Abstract:
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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.
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