Decision Criteria During Joint Modelling of Efficacy and Safety With MCP-Mod
Bibliographic record
Abstract
MCP‐Mod has been established as analysis method for investigating the dose‐response (DR) relationship and dose finding in clinical Phase II trials. While most work on MCP-Mod focusses on the efficacy DR relationship, in 2015, Tao, Lin, Pinheiro and Shih (2015) extended MCP-Mod to the joint modelling of efficacy and safety endpoints in “Dose Finding Method in Joint Modeling of Efficacy and Safety Endpoints in Phase II Studies”. Their proposed algorithm defines several decision criteria, which majorly impact results or even terminate the algorithm. This viewpoint investigates the robustness of two of these decision criteria. While the criterion on the relationship between the maximum safety dose and minimum effective dose is reasonable and robust, there exist some advantages applying a more generous criterion to establish proof of concept for safety. Increasing the proposed significance level in establishing proof of concept for the safety DR relationship, helps to identify non-flat safety DR relationships which ultimately improves final estimation of the optimal dose.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".