September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Detecting Glaucomatous Progression from Localized Visual Function Changes with Corrections for Multiple Comparison
Author Affiliations & Notes
  • Srinivas Rajaraman
    Electrical and Computer Engineering, The University of Memphis, Memphis, Tennessee, United States
  • Rupesh Singh
    Electrical and Computer Engineering, The University of Memphis, Memphis, Tennessee, United States
  • Siamak Yousefi
    Shiley Eye Institute, University of California, San Diego, California, United States
  • Christopher Bowd
    Shiley Eye Institute, University of California, San Diego, California, United States
  • Linda M Zangwill
    Shiley Eye Institute, University of California, San Diego, California, United States
  • Robert N Weinreb
    Shiley Eye Institute, University of California, San Diego, California, United States
  • Madhusudhanan Balasubramanian
    Electrical and Computer Engineering, The University of Memphis, Memphis, Tennessee, United States
  • Footnotes
    Commercial Relationships   Srinivas Rajaraman, None; Rupesh Singh, None; Siamak Yousefi, None; Christopher Bowd, None; Linda Zangwill, Carl Zeiss Meditec Inc (F), Carl Zeiss Meditec Inc (R), Heidelberg Engineering GmbH (F), Optovue Inc (F), Optovue Inc (R), Quark (F), Topcon Medical Systems Inc (F); Robert Weinreb, Alcon (C), Allergan (C), Amatek (C), Bausch+Lomb (C), Carl Zeiss Meditec (C), Carl Zeiss Meditec (F), Carl Zeiss Meditec (R), Forsight (C), Genentech (F), Heidelberg Engineering (F), Konan (F), National Eye Institute (F), Neurovision (F), Optovue (F), Quark (F), Reichert (F), Tomey (F), Topcon (C), Topcon (F), Valeant (C); Madhusudhanan Balasubramanian, None
  • Footnotes
    Support  R00 EY020518 (MB) EY11008, EY019869, P30 EY022589 (LMZ) EY022039 (CB) EY023704, K12EY024225, Research to Prevent Blindness
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3919. doi:
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      Srinivas Rajaraman, Rupesh Singh, Siamak Yousefi, Christopher Bowd, Linda M Zangwill, Robert N Weinreb, Madhusudhanan Balasubramanian; Detecting Glaucomatous Progression from Localized Visual Function Changes with Corrections for Multiple Comparison. Invest. Ophthalmol. Vis. Sci. 2016;57(12):3919.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To detect glaucomatous progression from localized visual function changes with type I error controlled in a nonparametric framework (PixR) and compare to the Permutation Analysis of Pointwise Linear Regression (PoPLR) method.

Methods : The rate of visual threshold sensitivity at each of the Standard Automated Perimetry (SAP) retinal test locations were estimated using linear regression. For nonparametric analysis, regression errors were assumed to be independent and identically distributed (exchangeability criterion). The significance of rate of change (p-value) in each location was estimated using permutation tests with Monte Carlo sampling while accounting for multiple simultaneous comparison problem using Bonferroni correction. Using these p-values, glaucoma progression was detected once again nonparametrically based on the significance (at a level of 5%) of the observed number of progressing locations. Study eyes with at least 4 SAP exams from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS) were included. 80 eyes of 74 participants were progressing based on stereophoto evaluation; and in 84 eyes of 45 participants all SAP measurements were within 3 months (stable group).

Results : Sensitivity (95% CI) of PixR and PoPLR were 64% (53%, 75%) and 50% (38%, 62%) respectively. Specificity (95% CI) of PixR and PoPLR were 98% (94%, 100%) and 94% (88%, 100%).

Conclusions : With high specificity, correction for multiple comparison using Bonferroni correction provided a high diagnostic accuracy of detecting glaucomatous progression. While Bonferroni correction is generally conservative, it provided an optimal diagnostic accuracy due to relatively fewer number of locations simultaneously tested.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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