Erin I McDonnell, Shanghong Xie, Karen Marder, Fanyu Cui, Yuanjia Wang
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引用次数: 0
摘要
在这项工作中,我们提出了一些方法来研究临床症状与脑成像生物标志物之间复杂的相互关系是如何随着时间的推移而变化的,从而导致已知遗传近似确定疾病的受试者被诊断出疾病。我们提出了一种与时间相关的无向图模型,该模型可确保特定时间网络的时间和结构平滑性,以研究疾病发生时对齐的标记物之间的相互作用轨迹。具体来说,我们将受试者锚定在疾病诊断时间(锚定时间)上,就像在一个复兴过程中一样,我们在每个感兴趣的时间点上估算出相对于锚定时间的网络。为了利用所有可用数据,我们应用核权重来借用与关注时间相近的观测信息。我们引入了自适应套索权重,以鼓励边缘强度的时间平滑性,而新颖的弹性融合- l 0 $$ {l}_0 $$ 惩罚则可去除虚假边缘,鼓励网络结构的时间平滑性。我们的方法可以处理诸如访问时间不平衡等实际复杂问题。我们进行了模拟研究,将我们的方法与现有方法进行了比较。然后,我们将我们的方法应用到 PREDICT-HD 的数据中,这是一项针对亨廷顿氏病(HD)显现前患者的大型前瞻性观察研究,目的是识别 HD 临床诊断前的症状和成像网络变化。
Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.
In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused- penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington's disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.
期刊介绍:
The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.