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Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma

Wonyul Lee1, Michelle F Miranda1, Philip Rausch2

  • 1Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX 77230.

Journal of the American Statistical Association
|June 26, 2019
PubMed
Summary
This summary is machine-generated.

Scleral strain, a factor in glaucoma, decreases with age, potentially altering optic nerve head biomechanics. This study developed new methods to analyze eye strain data, offering insights into glaucoma

Keywords:
Bayesian modelsFunctional data analysisFunctional mixed modelsFunctional regressionGlaucomaLongitudinal Functional DataNonparametric effectsSmoothing SplinesSpherical dataWavelets

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Statistical Modeling

Background:

  • Glaucoma is a leading cause of blindness, characterized by optic nerve damage linked to intraocular pressure (IOP).
  • The complete etiology of glaucoma remains unknown.
  • Existing methods for measuring scleral strain are limited.

Purpose of the Study:

  • To investigate the relationship between age, intraocular pressure (IOP), and scleral strain.
  • To assess how scleral strain varies across the posterior pole of the eye.
  • To develop advanced statistical models for analyzing complex ocular biomechanical data.

Main Methods:

  • Development of a custom device for continuous scleral strain measurement.
  • Adaptation of Bayesian Functional Mixed Models for analyzing correlated functional data on a spherical surface.
  • Incorporation of nonparametric age effects, multi-level random effects, and functional growth curves.

Main Results:

  • Scleral strain was found to decrease with increasing age.
  • Strain variation was analyzed across different IOP levels and ocular regions.
  • The developed statistical models provided robust inference on scleral biomechanics.

Conclusions:

  • Age-related changes in scleral strain may contribute to the biomechanical alterations leading to glaucoma.
  • The novel statistical framework offers a flexible approach for analyzing complex, high-dimensional functional data in various scientific fields.
  • Findings provide new insights into the biomechanical etiology of glaucoma.