  A treatment regime formalizes personalized medicine as a function from
individual patient characteristics to a recommended treatment. A high-quality
treatment regime can improve patient outcomes while reducing cost, resource
consumption, and treatment burden. Thus, there is tremendous interest in
estimating treatment regimes from observational and randomized studies.
However, the development of treatment regimes for application in clinical
practice requires the long-term, joint effort of statisticians and clinical
scientists. In this collaborative process, the statistician must integrate
clinical science into the statistical models underlying a treatment regime and
the clinician must scrutinize the estimated treatment regime for scientific
validity. To facilitate meaningful information exchange, it is important that
estimated treatment regimes be interpretable in a subject-matter context. We
propose a simple, yet flexible class of treatment regimes whose members are
representable as a short list of if-then statements. Regimes in this class are
immediately interpretable and are therefore an appealing choice for broad
application in practice. We derive a robust estimator of the optimal regime
within this class and demonstrate its finite sample performance using
simulation experiments. The proposed method is illustrated with data from two
clinical trials.
