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Optimal Bayesian-feasible dose escalation for cancer phase I trials

Author

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  • Zacks, S.
  • Rogatko, A.
  • Babb, J.

Abstract

We present an adaptive dose escalation scheme for cancer phase I clinical trials which is based on a parametric quantal response model. The dose escalation is Bayesian-feasible, Bayesian-optimal and consistent. It is designed to approach the maximum tolerated dose as fast as possible subject to the constraint that the predicted probability of assigning doses higher than the maximum tolerated dose is equal to a specified value.

Suggested Citation

  • Zacks, S. & Rogatko, A. & Babb, J., 1998. "Optimal Bayesian-feasible dose escalation for cancer phase I trials," Statistics & Probability Letters, Elsevier, vol. 38(3), pages 215-220, June.
    • Handle: RePEc:eee:stapro:v:38:y:1998:i:3:p:215-220
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    References listed on IDEAS

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    1. Robert K. Tsutakawa, 1980. "Selection of Dose Levels for Estimating a Percentage Point of a Logistic Quantal Response Curve," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 25-33, March.
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    Cited by:

    1. Jay Bartroff & Tze Leung Lai, 2011. "Incorporating Individual and Collective Ethics into Phase I Cancer Trial Designs," Biometrics, The International Biometric Society, vol. 67(2), pages 596-603, June.
    2. Mourad Tighiouart & Yuan Liu & André Rogatko, 2014. "Escalation with Overdose Control Using Time to Toxicity for Cancer Phase I Clinical Trials," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
    3. Graham M. Wheeler, 2018. "Incoherent dose-escalation in phase I trials using the escalation with overdose control approach," Statistical Papers, Springer, vol. 59(2), pages 801-811, June.
    4. Oron Assaf P. & Azriel David & Hoff Peter D., 2011. "Dose-Finding Designs: The Role of Convergence Properties," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-17, October.

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