Saddlepoint approximations for rank‐invariant permutation tests and confidence intervals with interval‐censoring
Bibliographic record
Abstract
Abstract Interval‐censored data occur when subjects are assessed by using regular follow‐up. In such instances, we consider rank‐invariant permutation tests to test the significance of a treatment versus a control. For a wide class of such tests, which includes the Peto & Peto class, we present saddlepoint approximations for the exact permutation mid‐ P ‐values which achieve extremely small relative errors. The speed and stability of these saddlepoint computations make them practicable for inverting the permutation tests and we compute nominal confidence intervals for the treatment effect. Such confidence intervals are of substantial clinical importance since, more than simply stating the level of statistical significance, they quantify the significant benefit of the treatment by providing a confidence interval for the percentage increase in mean (or median) treatment survival time as compared to control. Our methodology makes heavy use of nonparametric MLEs (NPMLEs) for survival functions and some limitations of existing algorithms, such as the hybrid ICM algorithm, are noted and accommodated. The Canadian Journal of Statistics 42: 308–324; 2014 © 2014 Statistical Society of Canada
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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 | 0.003 | 0.101 |
| 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".