Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models.

ABSTRACT:

Purpose

Develop a hierarchical longitudinal regression model for estimating local rates of change of macular ganglion cell complex (GCC) measurements with optical coherence tomography (OCT).

Methods

We enrolled 112 eyes with four or more macular OCT images and ≥2 years of follow-up. GCC thickness measurements within central 6 × 6 superpixels were extracted from macular volume scans. We fit data from each superpixel separately with several hierarchical Bayesian random-effects models. Models were compared with the Watanabe-Akaike information criterion. For our preferred model, we estimated population and individual slopes and intercepts (baseline thickness) and their correlation.

Results

Mean (SD) follow-up time and median (interquartile range) baseline 24-2 visual fiel

SUBMITTER: Mohammadzadeh V 

PROVIDER: S-EPMC8054624 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature