Bayesian robustness for decision making problems: Applications in medical contexts

Practical implementation of Bayesian decision making is hindered by the fact that optimal decisions may be sensitive to the model inputs: the prior, the likelihood and/or the underlying utility function. Given the structure of a problem, the analyst has to decide which sensitivity measures are relevant and compute them efficiently. We address the issue of robustness of the optimal action in a decision making problem with respect to the prior model and the utility function. We discuss some general principles and apply novel computational strategies in the context of two relatively complex medical decision making problems.