May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Evaluating Glaucoma Progression Using a Latent Class Model for MD, PSD, AGIS, and VFI With RNFL as Covariate
Author Affiliations & Notes
  • R. A. Bilonick
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • K. Sung
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • G. Wollstein
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • H. Ishikawa
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • K. A. Townsend
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • L. Kagemann
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • R. J. Noecker
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • J. S. Schuman
    Department of Ophthalmology, UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • Footnotes
    Commercial Relationships  R.A. Bilonick, None; K. Sung, None; G. Wollstein, Carl Zeiss Meditec, Inc., F; Optoview, F; H. Ishikawa, None; K.A. Townsend, None; L. Kagemann, None; R.J. Noecker, Heidelberg Engineering, R; Alcon, R; Merck, R; Lumenis, R; Allergan, R; J.S. Schuman, Carl Zeiss Meditec, Inc., F; Allergan, F; Carl Zeiss Meditec, Inc., F; Merck, F; Heidelberg Engineering, F; Optovue, F; Carl Zeiss Meditec, Inc., P; Alcon, R; Allergan, R; Carl Zeiss Meditec, Inc., R; Merck, R; Heidelberg Engineering, R; Optovue, R.
  • Footnotes
    Support  NIH R01-EY013178-6, P30-EY008098; Eye and Ear Foundation (Pittsburgh, PA); Research to Prevent Blindness
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 3610. doi:
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      R. A. Bilonick, K. Sung, G. Wollstein, H. Ishikawa, K. A. Townsend, L. Kagemann, R. J. Noecker, J. S. Schuman; Evaluating Glaucoma Progression Using a Latent Class Model for MD, PSD, AGIS, and VFI With RNFL as Covariate. Invest. Ophthalmol. Vis. Sci. 2008;49(13):3610.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose:
 

This study predicts glaucoma progression using a longitudinal latent class regression (LCR) model of visual field global parameters: mean deviation (MD), pattern standard deviation (PSD), advanced glaucoma intervention study (AGIS) score, and visual field index (VFI). Baseline optical coherence tomography retinal nerve fiber layer (RNFL) average thickness was included as an exogenous covariate and longitudinal RNFL as an endogenous covariate.

 
Methods:
 

LCR model isf(yi|zi, θ) = Σk πk|zi Πj fk(yij|zi, θjk) where yi = responses, zi = covariates, θ = model parameters, πk|zi = prior probability for class k given exogenous covariates z, f = response joint density, and fk = univariate response densities for each class. Posterior probabilities for class (progressor, nonprogressor) were computed. Models with 2 classes were fitted to MD, PSD, AGIS, and VFI measured 5 times over a 5 year period for 61 glaucoma subjects and glaucoma suspects (106 eyes). Model 1 included RNFL as an exogenous covariate with time as an endogenous covariate. Model 2 added RNFL and RNFL × time as endogenous covariates. Response variables were standardized. Maximum likelihood was used to estimate the model parameters using R and the mmlcr package.

 
Results:
 

Baseline RNFL was highly statistically significant in both models. Profiles for progressors and nonprogressors were substantially different (see table). In Model 1, slopes were flat for nonprogressors. MD and VFI slopes for progressors were strongly negative and for PSD and AGIS strongly positive. Slope magnitude was highest for VFI. AICs indicated Model 2 as the better model. There were no interactions for nonprogressors but there were substantial interactions for progressors. For high RNFL, PSD tends to increase over time while for low RNFL, PSD remains flat for progressors.

 
Conclusions:
 

LCR provided a very flexible method for classifying subjects and describing the relationships among response variables adjusting for covariates.  

 
Clinical Trial:
 

NCT00286637

 
Keywords: clinical (human) or epidemiologic studies: biostatistics/epidemiology methodology • visual fields 
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