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Clin J Am Soc Nephrol. 2017 Jul 27;12(8). pii: CJN.11311116. doi: 10.2215/CJN.11311116. Print 2017 Aug 7.

Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD.

Author information

1
Department of Biostatistics, Epidemiology and Informatics and hshou@mail.med.upenn.edu.
2
Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
3
Department of Biostatistics, Epidemiology and Informatics and.
4
Department of Medicine, Division of Nephrology, University of Maryland School of Medicine, Baltimore, Maryland; and.
5
Department of Medicine, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland.

Abstract

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.

KEYWORDS:

Biomarkers; CKD; Chronic; Cohort Studies; Disease Progression; GEE; GFR; Humans; Renal Insufficiency; correlation structures; kidney; longitudinal data; mixed effects model; repeated measures; risk factors

PMID:
28751576
PMCID:
PMC5544518
DOI:
10.2215/CJN.11311116
[Indexed for MEDLINE]
Free PMC Article

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