  Most Bayesian response-adaptive designs unbalance randomization rates towards
the most promising arms with the goal of increasing the number of positive
treatment outcomes during the study, even though the primary aim of the trial
is different. We discuss Bayesian uncertainty directed designs (BUD), a class
of Bayesian designs in which the investigator specifies an information measure
tailored to the experiment. All decisions during the trial are selected to
optimize the available information at the end of the study. The approach can be
applied to several designs, ranging from early stage multi-arm trials to
biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of
the patient allocation proportion to treatments, and illustrate the
finite-sample operating characteristics of BUD designs through examples,
including multi-arm trials, biomarker-stratified trials, and trials with
multiple co-primary endpoints.
