Interpretable treatment regimes

A treatment regime formalizes personalized medicine as a rule that provides treatment recommendations from individual patient characteristics. 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 estimation of 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 to those without advanced statistical training. We propose a simple, yet expressive class of treatment regimes that are representable as simple list of if-then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for collaboration. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulated experiments. The proposed method is illustrated using data from a breast cancer clinical trial.