Using decision lists to construct interpretable and parsimonious treatment regimes

Biometrics. 2015 Dec;71(4):895-904. doi: 10.1111/biom.12354. Epub 2015 Jul 20.


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.

Keywords: Decision lists; Exploratory analyses; Interpretability; Personalized medicine; Treatment regimes.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Biometry / methods
  • Breast Neoplasms / drug therapy
  • Clinical Protocols*
  • Clinical Trials as Topic / statistics & numerical data
  • Computer Simulation
  • Decision Trees*
  • Depression / therapy
  • Evidence-Based Medicine / statistics & numerical data
  • Female
  • Humans
  • Models, Statistical
  • Precision Medicine / statistics & numerical data