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Activity Number: 271
Type: Contributed
Date/Time: Monday, August 10, 2015 : 3:05 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #317795
Title: Predicting Binary Outcome Using Multivariate Longitudinal Data: Monitoring Disease Progression in Patients with Newly Diagnosed Primary Open-Angle Glaucoma
Author(s): Feng Gao* and Philip Miller and Chengjie Xiong and Julia Beiser and Mae Gordon
Companies: Washington University School of Medicine and Washington University School of Medicine and Washington University in St. Louis and Washington University School of Medicine and Washington University School of Medicine
Keywords: latent class analysis ; multivariate longitudinal data ; growth mixture model ; primary open angle glaucoma ; dynamic prediction
Abstract:

Primary open angle glaucoma (POAG) is a chronic progressive and potentially blinding optic neuropathy. Glaucoma damage is irreversible because nothing yet can restore the optic nerve cells once they are dead. However, the risk of blindness due to progressive visual field (VF) loss varies substantially from patient to patient. Early identification of those patients destined to rapid progressive visual loss is crucial to prevent further irreversible visual field loss. In Ocular Hypertension Treatment Study, we developed a model to dynamically predict the binary outcome of VF progression using longitudinal visual field mean deviation (MD) and pattern standard deviation (PSD) from the first eye of 277 participants who developed POAG. Specifically, a growth mixture model was fitted to identify unique subgroups (latent classes) based on the longitudinally changing patterns of visual field MD and PSD over time, and the probability of VF progression for an individual given repeated measurements up to time t can be estimated as the weighted average across latent classes, weighted by posterior probability of class membership. Performance of method was also assessed using simulation studies.


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

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