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Activity Number: 297
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #314925 View Presentation
Title: Joint Modeling and Clustering Paired Generalized Longitudinal Trajectories with Application to Cocaine Abuse Treatment Data
Author(s): Yehua Li* and Hui Huang and Yongtao Guan
Companies: Iowa State University and Peking University and University of Miami
Keywords: Clustering ; functional data analysis ; joint modeling ; EM algorithm ; Metropolis-Hastings algorithm ; splines
Abstract:

In a cocaine dependence treatment study, we have paired binary longitudinal trajectories that record the cocaine use patterns of each patient before and after a treatment. We propose a general framework based on functional data analysis to jointly model and cluster these paired non-Gaussian longitudinal trajectories. Our approach assumes that the response variables follow distributions from the exponential family, with the canonical parameters determined by some latent Gaussian processes. We express the latent processes by a truncated Karhunen-L\'{o}eve (KL) expansion allowing the mean and covariance functions to be different across clusters. We further represent the mean and eigenfunctions functions by flexible spline bases, and determine the orders of the truncated KL expansions using data-driven methods. The cluster membership and parameters are jointly estimated by a Monte Carlo EM algorithm with Gibbs sampling steps. We discover subgroups of patients with distinct behaviors in terms of overall probability to use, binge verses periodic use pattern, etc. The joint modeling approach also sheds new lights on relating relapse behavior to baseline pattern in each subgroup.


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

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