Using decision lists to construct interpretable and parsimonious treatment regimes.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Zhang, Y; Laber, EB; Tsiatis, A; Davidian, M

Published Date

  • December 2015

Published In

Volume / Issue

  • 71 / 4

Start / End Page

  • 895 - 904

PubMed ID

  • 26193819

Pubmed Central ID

  • 26193819

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/biom.12354

Language

  • eng

Conference Location

  • United States