A Flexible Approach for Modeling Treatment Response with Images as Modifiers of Treatment Effect: An Application to Data from a Clinical Trial for Treatment of MDD

• Activity Number: 243
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Adam Ciarleglio* and Eva Petkova and Thaddeus Tarpey and R. Todd Ogden
• Companies: New York University and New York University School of Medicine and Wright State University and Columbia University
• Keywords: treatment decision ; functional data ; placebo response ; imaging data

Abstract

It has been suggested that brain-imaging data (e.g., data derived from EEG, MRI, and fMRI) may provide information that can help guide clinicians in the selection of treatment for major depressive disorder and may also help to better understand placebo response. In this talk, we discuss a novel functional data analytic approach for modeling the relationship between a response of interest (e.g., change in HAMD or remission) and treatment such that the effect of treatment depends on the characteristics measured by the imaging data. The proposed approach obviates the need to directly model main effects and allows for flexible modeling of the interaction between the imaging predictors and treatment. Our approach will be illustrated and evaluated via simulations and applied to data coming from a large depression study comparing placebo and an active drug.