A model to select regimens for phase III trials for patients with advanced-stage non-small cell lung cancer.
Bibliographic record
Abstract
PURPOSE: Historical data from pilot, Phase II, and Phase III studies for patients with advanced-stage non-small cell lung cancer (NSCLC) were used to evaluate a statistical model developed to provide assistance in selecting regimens from pilot studies for subsequent use in larger Phase III randomized studies. EXPERIMENTAL DESIGN: Information from 33 Phase III trials for patients with advanced-stage NSCLC performed from 1973 and 1994 in the United States and Canada was collected. The data from antecedent pilot or Phase II and subsequent Phase III trials were analyzed using a predictive statistical model. This model uses the number of patients in the pilot/Phase II study, the median survival of patients in the pilot, and the number of deaths observed, to estimate the statistical likelihood that the pilot regimen will be shown superior to standard therapy in a subsequent Phase III trial. RESULTS: Ten pilot/Phase II studies were identified that preceded eleven subsequent Phase III studies. The three pilot regimens associated with Phase III trials, revealing statistically significant longer survival, had an expected power of 0.69, 0.85, and 0.94 respectively. The regimens from the seven other pilot studies for which the median power expected was 0.38 (range, 0.07-0.80) showed no difference when compared with standard treatment in a Phase III trial. CONCLUSION: The use of the expected power model provides an important enhancement to the screening of new therapies. Regimens with an expected power of >0.55 may be good candidates for testing in Phase III trials.
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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 | 0.006 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".