Work

Flexible functional regression methods for estimating individualized treatment rules

Year: 2016

Type: article

Abstract: A major focus of personalized medicine is on the development of individualized treatment rules that depend upon baseline measures. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods for developing such rules are progressing rapidly, but few methods have considered the use of pretreatment functional data to guide decision‐making. Furthermore, those methods that do allow for the incorporation of functional pretreatment covariates typically make strong assumptions about the relationships between the functional covariates and the response of interest. We propose two approaches for using functional data to select an optimal treatment that address some of the shortcomings of previously developed methods. Specifically, we combine the flexibility of functional additive regression models with Q‐learning or A‐learning in order to obtain treatment decision rules. Properties of the corresponding estimators are discussed. Our approaches are evaluated in several realistic settings using synthetic data and are applied to data arising from a clinical trial comparing two treatments for major depressive disorder in which baseline imaging data are available for subjects who are subsequently treated. Copyright © 2016 John Wiley & Sons, Ltd.

Source: Stat

DOI: 10.1002/sta4.114

Authors: Adam Ciarleglio, Eva Petkova, Thaddeus Tarpey, R. Todd Ogden

Institutions: NYU Langone Health, New York University, Nathan Kline Institute for Psychiatric Research, Wright State University, Columbia University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: NYU Langone Medical Center, National Institutes of Health, National Institute of Mental Health