Modeling normative kinetic perimetry isopters using mixedeffects quantile regression

Abstract

Kinetic perimetry is used to quantify visual field size/ sensitivity. Clinically, perimetry can be used to diagnose and monitor ophthalmic and neuro-ophthalmic disease. Normative data are integral to the interpretation of these findings. However, there are few computational developments that allow clinicians to collect and analyze normative data from kinetic perimeters. In this article we describe an approach to fitting kinetic responses using linear quantile mixed models. Analogously to traditional linear mixed-effects models for the mean, linear quantile mixed models account for repeated measurements taken from the same individual, but differently from linear mixed-effects models, they are more flexible as they require weaker distributional assumptions and allow for quantile-specific inference. Our approach improves on parametric alternatives based on normal assumptions. We introduce the R package kineticF, a freely available and open-access resource for the analysis of perimetry data. Our proposed approach can be used to analyze normative data from further studies.