Functional Mixed-Effects Modeling of Longitudinal Duchenne Muscular Dystrophy Electrical Impedance Myography Data Using State-Space Approach
Functional Mixed-Effects Modeling of Longitudinal Duchenne Muscular Dystrophy Electrical Impedance Myography Data Using State-Space Approach
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DOI：
10.1109/tbme.2018.2879227
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发表时间：
2019-06-01
影响因子：
4.6
通讯作者：
Selukar, Rajesh
中科院分区：
文献类型：
--
作者：
Kapur, Kush;Sanchez, Benjamin;Selukar, Rajesh
目的：电阻抗肌动描记（EIM）是评估肌肉状态的定量和客观工具。 EIM 提供了取代传统的身体功能评分或生活质量测量的可能性，这些测量取决于患者的合作和情绪。方法：在这里，我们提出了一个功能混合效应模型，使用状态空间方法来描述对 16 名杜氏肌营养不良症男孩和 12 名健康对照者测量的 EIM 数据的反应轨迹，两组均在两年内进行测量。提出的建模框架对每次访问时收集的 EIM 数据施加平滑样条结构，并考虑到这些曲线沿纵向测量的受试者内部相关性。建模框架以状态空间方法重新构建，从而允许使用计算效率高的扩散卡尔曼滤波和平滑算法进行模型估计，以及对后验方差-协方差矩阵进行估计，以构建贝叶斯 95% 置信带。结果：所提出的模型使我们能够同时调整基线变量，区分平滑功能反应的纵向变化，并估计受试者和受试者时间与群体平均反应曲线的特定偏差。该代码在补充材料中公开提供。意义：所提出的建模方法将有可能增强 EIM 的能力，使其成为 DMD 和其他临床试验中测试治疗效果的生物标志物。
Objective: Electrical impedance myography (EIM) is a quantitative and objective tool to evaluate muscle status. EIM offers the possibility to replace conventional physical functioning scores or quality of life measures, which depend on patient cooperation and mood. Methods: Here, we propose a functional mixed-effects model using a state-space approach to describe the response trajectories of EIM data measured on 16 boys with Duchenne muscular dystrophy and 12 healthy controls, both groups measured over a period of two years. The modeling framework presented imposes a smoothing spline structure on EIM data collected at each visit and taking into account of within subject correlations of these curves along the longitudinal measurements. The modeling framework is recast in a state-space approach, thereby allowing for the employment of computationally efficient diffuse Kalman filtering and smoothing algorithms for the model estimation, as well as the estimates of the posterior variance-covariance matrix for the construction of the Bayesian 95% confidence bands. Results: The proposed model allows us to simultaneously adjust for baseline variables, differentiate the longitudinal changes in the smooth functional response and estimate the subject and subject-time specific deviations from the population-averaged response curves. The code is made publicly available in the supplementary material. Significance: The modeling approach presented will potentially enhance EIM capability to serve as a biomarker for testing therapeutic efficacy in DMD and other clinical trials.