Resubmission: Latent Class Trajectory Analysis in the OHTS Study

眼压治疗研究(OHTS)是一项纵向研究，测试局部眼部 降压药物可延缓或预防原发性开角型青光眼(POAG)的发病。刻画 视野变化的纵向轨迹及危险因素对轨迹的影响具有较高的公开性 对健康有重要意义。视力丧失的进展在患者之间有很大的不同。因此，它是 重要的是要识别更有可能进展迅速的患者，以防止不可逆转的视力丧失 功能。另一方面，对于青光眼保持稳定的患者，应避免过度治疗，以 减少成本、副作用和不便。例如，在OHTS中，将 轨迹分成几组，进展速度不同。在这个提案中，我们将使用潜在的 类混合模型(LCMM)来刻画视野变化轨迹中的异质性。LCMM是一种 数据驱动的分类方法，不像在其他模型中那样预先指定每个潜在类的切入点。 此外，LCMM是非常全面的，因为它包括几种常用的方法作为特殊的 案子。我们将扩展LCMM以解决视野轨迹建模中的一些具有挑战性的问题 在OHTS中：(1)建立了平均偏差(MD)的潜在类联合模型，MD是视力损失的定量度量 视野)轨迹预测时间到POAG；(2)变量使用(稀疏组)套索方法 选择在精度和效率之间取得适当平衡；(3)MD和其他模型的联合模型 POAG转换前疾病进展的测量，例如模式标准偏差轨迹 同时预测POAG的时间；(4)利用泛函考虑视野的逐点回归 主成分分析。我们对LCMM的扩展将更好地捕获 青光眼患者平均偏差和促进循证精确医学的时间趋势 眼科和其他医疗条件。

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.