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亨廷顿病发病的统计建模

Statistical modeling of Huntington disease onset.

作者信息

Garcia Tanya P, Marder Karen, Wang Yuanjia

机构信息

Department of Epidemiology and Biostatistics, Texas A&M Health Science Center, College Station, TX, United States.

Departments of Neurology and Psychiatry, Sergievsky Center and Taub Institute, Columbia University Medical Center, New York, NY, United States.

出版信息

Handb Clin Neurol. 2017;144:47-61. doi: 10.1016/B978-0-12-801893-4.00004-3.

Abstract

Huntington disease (HD) is caused by a CAG trinucleotide expansion in the huntingtin gene. We now have the power to predict age-at-onset from subject-specific features like motor and neuroimaging measures. In clinical trials, properly modeling onset age is important, because it improves power calculations and directs clinicians to recruit subjects with certain features. The history of modeling onset, from simple linear and logistic regression to advanced survival models, is discussed. We highlight their advantages and disadvantages, emphasizing the methodological challenges when genetic mutation status is unavailable. We also discuss the potential bias and higher variability incurred from the uncertainty associated with subjective definitions for onset. Methods to adjust for the uncertainty in survival models are still in their infancy, but would be beneficial for HD and neurodegenerative diseases with long prodromal periods like Alzheimer's and Parkinson's disease.

摘要

亨廷顿舞蹈症(HD)由亨廷顿基因中的CAG三核苷酸重复扩增引起。我们现在有能力根据运动和神经影像测量等个体特征来预测发病年龄。在临床试验中,对发病年龄进行恰当建模很重要,因为这能改进功效计算,并指导临床医生招募具有特定特征的受试者。文中讨论了从简单线性和逻辑回归到高级生存模型的发病建模历史。我们强调了它们的优缺点,着重指出在无法获取基因突变状态时所面临的方法学挑战。我们还讨论了因发病主观定义的不确定性所导致的潜在偏差和更高变异性。针对生存模型不确定性进行调整的方法仍处于起步阶段,但对HD以及像阿尔茨海默病和帕金森病这类具有较长前驱期的神经退行性疾病将大有裨益。

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