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Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.
Statistics in Medicine ( IF 2 ) Pub Date : 2020-07-27 , DOI: 10.1002/sim.8687
Lili Zhao 1 , Susan Murray 1 , Laura H Mariani 2 , Wenjun Ju 3
Affiliation  

Longitudinal biomarker data are often collected in studies, providing important information regarding the probability of an outcome of interest occurring at a future time. With many new and evolving technologies for biomarker discovery, the number of biomarker measurements available for analysis of disease progression has increased dramatically. A large amount of data provides a more complete picture of a patient's disease progression, potentially allowing us to make more accurate and reliable predictions, but the magnitude of available data introduces challenges to most statistical analysts. Existing approaches suffer immensely from the curse of dimensionality. In this article, we propose methods for making dynamic risk predictions using repeatedly measured biomarkers of a large dimension, including cases when the number of biomarkers is close to the sample size. The proposed methods are computationally simple, yet sufficiently flexible to capture complex relationships between longitudinal biomarkers and potentially censored events times. The proposed approaches are evaluated by extensive simulation studies and are further illustrated by an application to a data set from the Nephrotic Syndrome Study Network.

中文翻译:

在大数据时代结合纵向生物标记物进行动态风险预测:一种伪观察方法。

纵向生物标志物数据通常是在研究中收集的,可提供有关将来将来发生感兴趣结果的可能性的重要信息。随着许多新的生物标志物发现技术的发展,可用于疾病进展分析的生物标志物测量数量急剧增加。大量数据可以更全面地了解患者的疾病进展,可能使我们能够做出更准确和可靠的预测,但是可用数据的数量给大多数统计分析人员带来了挑战。现有方法极大地遭受了维数的诅咒。在本文中,我们提出了使用重复测量的大尺寸生物标志物进行动态风险预测的方法,包括生物标志物数量接近样本量的情况。所提出的方法在计算上是简单的,但是足够灵活以捕获纵向生物标志物与可能被审查的事件时间之间的复杂关系。通过广泛的模拟研究对提出的方法进行了评估,并通过对肾病综合症研究网络的数据集进行了进一步说明。
更新日期:2020-07-27
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