  We propose to use Bayesian optimization (BO) to improve the efficiency of the
design selection process in clinical trials. BO is a method to optimize
expensive black-box functions, by using a regression as a surrogate to guide
the search. In clinical trials, planning test procedures and sample sizes is a
crucial task. A common goal is to maximize the test power, given a set of
treatments, corresponding effect sizes, and a total number of samples. From a
wide range of possible designs we aim to select the best one in a short time to
allow quick decisions. The standard approach to simulate the power for each
single design can become too time-consuming. When the number of possible
designs becomes very large, either large computational resources are required
or an exhaustive exploration of all possible designs takes too long. Here, we
propose to use BO to quickly find a clinical trial design with high power from
a large number of candidate designs. We demonstrate the effectiveness of our
approach by optimizing the power of adaptive seamless designs for different
sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of
all candidate designs shows that BO finds competitive designs in a fraction of
the time.
