Adaptive Bayesian compound designs for dose finding studies

McGree, J. M., Drovandi, C. C., Thompson, M. H., Eccleston, J. A., Duffull, S. B., Mengersen, K., Pettitt, A. N. and Goggin, T. (2012) Adaptive Bayesian compound designs for dose finding studies. Journal of Statistical Planning and Inference, 142 6: 1480-1492.

Author McGree, J. M.
Drovandi, C. C.
Thompson, M. H.
Eccleston, J. A.
Duffull, S. B.
Mengersen, K.
Pettitt, A. N.
Goggin, T.
Title Adaptive Bayesian compound designs for dose finding studies
Journal name Journal of Statistical Planning and Inference   Check publisher's open access policy
ISSN 0378-3758
Publication date 2012-06
Sub-type Article (original research)
DOI 10.1016/j.jspi.2011.12.029
Volume 142
Issue 6
Start page 1480
End page 1492
Total pages 13
Place of publication Amsterdam , Netherlands
Publisher Elsevier
Collection year 2013
Language eng
Abstract We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.
Keyword Adaptive design
Compound utility
Importance sampling
Markov chain Monte Carlo
Optimal design
Utility functions
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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