Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes

TL;DR

This paper introduces a simple, interpretable class of treatment regimes using decision lists, which are optimized for personalized medicine to improve outcomes and reduce costs, demonstrated through simulations and clinical trial data.

Contribution

It proposes a novel, interpretable decision list framework for treatment regimes and derives a robust estimator for optimal regimes within this class.

Findings

Decision lists are effective for personalized treatment regimes.

The estimator performs well in finite samples.

Application to clinical trial data shows practical utility.

Abstract

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.