June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Understanding the relationship between longitudinal series of structural and functional measurements by time series analysis
Author Affiliations & Notes
  • Fang-I Chu
    Ophthalmology, Glick Eye Institute, Indiana University SOM, Indianapolis, Indiana, United States
  • Lyne Racette
    Ophthalmology, Glick Eye Institute, Indiana University SOM, Indianapolis, Indiana, United States
  • Footnotes
    Commercial Relationships   Fang-I Chu, None; Lyne Racette, None
  • Footnotes
    Support  This project was supported in part by the National Institutes of Health grant EY025756, the BrightFocus Foundation grant G2014096, IUPUI DRIVE grant, and by an unrestricted grant from Research to Prevent Blindness. The DIGS and ADAGES studies were supported by P30EY022589 Eyesight Foundation of Alabama; Alcon Laboratories Inc.; Allergan Inc.; Pfizer Inc.; Merck Inc.; Santen Inc.; and the Edith C. Blum Research Fund of the New York Glaucoma Research Institute, New York, NY, Unrestricted grant from Research to Prevent Blindness, New York, New York
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 5834. doi:
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      Fang-I Chu, Lyne Racette; Understanding the relationship between longitudinal series of structural and functional measurements by time series analysis. Invest. Ophthalmol. Vis. Sci. 2017;58(8):5834.

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

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Abstract

Purpose : To characterize and assess variations in the relationship between longitudinal series of structural and functional measurements among subjects.

Methods : 120 eyes of 120 patients with ocular hypertension or primary open-angle glaucoma were selected from the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study. All patients had 11 visits separated by at least 3 months over a period of 5 to 10 years. At each visit, rim area (RA) and mean sensitivity (MS) measurements were taken within 30 days of each other. The relationship between the RA and MS series was summarized using cross-correlation function (CCF) in time series analysis. CCF measured the strength of the correlation of the longitudinal series of RA and MS at all possible visit lags. Visit lag was defined as shifts in visits between the RA and MS series, e.g. the RA series was shifted by n visits compared to the MS series. CCF identified the number of visit lags that yielded the strongest correlation for each patient. The number of visit lags was used to categorize patients into three groups: 1) RA precedes MS series (lag<0), 2) MS precedes RA series (lag>0), and 3) RA and MS series evolve simultaneously (lag=0). Regression analysis with lagged variable was conducted to verify the existence of variations in the relationship between RA and MS within three groups of patients.

Results : In different patients, the strongest correlation was obtained when RA preceded the MS series (n=50, mean visit lag = -2.94), when MS preceded the RA series (n=54, mean visit lag= 3.35), or when the RA and MS series evolved simultaneously (n=16, mean visit lag = 0). The absolute correlations ranged from 0.26 to 0.78, 0.32 to 0.88 and 0.36 to 0.69, respectively. The number of lags for all patients ranged from -7 to +7 visits. Significant differences (p<0.0001) between patients within the three groups were identified in the relationship between RA and MS using the average visit lags with regression analysis with lagged variable.

Conclusions : The relationship between the longitudinal series of RA and MS varies among patients in both correlation strength and the number of visit lags needed to achieve the strongest correlation. This finding suggests that the relationship between RA and MS should be addressed at the subject level when using both measurements jointly to model glaucoma progression.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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