  We consider applying multi-armed bandits to model-assisted designs for
dose-finding clinical trials. Multi-armed bandits are very simple and powerful
methods to determine actions to maximize a reward in a limited number of
trials. Among the multi-armed bandits, we first consider the use of Thompson
sampling which determines actions based on random samples from a posterior
distribution. In the small sample size, as shown in dose-finding trials,
because the tails of posterior distribution are heavier and random samples are
too much variability, we also consider an application of regularized Thompson
sampling and greedy algorithm. The greedy algorithm determines a dose based on
a posterior mean. In addition, we also propose a method to determine a dose
based on a posterior median. We evaluate the performance of our proposed
designs for six scenarios via simulation studies.
