title: Treatment decisions based on scalar and functional baseline covariates.
abstract: The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of "advantage learning" to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available.
authors.affiliation: Department of Child and Adolescent Psychiatry, New York University, New York, NY 10016, U.S.A.
authors.forename: Adam
authors.initials: A
authors.lastname: Ciarleglio
authors.affiliation: Department of Child and Adolescent Psychiatry, New York University, New York, NY 10016, U.S.A.|Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, U.S.A.
authors.forename: Eva
authors.initials: E
authors.lastname: Petkova
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York NY, 10032, U.S.A.
authors.forename: R Todd
authors.initials: RT
authors.lastname: Ogden
authors.affiliation: Department of Mathematics and Statistics, Wright State University, Dayton, OH 45435, U.S.A.
authors.forename: Thaddeus
authors.initials: T
authors.lastname: Tarpey
mesh_terms: D001699:Biometry / Q000379:methods; D002985:Clinical Protocols; D003198:Computer Simulation; D003662:Decision Theory; D003865:Major Depressive Disorder / Q000473:pathology / Q000503:physiopathology / Q000628:therapy; D004569:Electroencephalography; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D057285:Precision Medicine / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software
keywords: Advantage learning; Depression; Electroencephalography data; Functional data; Personalized medicine; Treatment regime