A Generalized Phase I/II Dose Optimization Trial Design With Multi‐Categorical and Multi‐Graded Outcomes
Bibliographic record
Abstract
Pursuing accurate observations and rational assumptions always drives advances in clinical trial design. In recent years, more trials have begun to collect multi-graded outcomes for more informative analyses. At the same time, assumptions other than the traditional monotonicity relationship have been considered in the dose-efficacy curve to be more realistic. Inspired by these two trends, we propose a phase I/II design that simultaneously considers multi-categorical toxicity and efficacy with multi-graded outcomes, measured as quasi-continuous probability based on prespecified weight matrices of clinical significance. Following keyboard design, our approach aims to screen out overly toxic doses by the toxicity probability intervals and adaptively makes dose escalation or de-escalation decisions by comparing the posterior distributions of dose desirability (utility) among the adjacent levels of the current dose. It helps to more accurately identify the OBD in a non-monotonically increasing dose-efficacy relationship. We also comprehensively present the safety, accuracy and reliability performance through numerical simulations in multiple scenarios and compare the results with several already available designs. The benchmarking results of multiple operating characteristics convincingly support that our design leads in overall performance while ensuring robustness.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".