# Nonparametric and Survival Methods in Ophthalmology

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $451,134

## Abstract

Ophthalmic data is of necessity bivariate. Also, many scales used in ophthalmology are ordinal in nature. For
example, the ETDRS diabetic retinopathy scale is an ordinal scale ranging from 10 = no retinopathy to ≥ 60 =
proliferative diabetic retinopathy (PDR). Most commonly, the ordinal grades for the eyes of a subject are
combined into a 15 category ordinal level scale and progression is determined by a 2 or 3 level change in the
person-level scale. However, much information is lost by collapsing an eye level scale to a person-level scale
since there can be differences in retinopathy grades and progression in retinopathy for individual eyes.
Furthermore, information is also lost by categorizing change as ≥ 2 level or 3 level change rather than treating
the scale as an ordinal scale. Similar issues arise in AMD where drusen size is used to characterize subjects
with early AMD. Drusen size is usually coded as an ordinal variable and one wants to look at risk factors that
are associated with change in drusen sizes. The goal of specific aim 1 is to propose new analyses of clustered
ordinal longitudinal data that would be applicable to diabetic retinopathy scale and drusen size. The goal of
specific aim 2 is to assess risk prediction for ophthalmic endpoints such as AMD using the eye as the unit of
analysis. A common method used to assess discrimination of risk prediction rules is the area under the ROC
curve (or C statistic). However, most literature concerning estimating the C statistic is based on the person as
the unit of analysis where each person is considered an independent unit of analysis. In the previous cycle of
this grant we developed methods for assessing AUC for ophthalmic endpoints using the eye as the unit of
analysis, based on logistic regression models where each person is followed for the same length of time. In
specific aim 2, we now propose to extend this method to the case of familial data such as in the Seddon
longitudinal cohort where there can be several people in the same family and one has two levels of nesting for
person within family and eye within person. Another issue in risk prediction is that in some studies subjects are
followed for variable lengths of time (such as the AREDS study). There are extensions proposed of the C
statistic that are applicable to survival analysis, but they use the person as the unit of analysis. In specific aim
3, we propose to extend these methods based on the eye as the unit of analysis. Finally, in specific aim 4 we
propose to continue our work on translating statistical methods for clustered data to the ophthalmologic
community. This would be achieved by a combination of (a) papers (b) posters and/or presentations at
important ophthalmologic meetings (c) giving short courses on Intermediate Statistical Methods at ARVO.
Although there has been some movement towards using the eye as the unit of analysis in ophthalmology, the
issue is frequently ignored and people are often presenting resul...

## Key facts

- **NIH application ID:** 9928929
- **Project number:** 5R01EY022445-06
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Bernard A Rosner
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $451,134
- **Award type:** 5
- **Project period:** 2013-09-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9928929

## Citation

> US National Institutes of Health, RePORTER application 9928929, Nonparametric and Survival Methods in Ophthalmology (5R01EY022445-06). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9928929. Licensed CC0.

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