Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

• Activity Number: 140
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Brigid Betz-Stablein*+ and William H. Morgan and Philip H. House and Martin L. Hazelton
• Companies: Massey University and University of Western Australia and University of Western Australia and Massey University
• Keywords: conditional autoregressive prior ; disease mapping ; spatio-temporal ; glaucoma ; hierarchical model ; longitudinal

Abstract

Glaucoma is the second leading cause of blindness worldwide. Caused by increased pressure within the eye, glaucoma can lead to irreparable vision loss. Glaucoma severity can be measured by testing a subject's visual field (VF) over a grid like structure, and the progression of the disease determined by studying sequences of VF test results taken over time. By borrowing tools from the disease mapping literature, we develop models for longitudinal sets of VF data that account for the correlation structure generated by the spatial configuration of VF measurements across the retina. Our model is extended to include several physiological features such as adjacent loci on the VF map not being adjacent on the optic disk, the presence of the blind spot, and large measurement errors. We employ conditional autoregressive priors, weighted to account for the physiological correlations in the eye, to describe spatial and spatio-temporal correlation in the mean response over the VF. The models are fitted within a Bayesian framework and implemented using Metropolis-Hastings algorithms. Results indicate that our method is superior to current point-wise linear regression methods.