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Functional generalized estimating equation model to detect glaucomatous visual field progression.

Sanghun Jeong1, Hwayeong Kim2, Sangwoo Moon3

  • 1Department of Statistics, Pusan National University, Busan 46241, Republic of Korea.

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|January 23, 2026
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Summary
This summary is machine-generated.

A new functional generalized estimating equation (GEE) model effectively detects glaucomatous visual field progression in primary open-angle glaucoma (POAG) patients. This method identifies more cases faster than existing algorithms.

Keywords:
functional generalized estimating equation modelperimetric progressionprimary open angle glaucoma

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Area of Science:

  • Ophthalmology
  • Biostatistics
  • Medical Technology

Background:

  • Glaucomatous visual field progression is a key indicator of disease advancement.
  • Early and accurate detection of progression is crucial for timely intervention.
  • Current methods for detecting visual field progression have limitations in sensitivity and speed.

Purpose of the Study:

  • To develop and validate a functional generalized estimating equation (GEE) model for detecting glaucomatous visual field progression.
  • To compare the performance of the proposed functional GEE model against established algorithms.

Main Methods:

  • A cohort of 716 eyes from 716 primary open-angle glaucoma (POAG) patients with sufficient visual field test data and follow-up was analyzed.
  • A functional GEE model was trained on 501 eyes and tested on 215 eyes.
  • Performance was evaluated by comparing the functional GEE model with mean deviation (MD) and visual field index (VFI) rates, Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS) scores, and pointwise linear regression (PLR).

Main Results:

  • The functional GEE model identified the highest proportion of eyes with perimetric progression (54.4%), significantly outperforming VFI rate (34.4%), PLR (23.3%), MD rate (21.4%), CIGTS (7.9%), and AGIS (5.1%).
  • Progression was detected significantly faster using the functional GEE model compared to other methods (adjusted P≤0.019).
  • Moderate agreement was observed between the functional GEE model and the VFI rate (κ=0.47).

Conclusions:

  • The functional GEE model demonstrates superior performance in detecting perimetric progression in POAG patients.
  • This novel approach offers a shorter time to progression detection, potentially improving patient management.
  • The functional GEE model represents a promising advancement in the analysis of glaucomatous visual field changes.