# Resubmission: Latent Class Trajectory Analysis in the OHTS Study

> **NIH NIH R21** · WASHINGTON UNIVERSITY · 2022 · $196,458

## Abstract

The Ocular Hypertension Treatment Study (OHTS) is a longitudinal study that tested whether topical ocular
hypotensive medication delays or prevents the onset of primary open-angle glaucoma (POAG). Characterizing
the longitudinal trajectory of visual field change and the effect of risk factors on the trajectory has high public
health significance. The progression of visual loss varies substantially from patient to patient. Therefore, it is
important to identify patients who are more likely to progress rapidly to prevent irreversible loss of visual
function. On the other hand, for patients whose glaucoma remains stable, over-treatment should be avoided to
reduce costs, side-effects, and inconvenience. For example, in OHTS, it would be of interest to classify the
trajectories into several groups with different rates of progression. In this proposal we will employ the latent
class mixed model (LCMM) to characterize the heterogeneity in the trajectory of visual field change. LCMM is a
data-driven classification approach, without pre-specifying the cut-point of each latent class as in other models.
Furthermore, LCMM is very comprehensive in that it includes several commonly used approaches as special
cases. We will extend the LCMM to address a number of challenging issues in modeling visual field trajectory
in OHTS: (1) develop a latent class joint model of mean deviation (MD, a quantitative measure of vision loss in
the visual field) trajectory to predict time to POAG; (2) use the (sparse group) Lasso method for variable
selection to achieve a proper balance between accuracy and efficiency; (3) jointly model of MD and other
measures of disease progression prior to POAG conversion, e.g., pattern standard deviation trajectories
simultaneously to predict time of POAG; (4) consider pointwise regression of visual field using functional
principal component analysis. Our extensions to LCMM will better capture individual heterogeneity in the
temporal trends of mean deviation and facilitate evidence-based precision medicine in glaucoma, in
ophthalmology, and other medical conditions.

## Key facts

- **NIH application ID:** 10540153
- **Project number:** 1R21EY033518-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** MAE O GORDON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $196,458
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10540153

## Citation

> US National Institutes of Health, RePORTER application 10540153, Resubmission: Latent Class Trajectory Analysis in the OHTS Study (1R21EY033518-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10540153. Licensed CC0.

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