Predicting the onset time of Alzheimer’s disease is of great importance in preventive medicine. Structural changes in brain regions have been actively investigated in the association study of Alzheimer’s disease diagnosis and prognosis. In this study, we propose a functional linear regression model to predict the conversion time to Alzheimer’s disease among mild cognitive impairment patients. Midsagittal thickness change in corpus callosum is measured from magnetic resonance imaging scan and put into the model as a functional covariate. A synthetic response approach is taken to deal with the censored data. The simulation studies demonstrate that the proposed model successfully predicts the unobserved true survival time but indicate that high censoring rate may cause poor prediction in time. Through ADNI data application, we find that the atrophy in the rear area of corpus callosum is a possible neuroimaging marker on Alzheimer’s disease prognosis.

中文翻译：

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具有随机删失数据的函数线性回归模型：预测转换为阿尔茨海默病的时间

预测阿尔茨海默病的发病时间在预防医学中具有重要意义。在阿尔茨海默病诊断和预后的关联研究中，大脑区域的结构变化得到了积极的研究。在这项研究中，我们提出了一个函数线性回归模型来预测轻度认知障碍患者转变为阿尔茨海默病的时间。胼胝体的中矢状厚度变化是通过磁共振成像扫描测量的，并作为函数协变量放入模型中。采用综合响应方法来处理删失数据。模拟研究表明，所提出的模型成功地预测了未观察到的真实生存时间，但表明高审查率可能会导致时间预测不佳。通过ADNI数据应用，