Practical Model-Based Dose Finding in Early-Phase Clinical Trials
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A safe and effective tissue plasminogen activator (tPA) dose for childhood stroke has not been established. This article describes a Bayesian outcome-adaptive method for determining the best dose of an experimental agent and explains how this method was used to design a dose-finding trial for tPA in childhood. METHODS: The method assigns doses to successive cohorts of patients on the basis of each dose's desirability, quantified in terms of the tradeoff between efficacy and toxicity. The tradeoff function is constructed from several pairs of equally desirable (efficacy, toxicity) probabilities specified by the physicians planning the trial. Each cohort's dose is chosen adaptively, based on dose-outcome data from the patients treated previously in the trial, to optimize the efficacy-toxicity tradeoff. Application of the method to design the tPA trial is described, including a computer simulation study to establish design properties. A hypothetical cohort-by-cohort example is given to illustrate how the method works during trial conduct. RESULTS: Because only a dose that is both safe and efficacious may be selected and the method combines phase I and phase II by integrating efficacy and toxicity to choose doses, it avoids the more time-consuming and expensive conventional approach of conducting a phase I trial based on toxicity alone followed by a phase II trial based on efficacy alone. This is especially useful in settings with low accrual rates, such as trials of tPA for pediatric acute ischemic stroke.
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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 | 0.061 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".