alexa Predicting Clinical Binary Outcome Using Multivariate Longitudi nal Data: Application to Patients with Newly Diagnosed Primary Open - Angle Glaucoma
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Predicting Clinical Binary Outcome Using Multivariate Longitudi nal Data: Application to Patients with Newly Diagnosed Primary Open - Angle Glaucoma

Feng Gao1,2*, J Philip Miller2, Julia A Beiser3, Chengjie Xiong2 and Mae O Gordon2,3

1Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA

2Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA

3Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO, USA

*Corresponding Author:
Feng Gao
Division of Public Health Sciences
Campus Box 8100
Department of Surgery
Washington University School ofMedicine
660 S Euclid Avenue, St. Louis
MO 63110, USA
Tel: (314) 362- 3682
Fax: (314) 454-7941
E-mail: [email protected]

Received date: September 13, 2015; Accepted date: October 19, 2015; Published date: October26, 2015

Citation: Gao F, Miller JP, Beiser JA, Xiong C, Gordon MO (2015) Predicting Clinical Binary Outcome Using Multivariate Longitudinal Data: Application to Patients with Newly Diagnosed Primary Open-Angle Glaucoma. J Biom Biostat 6:254. doi:10.4172/2155-6180.1000254

Copyright: © 2015 Gao F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Primary open angle glaucoma (POAG) is a chronic, progressive, irreversible, and potentially blinding optic neuropathy. 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 damage. In this article, a latent class growth model (LCGM) was developed to predict the binary outcome of VF progression using longitudinal mean deviation (MD) and pattern standard deviation (PSD). Specifically, the trajectories of MD and PSD were summarized by a functional principal component (FPC) analysis, and the estimated FPC scores were used to identify subgroups (latent classes) of individuals with distinct patterns of MD and PSD trajectories. Probability of VF progression for an individual was then estimated as weighted average across latent classes, weighted by posterior probability of class membership given baseline covariates and longitudinal MD/PSD series. The model was applied to the participants with newly diagnosed POAG from the Ocular Hypertension Treatment Study (OHTS), and the OHTS data was best fit by a model with 4 latent classes. Using the resultant optimal LCGM, the OHTS participants with and without VF progression could be accurately differentiated by incorporating longitudinal MD and PSD.

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