Stopping rules employing response rates, time to progression, and early progressive disease for phase II oncology trials
Bibliographic record
Abstract
BACKGROUND: Response rate (RR), the most common early means of assessing oncology drugs, is not suitable as the sole endpoint for phase II trials of drugs which induce disease stability but not regression. Time to progression (TTP) may be more sensitive to such agents, but induces recruitment delays in multistage studies. Early progressive disease (EPD) is the earliest signal of time to progression, but is less intuitive to investigators, To study drugs with unknown anti-tumour effect, we designed the Combination Stopping Rule (CSR), which allows investigators to establish a hypothesis using RR and TTP, while the program also employs early progressive disease (EPD) to assess for drug inactivity during the first stage of study accrual. METHODS: A computer program was created to generate stopping rules based on specified error rates, trial size, and RR and median TTP of interest and disinterest for a two-stage phase II trial. Rules were generated for stage II such that the null hypothesis (H(nul)) was rejected if either RR or TTP met desired thresholds, and accepted if both did not. Assuming an exponential distribution for progression, EPD thresholds were determined based on specified TTP values. Stopping rules were generated for stage I such that Hnul was accepted and the study stopped if both RR and EPD were unacceptable. RESULTS: Patient thresholds were generated for RR, median TTP, and EPD which achieved specified error rates and which allowed early stopping based on RR and EPD. For smaller proportional differences between interesting and disinteresting values of RR or TTP, larger trials are required to maintain alpha error, and early stopping is more common with a larger first stage. CONCLUSION: Stopping rules are provided for phase II trials for drugs which have either a desirable RR or TTP. In addition, early stopping can be achieved using RR and EPD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.353 | 0.979 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".