Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington's Disease as a Model.
Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington's Disease as a Model.
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DOI：
10.1007/s11910-017-0723-4
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发表时间：
2017-03
影响因子：
5.6
通讯作者：
Marder K
中科院分区：
文献类型：
--
作者：
Garcia TP;Marder K
了解神经退行性疾病的总体进展对于治疗干预的时机和有效临床试验的设计至关重要。疾病进展可以通过纵向研究设计进行评估，其中随着时间的推移重复测量结局，并根据风险因素进行评估，重复测量或在基线时进行评估。纵向数据使研究人员能够评估时间疾病方面，但复杂的相关结构，不规则间隔的访问，缺失的数据以及时变和静态协变量效应的混合物使分析变得复杂。我们回顾了为应对这些挑战而设计的现代统计方法。在所有方法中，混合效应模型最灵活地适应挑战，并且是FDA在观察性和临床研究中的首选。来自亨廷顿氏病研究的实例用于澄清，但该方法一般适用于神经变性疾病，特别是随着通过敏感的生物标志物鉴定神经变性疾病的前驱形式的增加。
Understanding the overall progression of neurodegenerative diseases is critical to the timing of therapeutic interventions and design of effective clinical trials. Disease progression can be assessed with longitudinal study designs in which outcomes are measured repeatedly over time and are assessed with respect to risk factors, either measured repeatedly or at baseline. Longitudinal data allows researchers to assess temporal disease aspects, but the analysis is complicated by complex correlation structures, irregularly spaced visits, missing data, and mixtures of time-varying and static covariate effects. We review modern statistical methods designed for these challenges. Among all methods, the mixed effect model most flexibly accommodates the challenges and is preferred by the FDA for observational and clinical studies. Examples from Huntington’s disease studies are used for clarification, but the methods apply to neurodegenerative diseases in general, particularly as the identification of prodromal forms of neurodegenerative disease through sensitive biomarkers is increasing.
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影响因子：
2
作者：
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通讯作者：
Sangalli, Laura M.
DOI：
10.1080/13803395.2011.574606
发表时间：
2011-01-01
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