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Practical model-based dose-finding in phase I clinical trials: Methods based on toxicity
  1. P. F. THALL and
  2. S.-J. LEE
  1. Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, Texas
  1. Address correspondence and reprint requests to: P.F. Thall, Department of Biostatistics, Box 447, University of Texas, M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030 USA, E-mail: rex{at}


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.

  • adaptive decision making
  • Bayesian inference
  • clinical trial
  • dose-finding
  • phase I
  • safety monitoring
  • toxicity

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