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Activity Number: 293 - SPEED: Computing, Graphics, and Programming Statistics
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #324783 View Presentation
Title: A Network-based Algorithm for Clustering Multivariate Longitudinal Data
Author(s): Matthew Koslovsky* and Millennia Young and Caroline Schaefer and John Arellano and Al Feiveson
Companies: KBRwyle and National Aeronautics and Space Administration and MEI Technologies and MEI Technologies and National Aeronautics and Space Administration
Keywords: clustering ; longitudinal data ; network analysis ; community detection ; exploratory data analysis

The NASA Astronaut Corps are a unique occupational cohort that have vast amounts of repeated measures data collected over the course of their career in various researcher studies before, during, and after spaceflight and additionally in multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive data sets. Multivariate clustering algorithms for longitudinal data may help parse these data to identify homogeneous astronaut groups that have higher risks for a particular physiological change. However, available clustering methods often rely on strict model assumptions, require equally-spaced and balanced assessments times, and cannot handle missing data or differing time scales. To fill this gap, we propose a network-based clustering algorithm for multivariate longitudinal data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

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

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