Dose–schedule finding in phase I/II clinical trials using a Bayesian isotonic transformation

Authors

  • Yisheng Li,

    Corresponding author
    1. Department of Biostatistics, Division of Quantitative Sciences, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 447, Houston, TX 77030, U.S.A.
    • Department of Biostatistics, Division of Quantitative Sciences, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 447, Houston, TX 77030, U.S.A.
    Search for more papers by this author
  • B. Nebiyou Bekele,

    1. Department of Biostatistics, Division of Quantitative Sciences, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 447, Houston, TX 77030, U.S.A.
    Search for more papers by this author
  • Yuan Ji,

    1. Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 447, Houston, TX 77030, U.S.A.
    Search for more papers by this author
  • John D. Cook

    1. Division of Quantitative Sciences, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 447, Houston, TX 77030, U.S.A.
    Search for more papers by this author

Abstract

A dose–schedule-finding trial is a new type of oncology trial in which investigators aim to find a combination of dose and treatment schedule that has a large probability of efficacy yet a relatively small probability of toxicity. We demonstrate that a major difference between traditional dose-finding and dose–schedule-finding trials is that while the toxicity probabilities follow a simple nondecreasing order in dose-finding trials, those of dose–schedule-finding trials may adhere to a matrix order. We show that the success of a dose–schedule-finding method requires careful statistical modeling and a sensible dose–schedule allocation scheme. We propose a Bayesian hierarchical model that jointly models the unordered probabilities of toxicity and efficacy and apply a Bayesian isotonic transformation to the posterior samples of the toxicity probabilities, so that the transformed posterior samples adhere to the matrix-order constraints. On the basis of the joint posterior distribution of the order-constrained toxicity probabilities and the unordered efficacy probabilities, we develop a dose–schedule-finding algorithm that sequentially allocates patients to the best dose–schedule combination under certain criteria. We illustrate our methodology through its application to a clinical trial in leukemia and compare it with two alternative approaches. Copyright © 2008 John Wiley & Sons, Ltd.

Ancillary