A comprehensive comparison of the continual reassessment method to the standard 3 + 3 dose escalation scheme in Phase I dose-finding studies
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: An extensive literature has covered the statistical properties of the Continual Reassessment Method (CRM) and the modifications of this method. While there are some applications of CRM designs in recent Phase I trials, the standard method (SM) of escalating doses after three patients with an option for an additional three patients SM remains very popular, mainly due to its simplicity. From a practical perspective, clinicians are interested in designs that can estimate the MTD using fewer patients for a fixed number of doses, or can test more dose levels for a given sample size. PURPOSE: This article compares CRM-based methods with the SM in terms of the number of patients needed to reach the MTD, total sample size required, and trial duration. METHODS: The comparisons are performed under two alternative schemes: a fixed or a varying sample approach with the implementation of a stopping rule. The stopping rule halts the trial if the confidence interval around the MTD is within a pre-specified bound. Our simulations evaluated several CRM-based methods under different scenarios by varying the number of dose levels from five to eight and the location of the true MTD. RESULTS: CRM and SM are comparable in terms of how fast they reach the MTD and the total sample size required when testing a limited number of dose levels (<or=5), but as the number of dose levels increases, CRM reaches the MTD in fewer patients when used with a fixed sample of 20 patients. However, a sample size of 20-25 patients is not sufficient to achieve a narrow precision around the estimated toxicity rate at the MTD. LIMITATIONS: We focused on methods with practical design features that are of interest to clinicians. However, there are several alternative CRM-based designs that are not investigated in this manuscript, and hence our results are not generalizable to other designs. CONCLUSIONS: We show that CRM-based methods are an improvement over the SM in terms of accuracy and optimal dose allocation in almost all cases, except when the true dose is among the lower levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.587 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it