Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

被引:18
作者
Betz-Stablein, Brigid D. [1 ]
Morgan, William H. [2 ]
House, Philip H. [2 ]
Hazelton, Martin L. [1 ]
机构
[1] Massey Univ, Inst Fundamental Sci, Palmerston North 4442, New Zealand
[2] Univ Western Australia, Lions Eye Inst, Perth, WA 6009, Australia
基金
英国医学研究理事会;
关键词
TEST-RETEST VARIABILITY; OPTIC DISC; THRESHOLD; FILTER;
D O I
10.1167/iovs.12-11226
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
PURPOSE. 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.) (Invest Ophthalmol Vis Sci. 2013; 54: 1544-1553) DOI: 10.1167/iovs.12-11226
引用
收藏
页码:1544 / 1553
页数:10
相关论文
共 35 条
[1]  
Abdi H., 2007, ENCY MEASUREMENT STA, V3, P104, DOI DOI 10.4135/9781412952644.N60
[2]  
[Anonymous], 2005, R LANG ENV STAT COMP
[3]  
Artes PH, 2002, INVEST OPHTH VIS SCI, V43, P2654
[4]  
Atres PH, 2005, PROG RETIN EYE RES, V24, P333
[5]  
Banerjee S., 2003, Hierarchical modeling and analysis for spatial data
[6]   BAYESIAN IMAGE-RESTORATION, WITH 2 APPLICATIONS IN SPATIAL STATISTICS [J].
BESAG, J ;
YORK, J ;
MOLLIE, A .
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 1991, 43 (01) :1-20
[7]  
Besag J, 1995, BIOMETRIKA, V82, P733, DOI 10.2307/2337341
[8]   A comparison of Bayesian spatial models for disease mapping [J].
Best, N ;
Richardson, S ;
Thomson, A .
STATISTICAL METHODS IN MEDICAL RESEARCH, 2005, 14 (01) :35-59
[9]   DETERMINING PROGRESSIVE VISUAL-FIELD LOSS IN SERIAL HUMPHREY VISUAL-FIELDS [J].
BIRCH, MK ;
WISHART, PK ;
ODONNELL, NP .
OPHTHALMOLOGY, 1995, 102 (08) :1227-1234
[10]  
Chauhan BC, 1999, INVEST OPHTH VIS SCI, V40, P648