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College of Arts & Sciences
Department of Statistics

Xiaogang Su Colloquium

Thursday, October 20, 2016 - 2:45pm

Statistics Department Colloquium

Where: LeConte College, Room 210

Speaker: Xiaogang Su

Affiliation: University of Texas-El Paso, Department of Mathematical Sciences

Title: A Look into Personalized Medicine via Interaction Trees


There has been a growing interest in personalized medicine which essentially involves assessing heterogeneous treatment effects. Concerning experimental data collected from randomized trials, we explore stratified and individualized treatment effects with a machine learning approach -- Interaction Trees (IT; Su et al., 2009). We first propose a smooth sigmoid surrogate (SSS) splitting method, as an alternative to greedy search (GS), to speed up GS and amend its deficiencies. On the basis of modified IT, causal inference at different levels can be made. More specifically, an aggregated grouping procedure stratifies data into refined subgroups where the treatment effect remains homogeneous in each. Ensembles of IT models can provide prediction for individualized treatment effects (ITE), which compares favorably to the traditional ‘separate regression’ methods. In order to extract meaningful interpretations, we have also made available several other features such as variable importance ranking, partial dependence plot to help identify important effect moderators for the treatment among high-dimensional covariates, and ensemble majority voting for determining the optimal treatment regime. An empirical illustration of the proposed techniques is made via an analysis of quality of life (QoL) data from breast cancer survivors.

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