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    Area of Science:

    • Ophthalmology and Computational Statistics
    • Visual field analysis and disease diagnostics

    Background:

    • Kinetic perimetry quantifies visual field size and sensitivity for diagnosing and monitoring ophthalmic and neuro-ophthalmic diseases.
    • Analyzing normative data from kinetic perimeters is crucial but lacks computational tools.
    • Existing methods often rely on restrictive normal distribution assumptions.

    Purpose of the Study:

    • To describe a novel computational approach for fitting kinetic perimetry responses.
    • To introduce the R package kineticF for accessible perimetry data analysis.
    • To provide a flexible alternative to traditional parametric methods for normative data analysis.

    Main Methods:

    • Application of linear quantile mixed models to fit kinetic perimetry responses.
    • Accounting for repeated measurements within individuals using mixed-effects modeling.
    • Utilizing weaker distributional assumptions for more flexible quantile-specific inference.

    Main Results:

    • Demonstration of an improved approach over parametric methods based on normal assumptions.
    • Development of the R package kineticF as a freely available resource.
    • The proposed method enhances the analysis of normative data from kinetic perimetry studies.

    Conclusions:

    • Linear quantile mixed models offer a flexible and robust method for analyzing kinetic perimetry data.
    • The kineticF R package provides a valuable, open-access tool for clinical and research applications.
    • This approach facilitates more accurate interpretation of visual field sensitivity and disease progression.