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Activity Number: 702
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #316989
Title: Interpretable Treatment Regimes
Author(s): Yichi Zhang* and Eric Laber and Anastasios Tsiatis and Marie Davidian
Companies: North Carolina State University and North Carolina State University and North Carolina State University and North Carolina State University
Keywords: Decision lists ; Exploratory analyses ; Interpretability ; Personalized medicine ; Treatment regimes
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. 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, where 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. To this end, we propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if-then statements. 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.


Authors who are presenting talks have a * after their name.

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