  In the problem of composite hypothesis testing, identifying the potential
uniformly most powerful (UMP) unbiased test is of great interest. Beyond
typical hypothesis settings with exponential family, it is usually challenging
to prove the existence and further construct such UMP unbiased tests with
finite sample size. For example in the COVID-19 pandemic with limited previous
assumptions on the treatment for investigation and the standard of care,
adaptive clinical trials are appealing due to ethical considerations, and the
ability to accommodate uncertainty while conducting the trial. Although several
methods have been proposed to control type I error rates, how to find a more
powerful hypothesis testing strategy is still an open question. Motivated by
this problem, we propose an automatic framework of constructing test statistics
and corresponding critical values via machine learning methods to enhance power
in a finite sample. In this article, we particularly illustrate the performance
using Deep Neural Networks (DNN) and discuss its advantages. Simulations and
two case studies of adaptive designs demonstrate that our method is automatic,
general and pre-specified to construct statistics with satisfactory power in
finite-sample. Supplemental materials are available online including R code and
an R shiny app.
