Resubmission: Latent Class Trajectory Analysis in the OHTS Study

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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.