Skip to main content
Journal cover image

Using decision lists to construct interpretable and parsimonious treatment regimes.

Publication ,  Journal Article
Zhang, Y; Laber, EB; Tsiatis, A; Davidian, M
Published in: Biometrics
December 2015

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.

Duke Scholars

Published In

Biometrics

DOI

EISSN

1541-0420

Publication Date

December 2015

Volume

71

Issue

4

Start / End Page

895 / 904

Location

England

Related Subject Headings

  • Statistics & Probability
  • Precision Medicine
  • Models, Statistical
  • Humans
  • Female
  • Evidence-Based Medicine
  • Depression
  • Decision Trees
  • Computer Simulation
  • Clinical Trials as Topic
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhang, Y., Laber, E. B., Tsiatis, A., & Davidian, M. (2015). Using decision lists to construct interpretable and parsimonious treatment regimes. Biometrics, 71(4), 895–904. https://doi.org/10.1111/biom.12354
Zhang, Yichi, Eric B. Laber, Anastasios Tsiatis, and Marie Davidian. “Using decision lists to construct interpretable and parsimonious treatment regimes.Biometrics 71, no. 4 (December 2015): 895–904. https://doi.org/10.1111/biom.12354.
Zhang Y, Laber EB, Tsiatis A, Davidian M. Using decision lists to construct interpretable and parsimonious treatment regimes. Biometrics. 2015 Dec;71(4):895–904.
Zhang, Yichi, et al. “Using decision lists to construct interpretable and parsimonious treatment regimes.Biometrics, vol. 71, no. 4, Dec. 2015, pp. 895–904. Pubmed, doi:10.1111/biom.12354.
Zhang Y, Laber EB, Tsiatis A, Davidian M. Using decision lists to construct interpretable and parsimonious treatment regimes. Biometrics. 2015 Dec;71(4):895–904.
Journal cover image

Published In

Biometrics

DOI

EISSN

1541-0420

Publication Date

December 2015

Volume

71

Issue

4

Start / End Page

895 / 904

Location

England

Related Subject Headings

  • Statistics & Probability
  • Precision Medicine
  • Models, Statistical
  • Humans
  • Female
  • Evidence-Based Medicine
  • Depression
  • Decision Trees
  • Computer Simulation
  • Clinical Trials as Topic