that a study would be stopped at the first stage of accrual if r and epd were uninteresting. CONCLUSION: The simulation was able to establish stopping rules by combining the RR and the EPD that achieved the desired error rates. High rates of early stopping suggest that this design could shorten phase II trials of inactive 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 | 0.057 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".