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Activity Number: 186 - Contributed Poster Presentations: International Chinese Statistical Association
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #305235
Title: A Rank-Based Regression Tree for Subgroup Identification
Author(s): Xiang Peng* and Huixia Judy Wang
Companies: The George Washington University and The George Washington University
Keywords: quantile treatment effect; rank score; recursive partitioning; regression tree; subgroup analysis

One primary goal of subgroup analysis is to identify subgroups of subjects with differential treatment effects. Existing methods have focused on the mean treatment effect, and thus are ineffective when the two distributions differ in scales or only in the upper or lower tail. We develop a new rank-based regression tree method for subgroup identification. The new method uses rank score tests to partition the regression tree and is free of selection bias. In each node, the population difference is summarized by a generalized quantile treatment effect. We show that the proposed method gives more accurate or comparative subgroup identification than existing methods under various models.

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

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