Ophthalmic data is of necessity bivariate. Important information is lost when eye-specific outcome and exposure data are collapsed into person-specific scores. This necessitates adjustment to standard inferential methods to account for clustering. For example, mixed effects regression models are commonly used to model normally distributed longitudinal data, but require modification when clustering exists both among fellow eyes and repeat visits for an individual. However, many ocular measures are not normally distributed and nonparametric methods of longitudinal analysis are needed. We also consider nonparametric methods in the context of confounding by eye-specific covariates where a subject may be in different strata defined by confounders for the left and right eye. These are the goals of specific aim 1. Secondly, there have been major advances in risk prediction for AMD with the discovery of important genetic predictors. However, commonly used measures of discrimination and calibration of risk prediction rules require adjustment for correlated data. Furthermore, risk factors may vary by stage of maculopathy. This is the goal of specific aim 2.
In specific aim 3, we seek to use empirical Bayes methods to better predict disease course for individual RP patients, where the number of follow-up visits and duration of follow-up differs for individual patients.
In specific aim 3, we propose innovative techniques for disseminating information on correlated data methods to the ophthalmic community including periodic newsletters to NEI clinical trial investigators, giving education courses at ARVO and writing review papers on correlated data methods for ophthalmic journals.

Public Health Relevance

This proposal will consider (a) more efficient use of longitudinal data in ophthalmic treatment trials with non-normal outcome variables, (b) more valid estimates of accuracy of risk prediction rules for EMD so as to identify high risk subjects for possible future intervention trials, (c) more accurate assessment of long-term disease course in RP patients and (d) innovative approaches for dissemination of correlated data methods.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Special Emphasis Panel (ZEY1-VSN (05))
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Everett, Donald F
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Brigham and Women's Hospital
United States
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Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert et al. (2018) Tutorial on Biostatistics: Statistical Analysis for Correlated Binary Eye Data. Ophthalmic Epidemiol 25:1-12
Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert et al. (2017) Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data. Ophthalmic Epidemiol 24:130-140
Merle, Bénédicte M J; Silver, Rachel E; Rosner, Bernard et al. (2017) Associations Between Vitamin D Intake and Progression to Incident Advanced Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci 58:4569-4578
Rosner, Bernard; Glynn, Robert J (2017) Estimation of rank correlation for clustered data. Stat Med 36:2163-2186
Seddon, Johanna M; Silver, Rachel E; Kwong, Manlik et al. (2015) Risk Prediction for Progression of Macular Degeneration: 10 Common and Rare Genetic Variants, Demographic, Environmental, and Macular Covariates. Invest Ophthalmol Vis Sci 56:2192-202
Merle, Bénédicte M J; Silver, Rachel E; Rosner, Bernard et al. (2015) Adherence to a Mediterranean diet, genetic susceptibility, and progression to advanced macular degeneration: a prospective cohort study. Am J Clin Nutr 102:1196-206
Ferrara, Daniela; Seddon, Johanna M (2015) Phenotypic Characterization of Complement Factor H R1210C Rare Genetic Variant in Age-Related Macular Degeneration. JAMA Ophthalmol 133:785-91
Rosner, Bernard; Qiu, Weiliang; Lee, Mei-Ling T (2013) Assessing discrimination of risk prediction rules in a clustered data setting. Lifetime Data Anal 19:242-56
Glynn, Robert J; Rosner, Bernard (2012) Regression methods when the eye is the unit of analysis. Ophthalmic Epidemiol 19:159-65