Generalized Mixed Effects Regression Tree for Longitudinal Count Data

• Activity Number: 414 - Model Building and Selection
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Xiaoqin Xiong* and Joel A Dubin
• Companies: Food and Drug Administration and University of Waterloo
• Keywords: mixed effects ; regression tree ; longitudinal data ; simulation ; linearization

Abstract

We propose a generalized mixed effects regression tree (GMRTree) based method which estimates the tree by standard tree method such as CART and estimates the random effects by a generalized linear mixed effects model. One of the main steps in this method was to use a linearization technique to change the longitudinal count response into a continuous surrogate response. Simulations have shown that the GMRTree method can effectively detect the underlying tree structure in an applicable longitudinal dataset, and has better predictive performance than either a standard tree approach without random effects or a generalized linear mixed effects model, assuming the underlying model indeed has a tree structure. We have also applied this method to two longitudinal datasets, one from the aforementioned hemodialysis study and the other from an epilepsy study.