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Activity Number: 125 - Novel Approaches for Estimating and Evaluating Treatment Rules with Applications in Mental Health Research
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Mental Health Statistics Section
Abstract #312564
Title: Sufficient Dimension Reduction for Interactions and Its Application to Optimizing Individualized Dose Rules
Author(s): Hyung Park* and Eva Petkova and Thaddeus Tarpey and R. Todd Ogden
Companies: New York University and NYU School of Medicine and NYU School of Medicine and Columbia University
Keywords: Sufficient dimension reduction; Interaction effect; Projection-pursuit regression; ensor product P-splines

Dimension reduction lies at the heart of many statistical methods. In regression, dimension reduction has been linked to the notion of sufficiency whereby the relation of the response to a set of predictors is explained by a lower dimensional subspace in the predictor space. In this work, we consider the notion of a dimension reduction in regression on subspaces that are sufficient to explain interaction effects between a set of predictors and another variable of interest, leading to a parsimonious parametrization of projection-pursuit regression models specifically defined for the interaction effects. The motivation for this work is from precision medicine where the performance of an individualized does rule, given a set of patient’s characteristics, is determined by interaction effects between dose and patient’s characteristics.

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

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