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Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions.

Publication ,  Journal Article
Swaminathan, SS; Berchuck, SI; Jammal, AA; Rao, JS; Medeiros, FA
Published in: Transl Vis Sci Technol
February 1, 2022

PURPOSE: To compare the ability of linear mixed models with different random effect distributions to estimate rates of visual field loss in glaucoma patients. METHODS: Eyes with five or more reliable standard automated perimetry (SAP) tests were identified from the Duke Glaucoma Registry. Mean deviation (MD) values from each visual field and associated timepoints were collected. These data were modeled using ordinary least square (OLS) regression and linear mixed models using the Gaussian, Student's t, or log-gamma (LG) distributions as the prior distribution for random effects. Model fit was compared using the Watanabe-Akaike information criterion (WAIC). Simulated eyes of varying initial disease severity and rates of progression were created to assess the accuracy of each model in predicting the rate of change and likelihood of declaring progression. RESULTS: A total of 52,900 visual fields from 6558 eyes of 3981 subjects were included. Mean follow-up period was 8.7 ± 4.0 years, with an average of 8.1 ± 3.7 visual fields per eye. The LG model produced the lowest WAIC, demonstrating optimal model fit. In simulations, the LG model declared progression earlier than OLS (P < 0.001) and had the greatest accuracy in predicted slopes (P < 0.001). The Gaussian model significantly underestimated rates of progression among fast and catastrophic progressors. CONCLUSIONS: Linear mixed models using the LG distribution outperformed conventional approaches for estimating rates of SAP MD loss in a population with glaucoma. TRANSLATIONAL RELEVANCE: Use of the LG distribution in models estimating rates of change among glaucoma patients may improve their accuracy in rapidly identifying progressors at high risk for vision loss.

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Published In

Transl Vis Sci Technol

DOI

EISSN

2164-2591

Publication Date

February 1, 2022

Volume

11

Issue

2

Start / End Page

16

Location

United States

Related Subject Headings

  • Visual Fields
  • Visual Field Tests
  • Vision Disorders
  • Intraocular Pressure
  • Humans
  • Glaucoma
  • Follow-Up Studies
  • 3212 Ophthalmology and optometry
  • 1113 Opthalmology and Optometry
  • 0903 Biomedical Engineering
 

Citation

APA
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MLA
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Swaminathan, S. S., Berchuck, S. I., Jammal, A. A., Rao, J. S., & Medeiros, F. A. (2022). Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions. Transl Vis Sci Technol, 11(2), 16. https://doi.org/10.1167/tvst.11.2.16
Swaminathan, Swarup S., Samuel I. Berchuck, Alessandro A. Jammal, J Sunil Rao, and Felipe A. Medeiros. “Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions.Transl Vis Sci Technol 11, no. 2 (February 1, 2022): 16. https://doi.org/10.1167/tvst.11.2.16.
Swaminathan SS, Berchuck SI, Jammal AA, Rao JS, Medeiros FA. Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions. Transl Vis Sci Technol. 2022 Feb 1;11(2):16.
Swaminathan, Swarup S., et al. “Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions.Transl Vis Sci Technol, vol. 11, no. 2, Feb. 2022, p. 16. Pubmed, doi:10.1167/tvst.11.2.16.
Swaminathan SS, Berchuck SI, Jammal AA, Rao JS, Medeiros FA. Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions. Transl Vis Sci Technol. 2022 Feb 1;11(2):16.

Published In

Transl Vis Sci Technol

DOI

EISSN

2164-2591

Publication Date

February 1, 2022

Volume

11

Issue

2

Start / End Page

16

Location

United States

Related Subject Headings

  • Visual Fields
  • Visual Field Tests
  • Vision Disorders
  • Intraocular Pressure
  • Humans
  • Glaucoma
  • Follow-Up Studies
  • 3212 Ophthalmology and optometry
  • 1113 Opthalmology and Optometry
  • 0903 Biomedical Engineering