Garcia Tanya P, Marder Karen
Department of Epidemiology and Biostatistics, Texas A&M University, TAMU 1266, College Station, TX, 77843-1266, USA.
Department of Neurology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
Curr Neurol Neurosci Rep. 2017 Feb;17(2):14. doi: 10.1007/s11910-017-0723-4.
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.
了解神经退行性疾病的总体进展对于治疗干预的时机选择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，在这种设计中，随着时间的推移反复测量结果，并根据危险因素进行评估，危险因素可以反复测量或在基线时测量。纵向数据使研究人员能够评估疾病的时间方面，但分析因复杂的相关结构、不规则间隔的访视、缺失数据以及时变和静态协变量效应的混合而变得复杂。我们回顾了针对这些挑战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应对了这些挑战，并且在观察性和临床研究中受到美国食品药品监督管理局的青睐。亨廷顿舞蹈症研究的例子用于说明，但这些方法一般适用于神经退行性疾病，特别是随着通过敏感生物标志物识别神经退行性疾病前驱形式的情况日益增多。
