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移植肾后肾小球滤过率曲线的功能主成分分析。

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

机构信息

1 Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada.

2 Department of Medicine, University of British Columbia, Vancouver, Canada.

出版信息

Stat Methods Med Res. 2018 Dec;27(12):3785-3796. doi: 10.1177/0962280217712088. Epub 2017 Jun 20.

Abstract

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

摘要

本文的研究动机来自一些肾移植受者的纵向临床数据,其中肾功能进展记录为肾移植后多个时间点的估计肾小球滤过率。我们提出使用功能主成分分析方法来探索肾小球滤过率曲线变异的主要来源。我们发现,估计的功能主成分得分可用于对肾小球滤过率曲线进行聚类。对功能主成分得分进行排序可以检测到异常的肾小球滤过率曲线。最后,功能主成分分析可以有效地估计缺失的肾小球滤过率值并预测未来的肾小球滤过率值。

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