qTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities
Bibliographic record
Abstract
The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability ([Formula: see text]) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to [Formula: see text] according to a severity weight matrix. Dose–toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design.
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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 | 0.070 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".