  The personalization of treatment via bio-markers and other risk categories
has drawn increasing interest among clinical scientists. Personalized treatment
strategies can be learned using data from clinical trials, but such trials are
very costly to run. This paper explores the use of active learning techniques
to design more efficient trials, addressing issues such as whom to recruit, at
what point in the trial, and which treatment to assign, throughout the duration
of the trial. We propose a minimax bandit model with two different optimization
criteria, and discuss the computational challenges and issues pertaining to
this approach. We evaluate our active learning policies using both simulated
data, and data modeled after a clinical trial for treating depressed
individuals, and contrast our methods with other plausible active learning
policies.
