A simple Bayesian decision-theoretic design for dose-finding trials

Fan, Shenghua Kelly, Lu, Ying and Wang, You-Gan (2012) A simple Bayesian decision-theoretic design for dose-finding trials. Statistics in Medicine, 31 28: 3719-3730. doi:10.1002/sim.5438


Author Fan, Shenghua Kelly
Lu, Ying
Wang, You-Gan
Title A simple Bayesian decision-theoretic design for dose-finding trials
Journal name Statistics in Medicine   Check publisher's open access policy
ISSN 0277-6715
1097-0258
Publication date 2012-12-01
Sub-type Article (original research)
DOI 10.1002/sim.5438
Volume 31
Issue 28
Start page 3719
End page 3730
Total pages 12
Place of publication Chichester, West Sussex, United Kingdom
Publisher John Wiley & Sons
Collection year 2013
Language eng
Abstract A flexible and simple Bayesian decision-theoretic design for dose-finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose-toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one-step-look-ahead (OSLA), which selects the best-so-far dose. A more complicated rule, such as the two-step-look-ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two-step-look-ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage.
Keyword Bayesian adaptive designs
Dose-finding trials
Decision theory
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2013 Collection
 
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