title: Spatial modeling of visual field data for assessing glaucoma progression.
abstract: OBJECTIVE
  In order to reduce noise and account for spatial correlation, we applied disease mapping techniques to visual field (VF) data. We compared our calculated rates of progression to other established techniques.
  
  
  METHODS
  Conditional autoregressive (CAR) priors, weighted to account for physiologic correlations, were employed to describe spatial and spatiotemporal correlation over the VF. Our model is extended to account for several physiologic features, such as the nerve fibers serving adjacent loci on the VF not mapping to the adjacent optic disc regions, the presence of the blind spot, and large measurement fluctuation. The models were applied to VFs from 194 eyes and fitted within a Bayesian framework using Metropolis-Hastings algorithms.
  
  
  RESULTS
  Our method (SPROG for Spatial PROGgression) showed progression in 42% of eyes. Using a clinical reference, our method had the best receiver operating characteristics compared with the point-wise linear regression methods. Because our model intrinsically accounts for the large variation of VF data, by adjusting for spatial correlation, the effects of outliers are minimized, and spurious trends are avoided.
  
  
  CONCLUSIONS
  by using CAR priors, we have modeled the spatial correlation in the eye. combining this with physiologic information, we are able to provide a novel method for VF analysis. model diagnostics, sensitivity, and specificity show our model to be apparently superior to CURRENT POINT-wise linear regression methods. (http://www.anzctr.org.au number, ACTRN12608000274370.).
authors: Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. b.d.betz-stablein@massey.ac.nz
authors: Brigid D
authors: BD
authors: Betz-Stablein
authors: William H
authors: WH
authors: Morgan
authors: Philip H
authors: PH
authors: House
authors: Martin L
authors: ML
authors: Hazelton
mesh_terms: D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D018450:Disease Progression; D005260:Female; D005902:Glaucoma, Open-Angle / Q000175:diagnosis*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009898:Optic Disk / Q000473:pathology; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D014786:Vision Disorders / Q000175:diagnosis*; D058609:Visual Field Tests; D014794:Visual Fields