Improved Estimation of Rates of Glaucoma Progression Using Spatial Two-Fold Linear Mixed Models.

Authors

Chen Zhao, Swarup S Swaminathan, J Sunil Rao

Published in

Journal of statistical theory and applications : JSTA. Volume 25. Issue 1. Pages 36. Epub Sep 15, 2026.

Abstract

Visual field testing is essential for detecting glaucomatous damage. Traditional approaches such as pointwise linear regression often fail to account for the spatial correlations and hierarchical structure among test locations. We propose a two-fold linear mixed model that incorporates both eye-level and within-eye cluster-level random effects. Using the Bascom Palmer Glaucoma Repository, we show that our model outperforms conventional pointwise linear regression and permutation-based pointwise regression. In simulation studies, the proposed method yields markedly lower mean squared error than ordinary least squares, achieving MSEs of 0.41 (lower eye) and 0.50 (upper eye), compared with 1.12 and 1.72, respectively, while also reducing false-positive and false-negative rates. This framework provides improved precision and interpretability for quantifying visual field progression in glaucoma.

PMID:
42750724
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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