Longitudinal scalar-on-functions regression with application to tractography data
Longitudinal scalar-on-functions regression with application to tractography data
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DOI：
10.1093/biostatistics/kxs051
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发表时间：
2013-07-01
期刊：
影响因子：
2.1
通讯作者：
Greven, Sonja
中科院分区：
文献类型：
--
作者：
Gertheiss, Jan;Goldsmith, Jeff;Greven, Sonja
我们提出了一类标量函数回归的估计技术，其中可以在多次访问中观察到结果和功能预测因子。我们的方法是由纵向脑弥散张量成像纤维束成像研究的动机。该研究的主要目标之一是评估人类功能与大脑成像之间随时间推移的同期关联。这项研究的复杂性要求开发能够同时纳入以下内容的方法：（1）多个功能（和标量）回归变量;（2）每个患者的纵向结果和预测指标;（3）高斯或非高斯结果;（4）功能预测指标中的缺失值。我们提出了两个版本的一种新方法，纵向功能主成分回归（PCR）。这些方法扩展了公知的功能性PCR，并允许曲线中的受试者特异性趋势和与该趋势的访视特异性偏差的不同影响。新的方法与现有的方法进行了比较，最有前途的技术用于分析纤维束成像数据。
We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study's primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.