Multiple Test Procedures for Identifying the Maximum Safe Dose
Bibliographic record
Abstract
We consider dose response studies for safety assessment of crop protection compounds and drugs, and we offer a hypothesis testing approach for identifying the maximum dose level that is guaranteed to be safe with preassigned confidence. The focus is on step-down (SD) multiple test procedures for identifying the maximum safe dose. We propose two classes of contrasts among the dose means as test statistics for these procedures: pairwise contrasts (PC) and Helmert contrasts (HC). The first procedure (SD2PC) consists of a sequence of ordinary t tests and is thus easy to apply, but it can have very low power for certain step response functions. The second procedure (SD1HC) does not suffer from this drawback, but it requires a weak monotonicity assumption for its mathematical validity. The powers of SD2PC and SD1HC are studied via Monte Carlo simulation. We recommend SD2PC for linear response functions and SD1HC for step response functions. The procedures are illustrated by applying them to data from an aquatic toxicity laboratory experiment conducted to assess the safe level of a pesticide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.271 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".