Adaptive Bayesian compound designs for dose finding studies

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.     

A LARGE-SCALE PERSONNEL ASSIGNMENT MODEL FOR THE NAVY*

The U.S. Navy has been trying for years to develop a model to automate its personnel assignment process The requirement of rotating navy personnel frequently between sea duty and shore duty in accordance with multiple policies makes modeling efforts difficult. However, without a model to optimize assignments with regard to multiple policies, navy managers do not know the real impact of personnel assignments. This limits their ability to make effective decisions. This paper summarizes our effort to develop a large-scale network model for the navy's assignment problem. The navy has accepted this model for implementation, replacing the current manual assignment process. The approach in this paper can be generalized to a wide variety of perosnnel assignment problems.