A random set approach to confidence regions with applications to the effective dose with combinations of agents
Why this work is in the frame
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Bibliographic record
Abstract
The effective dose (ED) is the pharmaceutical dosage required to produce a therapeutic response in a fixed proportion of the patients. When only one drug is considered, the problem is a univariate one and has been well-studied. However, in the multidimensional setting, that is, in the presence of combinations of agents, estimation of the ED becomes more difficult. This study is focused on the plug-in logistic regression estimator of the multidimensional ED. We discuss consistency of such estimators and focus on the problem of simultaneous confidence regions. We develop a bootstrap algorithm to estimate confidence regions for the multidimensional ED. Through simulation, we show that the proposed method gives 95% confidence regions, which have better empirical coverage than the previous method for moderate to large sample sizes. The novel approach is illustrated on a cytotoxicity study on the effect of two toxins in the leukemia cell line HL-60 and a decompression sickness study of the effects of the duration and depth of the dive.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it