Purpose: We developed a statistical model to improve the detection of glaucomatous visual field (VF) progression as defined by the consensus of expert clinicians. Methods: We developed new methodology in the Bayesian setting to properly model the progression status of a patient (as determined by a group of expert clinicians) as a function of changes in spatially correlated sensitivities at each VF location jointly. We used a spatial probit regression model that jointly incorporates all highly correlated VF changes in a single framework while accounting for structural similarities between neighboring VF regions. Results: Our method had improved model fit and predictive ability compared to competing models as indicated by the deviance information criterion (198.15 vs. 201.29–213.38), a posterior predictive model selection metric (130.08 vs. 142.08– 155.59), area under the receiver operating characteristic curve (0.80 vs. 0.59–0.72; all P values, 0.018), and optimal sensitivity (0.92 vs. 0.28–0.82). Simulation study results suggest that estimation (reduction of mean squared errors) and inference (correct coverage of 95% credible intervals) for the model parameters are improved when spatial modeling is incorporated. Conclusions: We developed a statistical model for the detection of VF progression defined by clinician expert consensus that accounts for spatially correlated changes in visual sensitivity over time, and showed that it outperformed competing models in a number of areas. Translational Relevance: This model may easily be incorporated into routine clinical practice and be useful for detecting glaucomatous VF progression defined by clinician expert consensus.
- Conditional autoregressive model
- Glaucoma progression
- Spatially correlated predictors
- Spatially varying oefficients
- Visual field
ASJC Scopus subject areas
- Biomedical Engineering