  We study the problem of adaptively identifying patient subpopulations that
benefit from a given treatment during a confirmatory clinical trial. This type
of adaptive clinical trial has been thoroughly studied in biostatistics, but
has been allowed only limited adaptivity so far. Here, we aim to relax
classical restrictions on such designs and investigate how to incorporate ideas
from the recent machine learning literature on adaptive and online
experimentation to make trials more flexible and efficient. We find that the
unique characteristics of the subpopulation selection problem -- most
importantly that (i) one is usually interested in finding subpopulations with
any treatment benefit (and not necessarily the single subgroup with largest
effect) given a limited budget and that (ii) effectiveness only has to be
demonstrated across the subpopulation on average -- give rise to interesting
challenges and new desiderata when designing algorithmic solutions. Building on
these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for
subpopulation construction. We empirically investigate their performance across
a range of simulation scenarios and derive insights into their (dis)advantages
across different settings.
