A spatially varying change points model for monitoring glaucoma progression using visual field data.

@article{Berchuck2019ASV,
  title={A spatially varying change points model for monitoring glaucoma progression using visual field data.},
  author={Samuel I. Berchuck and Jean-Claude Mwanza and Joshua L. Warren},
  journal={Spatial statistics},
  year={2019},
  volume={30},
  pages={
          1-26
        }
}

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References

SHOWING 1-10 OF 61 REFERENCES
Spatial modeling of visual field data for assessing glaucoma progression.
TLDR
By using CAR priors, the spatial correlation in the eye is modeled by using a novel method that intrinsically accounts for the large variation of VF data and is apparently superior to point-wise linear regression methods.
Diagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method
TLDR
A spatiotemporal boundary detection model is introduced that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time and provides novel insight into vision loss.
A Statistical Model to Analyze Clinician Expert Consensus on Glaucoma Progression using Spatially Correlated Visual Field Data
TLDR
A statistical model for the detection of VF progression defined by clinician expert consensus that accounts for spatially correlated changes in visual sensitivity over time is developed and showed that it outperformed competing models in a number of areas.
Detecting Changes in Retinal Function: Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS)
TLDR
ANSWERS is a new efficient method for detecting changes in retinal function that allows for better detection of change, more efficient endpoints and can potentially shorten the time in clinical trials for new therapies.
Global Visit Effects in Point-Wise Longitudinal Modeling of Glaucomatous Visual Fields.
TLDR
By incorporating the GVE in the longitudinal modeling of VF data, better estimates may be obtained of the rate of progression as well as of predicted future sensitivities.
Analysis of visual field progression in glaucoma.
TLDR
This new technique, which combines the change in perimetric sensitivity over time with colour coding of significant change into one image may provide an efficient method to detect true progression in glaucomatous field loss.
A Bayesian semiparametric approach with change points for spatial ordinal data
TLDR
A Bayesian hierarchical spatial model with change points for spatial ordinal data to detect the unknown threshold effects of soil chemical exposures associated with the risk of intellectual disability in children is proposed.
Longitudinal changes in the visual field and optic disc in glaucoma
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5
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