Modeling and Predicting Osteoarthritis Progression

To model OA progression, we used eight-year joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire, clustering disease progression trajectories with a mixed-effects mixture model that was designed especially for functional data (trajectories) with missing portions. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection operator (LASSO) regression models for predicting the probabilities of belonging to each cluster. For more details, please refer to the following article:Halilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. Osteoarthritis Cartilage. 2018;26(12):1643-1650. doi:10.1016/j.joca.2018.08.003Statistical modeling was based on the following articles:James GM, Sugar CA. Clustering for Sparsely Sampled Functional Data. J Am Stat Assoc. 2003;98(462):397-408.James GM, Hastie TJ. Functional linear discriminant analysis for irregularly sampled curves. J R Stat Soc Ser B Stat Methodol. 2001;63(3):533-550.James GM, Hastie TJ, Sugar CA. Principal component models for sparse functional data. Biometrika. 2000;87(3):587-602. <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=1490#pack_2024">OA Progression Model </a> </li> </ul>

为模拟骨关节炎的进展，本研究采用了来自X射线的八年联合空间宽度测量数据以及来自西方安大略和麦克马斯特大学骨关节炎指数（WOMAC）问卷的疼痛评分。通过构建适用于具有缺失部分的函数数据（轨迹）的混合效应混合模型，对疾病进展轨迹进行聚类。在根据放射学和疼痛进展对受试者进行聚类后，我们利用收集于前一年的临床变量建立了最小绝对收缩和选择算子（LASSO）回归模型，以预测属于每个聚类的概率。更多详情请参阅以下文章：Halilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. 模拟和预测骨关节炎进展：骨关节炎倡议的数据。骨关节炎软骨。2018;26(12):1643-1650. doi:10.1016/j.joca.2018.08.003。统计建模基于以下文章：James GM, Sugar CA. 对稀疏样本功能数据进行聚类。美国统计学会杂志。2003;98(462):397-408。James GM, Hastie TJ. 用于不规则采样曲线的功能线性判别分析。皇家统计学会系列B统计方法。2001;63(3):533-550。James GM, Hastie TJ, Sugar CA. 稀疏功能数据的主成分模型。生物计量学。2000;87(3):587-602。该项目包括以下软件/数据包：
<ul>
<li><a href="https://simtk.org/frs?group_id=1490#pack_2024">骨关节炎进展模型</a></li>
</ul>