Phase II stopping rules employing response rates and early progression
Bibliographic record
Abstract
2553 Background: Drug development in oncology has become increasingly resource intensive. Anything that might accelerate drug development would benefit patients and drug developers. Traditionally, tumour response rates (RR) have been used to assess the efficacy of new agents in phase II trials. High rates of early progression of disease (EPD) may also indicate lack of drug efficacy, and this endpoint has the potential to shorten trials compared to assessing RR alone. This work seeks to create a set of rules that will allow phase II drug assessment employing both RR and EPD. Previous work on this subject suffered from insufficient power, as determined by the authors [Freidlin et al, J Clin Oncol, 20:599, 2002]. Methods: Using TreeAge Pro Healthcare software, we created a computer model that would accept specified trial parameters and determine through simulations stopping rules based on observed number of responses and EPDs to achieve the desired power and alpha error for a single-armed two-stage study with 15 patients in each stage. The null hypothesis (H(nul)) specified the response rate (rr(nul)) and early progressive disease rate (epd(nul)) that would render a drug uninteresting for further development, such that: H(nul): rr LE rr(nul) & epd GE epd(nul), where rr is that actual response rate observed and epd is the actual rate of early progression. Similarly, the alternate hypothesis (H(alt)) specified the response rate (rr(alt)) and early progressive disease rate (epd(alt)) that would render a drug interesting for further development, such that: H(alt): rr GE rr(alt) or epd LE epd(alt). (LE, less than, equal to; GE, greater than, equal to) Results: Conclusions: The simulation was able to establish stopping rules for observed number of responses and EPDs to achieve the desired error rates. Variations on the rules based on other trial sample types and design parameters will be detailed. [Table: see text] No significant financial relationships to disclose.
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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 | 0.038 | 0.083 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".