Global Visit Effects in Point-Wise Longitudinal Modeling of Glaucomatous Visual Fields

Area of Science:

• Ophthalmology
• Biostatistics
• Medical image analysis

Background:

• Visual field (VF) testing is crucial for glaucoma management.
• Point-wise sensitivity estimates in VF data often contain large, correlated measurement errors.
• These errors can impact the accuracy of glaucoma progression analysis.

Purpose of the Study:

• To introduce and evaluate a novel model incorporating global visit effects (GVE) for VF data.
• To assess the impact of GVE on the estimation and prediction of glaucoma progression.
• To compare the performance of a GVE model against a conventional model.

Main Methods:

• Analysis of 15 biannual VFs from 125 primary glaucoma patients.
• Comparison of model fit and predictive ability between a conventional model and a GVE-incorporating model.
• Evaluation of GVE's effect on estimated progression slopes and magnitude relative to other errors.

Main Results:

• The GVE model demonstrated significant improvements in model fit and predictive ability, particularly with limited VF data.
• The average absolute difference in estimated progression slopes between models was 0.13 dB/y.
• The magnitude of GVE was found to be over three times larger than combined measurable factors.

Conclusions:

• Incorporating GVE into longitudinal VF data modeling enhances the accuracy of progression rate estimates.
• The GVE model improves the prediction of future visual sensitivities in glaucoma patients.
• This modeling approach offers better insights into glaucoma progression dynamics.