Resubmission: Latent Class Trajectory Analysis in the OHTS Study

重新提交：OHTS 研究中的潜在类别轨迹分析

基本信息

批准号：
10540153
负责人：
MAE O GORDON
金额：
$ 19.65万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10540153
关键词：
Acquired Immunodeficiency Syndrome Address Affect African American population African ancestry American Blindness Cancer Patient Characteristics Classification Computer software Data Data Set Diagnostic Disease Progression Equilibrium Evaluation Studies Event Eye Glaucoma Growth Heterogeneity Incidence Institutes Joints Lasso Letters Longitudinal Studies Longitudinal trends Malignant neoplasm of prostate Measures Medical Methods Modeling Ocular Hypertension Ophthalmology Outcome Participant Patients Pattern Pharmaceutical Preparations Phase Primary Open Angle Glaucoma Principal Component Analysis Prostate-Specific Antigen Public Health Quality of life Radiation therapy Research Personnel Risk Factors Specific qualifier value Structure Testing Time Time trend United States Vision Visual Fields Work aged aggressive therapy base cost efficacy evaluation evidence base follow-up high risk hypertension treatment individual variation innovation interest ocular hypotensive overtreatment patient subsets precision medicine prevent randomized trial side effect software development unnecessary treatment user-friendly

项目摘要

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.

高眼压治疗研究 (OHTS) 是一项纵向研究，测试局部眼压治疗是否有效降血压药物可延迟或预防原发性开角型青光眼 (POAG) 的发作。表征视野变化的纵向轨迹以及危险因素对该轨迹的影响具有较高的公众性健康意义。视力丧失的进展因患者而异。因此，它是识别更有可能快速进展的患者很重要，以防止不可逆转的视力丧失功能。另一方面，对于青光眼病情稳定的患者，应避免过度治疗。降低成本、副作用和不便。例如，在 OHTS 中，将轨迹分为几个具有不同进展速率的组。在这个提案中，我们将利用潜在的类混合模型（LCMM）来表征视野变化轨迹的异质性。 LCMM 是一个数据驱动的分类方法，无需像其他模型那样预先指定每个潜在类别的切点。此外，LCMM 非常全面，因为它包括几种常用的方法作为特殊方法案例。我们将扩展 LCMM 以解决视野轨迹建模中的许多具有挑战性的问题在 OHTS 中：(1) 开发平均偏差的潜在类别联合模型（MD，视力丧失的定量测量）视野）预测 POAG 时间的轨迹；（2）对变量使用（稀疏组）Lasso方法选择以达到准确性和效率之间的适当平衡； (3)MD与其他的联合模型 POAG 转换之前疾病进展的测量，例如模式标准偏差轨迹同时预测POAG时间； (4) 考虑使用函数进行视野的逐点回归主成分分析。我们对 LCMM 的扩展将更好地捕捉个体异质性平均偏差的时间趋势并促进青光眼的循证精准医学眼科和其他医疗状况。