Abstract
We describe two practical, outcome-adaptive statistical methods for dose-finding in phase I clinical trials. One is the continual reassessment method and the other is based on a logistic regression model. Both methods use Bayesian probability models as a basis for learning from the accruing data during the trial, choosing doses for successive patient cohorts, and selecting a maximum tolerable dose (MTD). These methods are illustrated and compared to the conventional 3 + 3 algorithm by application to a particular trial in renal cell carcinoma. We also compare their average behavior by computer simulation under each of several hypothetical dose-toxicity curves. The comparisons show that the Bayesian methods are much more reliable than the conventional algorithm for selecting an MTD, and that they have a low risk of treating patients at unacceptably toxic doses.
Original language | English (US) |
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Pages (from-to) | 251-261 |
Number of pages | 11 |
Journal | International Journal of Gynecological Cancer |
Volume | 13 |
Issue number | 3 |
DOIs | |
State | Published - May 2003 |
Keywords
- Adaptive decision making
- Bayesian inference
- Clinical trial
- Dose-finding
- Phase I
- Safety monitoring
- Toxicity
ASJC Scopus subject areas
- Oncology
- Obstetrics and Gynecology