Abstract
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.
Original language | English (US) |
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Pages (from-to) | 266-279 |
Number of pages | 14 |
Journal | Statistics in Medicine |
Volume | 36 |
Issue number | 2 |
DOIs | |
State | Published - Jan 30 2017 |
Keywords
- BMA-CRM
- Bayesian adaptive design
- continual reassessment method
- maximum tolerated dose
- skeleton specification
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
MD Anderson CCSG core facilities
- Biostatistics Resource Group