Functional ensemble survival tree: Dynamic prediction of Alzheimer's disease progression accommodating multiple time-varying covariates
Functional ensemble survival tree: Dynamic prediction of Alzheimer's disease progression accommodating multiple time-varying covariates
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DOI：
10.1111/rssc.12449
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发表时间：
2020-11-07
影响因子：
1.6
通讯作者：
Colditz, Graham A.
中科院分区：
文献类型：
--
作者：
Jiang, Shu;Xie, Yijun;Colditz, Graham A.
随着数据收集的指数增长，临床研究中通常会遇到多种时变生物标志物，沿着丰富的基线协变量。本文旨在解决阿尔茨海默病（AD）领域的一个关键问题，我们的目标是预测轻度认知障碍患者AD转换的时间，以告知预防和早期治疗决策。传统的生物标志物轨迹与事件发生时间数据的联合模型严重依赖于模型假设，当协变量数量很大时可能不适用。这促使我们考虑一个功能集成生存树框架来描述功能和基线协变量在预测疾病进展中的联合作用。该框架采用多变量功能主成分分析来表征多个随时间变化的神经认知生物标志物轨迹的变化模式，然后将这些特征嵌套在集成生存树中以预测AD的进展。我们提供了一个快速实现的算法，适应个性化的动态预测，可以更新为新的观察收集，以反映病人的最新预后。经验表明，该算法在模拟研究中表现良好，并通过分析阿尔茨海默病神经成像倡议（ADNI）（http：//adni.loni.usc.edu/）的数据进行说明。我们在R包funest中实现了我们提出的方法。
With the exponential growth in data collection, multiple time-varying biomarkers are commonly encountered in clinical studies, along with a rich set of baseline covariates. This paper is motivated by addressing a critical issue in the field of Alzheimer's disease (AD) in which we aim to predict the time for AD conversion in people with mild cognitive impairment to inform prevention and early treatment decisions. Conventional joint models of biomarker trajectory with time-to-event data rely heavily on model assumptions and may not be applicable when the number of covariates is large. This motivated us to consider a functional ensemble survival tree framework to characterize the joint effects of both functional and baseline covariates in predicting disease progression. The proposed framework incorporates multivariate functional principal component analysis to characterize the changing patterns of multiple time-varying neurocognitive biomarker trajectories and then nest these features within an ensemble survival tree in predicting the progression of AD. We provide a fast implementation of the algorithm that accommodates personalized dynamic prediction that can be updated as new observations are gathered to reflect the patient's latest prognosis. The algorithm is empirically shown to perform well in simulation studies and is illustrated through the analysis of data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (http://adni.loni.usc.edu/). We provide implementation of our proposed method in an R package funest.