Kang Kai, Pan Deng, Song Xinyuan
Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China.
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China.
Stat Med. 2022 Jan 30;41(2):356-373. doi: 10.1002/sim.9241. Epub 2021 Nov 2.
Alzheimer's disease (AD) is an incurable and progressive disease that starts from mild cognitive impairment and deteriorates over time. Examining the effects of patients' longitudinal cognitive decline on time to conversion to AD and obtaining a reliable diagnostic model are therefore critical to the evaluation of AD prognosis and early treatment. Previous studies either assess patients' cognitive impairment through a single cognitive test or assume it changes linearly across time, thereby leading to an incomplete measure of cognitive decline or overlooking the subtle trajectory pattern of patients' cognitive impairment. This study develops a new joint model to address these shortcomings. First, a dynamic factor analysis model is adopted to characterize cognitive impairment through multiple cognitive measures in a comprehensive manner. Second, a spline-based random coefficient model is proposed to reveal possibly nonlinear trajectories of patients' cognitive decline. Finally, a proportional hazard model is considered to examine the effects of time-invariant markers and time-variant cognitive impairment on AD hazards. A Bayesian approach coupled with spline approximation techniques and MCMC methods is developed to conduct statistical inference. The application of the proposed method to the Alzheimer's Disease Neuroimaging Initiative study provides new insights into the prevention of AD and shows a high prediction capacity of the proposed method.
阿尔茨海默病（AD）是一种无法治愈的进行性疾病，始于轻度认知障碍，并随时间推移而恶化。因此，研究患者纵向认知衰退对转化为AD的时间的影响，并获得可靠的诊断模型，对于评估AD预后和早期治疗至关重要。以往的研究要么通过单一认知测试评估患者的认知障碍，要么假设其随时间呈线性变化，从而导致对认知衰退的测量不完整，或者忽略了患者认知障碍的细微轨迹模式。本研究开发了一种新的联合模型来解决这些缺点。首先，采用动态因子分析模型通过多种认知测量全面表征认知障碍。其次，提出基于样条的随机系数模型以揭示患者认知衰退可能的非线性轨迹。最后，考虑比例风险模型来研究时不变标志物和时变认知障碍对AD风险的影响。开发了一种结合样条近似技术和MCMC方法的贝叶斯方法来进行统计推断。将所提出的方法应用于阿尔茨海默病神经影像倡议研究，为AD的预防提供了新见解，并显示了所提出方法的高预测能力。
