A default method to specify skeletons for Bayesian model averaging continual reassessment method for phase I clinical trials

The Bayesian model averaging continual reassessment method (CRM) is a Bayesian dose-finding design. It improves the robustness and overall performance of the continual reassessment method (CRM) by specifying multiple skeletons (or models) and then using Bayesian model averaging to automatically favor the best-fitting model for better decision making. Specifying multiple skeletons, however, can be challenging for practitioners. In this paper, we propose a default way to specify skeletons for the Bayesian model averaging CRM. We show that skeletons that appear rather different may actually lead to equivalent models. Motivated by this, we define a nonequivalence measure to index the difference among skeletons. Using this measure, we extend the model calibration method of Lee and Cheung (2009) to choose the optimal skeletons that maximize the average percentage of correct selection of the maximum tolerated dose and ensure sufficient nonequivalence among the skeletons. Our simulation study shows that the proposed method has desirable operating characteristics. We provide software to implement the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  J O'Quigley,et al.  Posterior maximization and averaging for Bayesian working model choice in the continual reassessment method , 2011, Statistics in medicine.

[2]  S Zacks,et al.  Cancer phase I clinical trials: efficient dose escalation with overdose control. , 1998, Statistics in medicine.

[3]  J O'Quigley,et al.  Continual reassessment method: a practical design for phase 1 clinical trials in cancer. , 1990, Biometrics.

[4]  Shing M. Lee,et al.  Characterization of the likelihood continual reassessment method , 2014 .

[5]  Shing M. Lee,et al.  Model calibration in the continual reassessment method , 2009, Clinical trials.

[6]  Ying Yuan,et al.  Bayesian hybrid dose‐finding design in phase I oncology clinical trials , 2011, Statistics in medicine.

[7]  Ying Kuen Cheung,et al.  Dose Finding by the Continual Reassessment Method , 2011 .

[8]  S. Weisberg Applied Linear Regression: Weisberg/Applied Linear Regression 3e , 2005 .

[9]  J Whitehead,et al.  Bayesian decision procedures for dose determining experiments. , 1995, Statistics in medicine.

[10]  Ying Kuen Cheung,et al.  Sequential Implementation of Stepwise Procedures for Identifying the Maximum Tolerated Dose , 2007 .

[11]  Ying Yuan,et al.  Bayesian optimal interval designs for phase I clinical trials , 2015, Journal of the Royal Statistical Society: Series C (Applied Statistics).

[12]  Nancy Flournoy,et al.  Dose Finding Using the Biased Coin Up‐and‐Down Design and Isotonic Regression , 2002, Biometrics.

[13]  B E Storer,et al.  Design and analysis of phase I clinical trials. , 1989, Biometrics.

[14]  Ying Yuan,et al.  Bayesian Model Averaging Continual Reassessment Method in Phase I Clinical Trials , 2009 .

[15]  Chikuma Hamada,et al.  Bayesian Model Averaging Continual Reassessment Method for Bivariate Binary Efficacy and Toxicity Outcomes in Phase I Oncology Trials , 2014, Journal of biopharmaceutical statistics.

[16]  D H Leung,et al.  Isotonic designs for phase I trials. , 2001, Controlled clinical trials.

[17]  John O'Quigley,et al.  Consistency of continual reassessment method under model misspecification , 1996 .