Potential disease-modifying therapies for neurodegenerative disorders need to be introduced prior to the symptomatic stage in order to be effective. However, current diagnosis of neurological disorders mostly rely on measurements of clinical symptoms and thus only identify symptomatic subjects in their late disease course. Thus, it is of interest to select and integrate biomarkers that may reflect early disease-related pathological changes for earlier diagnosis and recruiting pre-sypmtomatic subjects in a prevention clinical trial. Two sources of biological information are relevant to the construction of biomarker signatures for time to disease onset that is subject to right censoring. First, biomarkers' effects on disease onset may vary with a subject's baseline disease stage indicated by a particular marker. Second, biomarkers may be connected through networks, and their effects on disease may be informed by this network structure. To leverage these information, we propose a varying-coefficient hazards model to induce double smoothness over the dimension of the disease stage and over the space of network-structured biomarkers. The distinctive feature of the model is a non-parametric effect that captures non-linear change according to the disease stage and similarity among the effects of linked biomarkers. For estimation and feature selection, we use kernel smoothing of a regularized local partial likelihood and derive an efficient algorithm. Numeric simulations demonstrate significant improvements over existing methods in performance and computational efficiency. Finally, the methods are applied to our motivating study, a recently completed study of Huntington's disease (HD), where structural brain imaging measures are used to inform age-at-onset of HD and assist clinical trial design. The analysis offers new insights on the structural network signatures for premanifest HD subjects.

中文翻译：

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使用多维网络结构的生物标记物构建疾病发作特征。

为了有效，需要在有症状的阶段之前引入潜在的神经退行性疾病的疾病改良疗法。然而，当前对神经系统疾病的诊断主要依赖于临床症状的测量，因此仅在其晚期疾病过程中识别有症状的受试者。因此，在预防性临床试验中选择和整合可能反映早期疾病相关病理变化的生物标记物以进行早期诊断和招募有症状前受试者的研究是有意义的。生物学信息的两个来源与疾病发作时间的生物标记签名的构建有关，该疾病需经过正确的审查。首先，生物标志物对疾病发作的影响可能随特定标志物指示的受试者基线疾病阶段而变化。第二，生物标志物可以通过网络连接，并且其对疾病的影响可以通过这种网络结构得知。为了利用这些信息，我们提出了一种变系数危害模型，以在疾病阶段和网络结构生物标记物的空间上引起双重平滑度。该模型的显着特征是非参数效应，可根据疾病阶段和链接的生物标记物的效应之间的相似性捕获非线性变化。为了进行估计和特征选择，我们使用正则化局部偏似度的核平滑方法，并得出一种有效的算法。数值模拟显示出在性能和计算效率上比现有方法有了显着改进。最后，这些方法被应用到我们的激励研究中，亨廷顿舞蹈病（HD）的一项最近完成的研究，其中使用结构性脑成像措施来告知HD发病年龄并协助临床试验设计。该分析提供了有关预高清对象的结构网络签名的新见解。