Regression methods for investigating risk factors of chronic kidney disease outcomes: the state of the art

BMC Nephrology

Karen Leffondre⁴  Rainer Oberbauer¹  Kitty J Jager³  Georg Heinze²  Julie Boucquemont⁴ 

[1] Medical University of Vienna, Vienna, Austria;Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent Systems, Section for Clinical Biometrics, Vienna, Austria;Department of Medical Informatics, ERA-EDTA Registry, Academic Medical Center, Amsterdam, The Netherlands;University of Bordeaux, ISPED, Centre INSERM U897-Epidemiology-Biostatistics, Bordeaux F33000, France

关键词: Mixed models;    Longitudinal analysis;    Multistate model;    Interval censoring;    Competing risks;    Survival analysis;    ESRD;    Progression;    Kidney disease;   

Others  :  1082714
DOI  :  10.1186/1471-2369-15-45

 received in 2013-08-21, accepted in 2014-02-20,  发布年份 2014

【 摘 要 】

Background

Chronic kidney disease (CKD) is a progressive and usually irreversible disease. Different types of outcomes are of interest in the course of CKD such as time-to-dialysis, transplantation or decline of the glomerular filtration rate (GFR). Statistical analyses aiming at investigating the association between these outcomes and risk factors raise a number of methodological issues. The objective of this study was to give an overview of these issues and to highlight some statistical methods that can address these topics.

Methods

A literature review of statistical methods published between 2002 and 2012 to investigate risk factors of CKD outcomes was conducted within the Scopus database. The results of the review were used to identify important methodological issues as well as to discuss solutions for each type of CKD outcome.

Results

Three hundred and four papers were selected. Time-to-event outcomes were more often investigated than quantitative outcome variables measuring kidney function over time. The most frequently investigated events in survival analyses were all-cause death, initiation of kidney replacement therapy, and progression to a specific value of GFR. While competing risks were commonly accounted for, interval censoring was rarely acknowledged when appropriate despite existing methods. When the outcome of interest was the quantitative decline of kidney function over time, standard linear models focussing on the slope of GFR over time were almost as often used as linear mixed models which allow various numbers of repeated measurements of kidney function per patient. Informative dropout was accounted for in some of these longitudinal analyses.

Conclusions

This study provides a broad overview of the statistical methods used in the last ten years for investigating risk factors of CKD progression, as well as a discussion of their limitations. Some existing potential alternatives that have been proposed in the context of CKD or in other contexts are also highlighted.

【 授权许可】

   
2014 Boucquemont et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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