For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of the disease. However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying-coefficient regression. However, this is challenging when there is a pre-specified group structure among variables. We propose a novel variable selection method for a varying-coefficient regression with such structured variables and provide a publicly available R package svreg for implementation of our method. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease severity.

中文翻译：

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svReg：结构变系数回归，以区分区域脑萎缩如何影响亨廷顿病严重程度组的运动障碍

对于亨廷顿病，识别与运动障碍相关的大脑区域可用于开发干预措施以缓解运动症状，该疾病的主要症状。然而，对于不同的患者群体，大脑区域对运动障碍的影响可能会有所不同。因此，我们的兴趣不仅在于识别大脑区域，还在于了解它们对运动障碍的影响如何因患者群体而异。这可以作为变系数回归的模型选择问题。但是，当变量之间存在预先指定的组结构时，这是具有挑战性的。我们提出了一种新颖的变量选择方法，用于具有此类结构化变量的变系数回归，并提供了一个公开可用的 R 包svreg用于实现我们的方法。我们的方法被经验证明可以一致地选择相关变量。此外，我们的方法比现有方法更好地筛选无关变量。因此，我们的方法导致模型比现有方法具有更高的灵敏度、更低的错误发现率和更高的预测精度。最后，我们发现大脑区域对运动障碍的影响因患者的疾病严重程度而异。据我们所知，我们的研究首次确定了疾病严重程度和大脑区域之间的这种相互作用效应，这表明需要根据疾病严重程度进行定制干预。