Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Jan 1;1(1):103-114.
doi: 10.1002/sta.411.

Estimating Optimal Treatment Regimes from a Classification Perspective

Baqun Zhang  1 , Anastasios A Tsiatis, Marie Davidian, Min Zhang, Eric Laber
Affiliations

Estimating Optimal Treatment Regimes from a Classification Perspective

Baqun Zhang et al. Stat. .

Abstract

A treatment regime maps observed patient characteristics to a recommended treatment. Recent technological advances have increased the quality, accessibility, and volume of patient-level data; consequently, there is a growing need for powerful and flexible estimators of an optimal treatment regime that can be used with either observational or randomized clinical trial data. We propose a novel and general framework that transforms the problem of estimating an optimal treatment regime into a classification problem wherein the optimal classifier corresponds to the optimal treatment regime. We show that commonly employed parametric and semi-parametric regression estimators, as well as recently proposed robust estimators of an optimal treatment regime can be represented as special cases within our framework. Furthermore, our approach allows any classification procedure that can accommodate case weights to be used without modification to estimate an optimal treatment regime. This introduces a wealth of new and powerful learning algorithms for use in estimating treatment regimes. We illustrate our approach using data from a breast cancer clinical trial.

Keywords: classification; doubly robust estimator; inverse probability weighting; personalized medicine; potential outcomes; propensity score.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Breiman L, Freidman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Wadsworth; Belmont, CA: 1984.
    1. Brinkley J, Tsiatis AA, Anstrom KJ. A generalized estimator of the attributable benefit of an optimal treatment regime. Biometrics. 2009;21:512–522. - PMC - PubMed
    1. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995:273–297.
    1. Fisher B, Redmond C, Brown A, Wickerham DL, Wolmark N, Allegra J, Escher G, Lippman M, Savlov E, Wittliff J. Influence of tumor estrogen and progesterone receptor levels on the response to Tamoxifen and chemotherapy in primary breast cancer. Journal of Clinical Oncology. 1983;1:227–241. - PubMed
    1. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences. 1997;55:119–139.

LinkOut - more resources