Practical model-based dose-finding in phase I clinical trials: methods based on toxicity.
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.
Thall
P F
PF
Department of Biostatistics, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030, USA. rex@mdanderson.org
Lee
S-J
SJ
D000465:Algorithms; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D020714:Maximum Tolerated Dose; D015233:Models, Statistical; D018401:Sample Size; D018675:Toxicity Tests / Q000379:methods*