Department of Biostatistics, Epidemiology and Informatics and.
Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Clin J Am Soc Nephrol. 2017 Aug 7;12(8):1357-1365. doi: 10.2215/CJN.11311116. Epub 2017 Jul 27.
Repeated measures of various biomarkers provide opportunities for us to enhance understanding of many important clinical aspects of CKD, including patterns of disease progression, rates of kidney function decline under different risk factors, and the degree of heterogeneity in disease manifestations across patients. However, because of unique features, such as correlations across visits and time dependency, these data must be appropriately handled using longitudinal data analysis methods. We provide a general overview of the characteristics of data collected in cohort studies and compare appropriate statistical methods for the analysis of longitudinal exposures and outcomes. We use examples from the Chronic Renal Insufficiency Cohort Study to illustrate these methods. More specifically, we model longitudinal kidney outcomes over annual clinical visits and assess the association with both baseline and longitudinal risk factors.
对各种生物标志物的重复测量为我们提供了机会，使我们能够更好地了解慢性肾脏病的许多重要临床方面，包括疾病进展模式、不同危险因素下肾功能下降的速度，以及患者之间疾病表现的异质性程度。然而，由于存在相关性等独特特征，以及数据随时间的依赖性，这些数据必须使用纵向数据分析方法进行适当处理。我们提供了队列研究中收集的数据的特征的概述，并比较了用于分析纵向暴露和结果的适当统计方法。我们使用慢性肾功能不全队列研究的实例来说明这些方法。更具体地说，我们对每年的临床就诊中的肾脏纵向结局进行建模，并评估与基线和纵向危险因素的关联。
