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Objective: Central corneal thickness influences intraocular pressure (IOP) measurement. We examined the central corneal thickness of subjects in the Ocular Hypertension Treatment Study (OHTS) and determined if central corneal thickness is related to race., Design: Cross-sectional study., Participants: One thousand three hundred one OHTS subjects with central corneal thickness measurements., Intervention: Central corneal thickness was determined with ultrasonic pachymeters of the same make and model at all clinical sites of the OHTS., Main Outcome Measures: Correlation of mean central corneal thickness with race, baseline IOP, refraction, age, gender, systemic hypertension, and diabetes., Results: Mean central corneal thickness was 573.0 ± 39.0 μm. Twenty-four percent of the OHTS subjects had central corneal thickness > 600 μm. Mean central corneal thickness for African American subjects (555.7 ± 40.0 μm; n = 318) was 23 μm thinner than for white subjects (579.0 ± 37.0 μm; P < 0.0001). Other factors associated with greater mean central corneal thickness were younger age, female gender, and diabetes., Conclusions: OHTS subjects have thicker corneas than the general population. African American subjects have thinner corneas than white subjects in the study. The effect of central corneal thickness may influence the accuracy of applanation tonometry in the diagnosis, screening, and management of patients with glaucoma and ocular hypertension., (Copyright © 2020. Published by Elsevier Inc.)

Background: Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average., Methods: Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data., Results: There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples., Conclusion: Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Primary open angle glaucoma (POAG) is a chronic, progressive, irreversible, and potentially blinding optic neuropathy. The risk of blindness due to progressive visual field (VF) loss varies substantially from patient to patient. Early identification of those patients destined to rapid progressive visual loss is crucial to prevent further damage. In this article, a latent class growth model (LCGM) was developed to predict the binary outcome of VF progression using longitudinal mean deviation (MD) and pattern standard deviation (PSD). Specifically, the trajectories of MD and PSD were summarized by a functional principal component (FPC) analysis, and the estimated FPC scores were used to identify subgroups (latent classes) of individuals with distinct patterns of MD and PSD trajectories. Probability of VF progression for an individual was then estimated as weighted average across latent classes, weighted by posterior probability of class membership given baseline covariates and longitudinal MD/PSD series. The model was applied to the participants with newly diagnosed POAG from the Ocular Hypertension Treatment Study (OHTS), and the OHTS data was best fit by a model with 4 latent classes. Using the resultant optimal LCGM, the OHTS participants with and without VF progression could be accurately differentiated by incorporating longitudinal MD and PSD.