Identification of Maximum Safe Dose Based on Ratio of Normally Distributed Data under Heteroscedasticity
Bibliographic record
Abstract
In most of the various stepwise confidence interval procedures formulated for identifying maximum safe dose (MSD), homogeneity of variances among different dose levels were required. But in practice, homogeneity of variance is often in doubt. This paper proposes a stepwise confidence set procedure for identifying MSD of drugs based on ratio of population means for normally distributed data under heteroscedasticity without the need for multiplicity adjustment. The procedure employed Fieller's method and obtained individual (1-α)100% confidence intervals for identification of the MSD. We illustrate the procedure with a real life example. In addition, we show that power of the procedure increases with increasing ratio of means, and sample size. Power however decreases with increase in clinical relevance margins. We also illustrate that the new procedure can properly control familywise error rate (FWER).
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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 | 0.037 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".