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Open Access Highly Accessed Research article

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

Julie Boucquemont1, Georg Heinze2, Kitty J Jager3, Rainer Oberbauer4 and Karen Leffondre1*

Author Affiliations

1 University of Bordeaux, ISPED, Centre INSERM U897-Epidemiology-Biostatistics, Bordeaux F33000, France

2 Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent Systems, Section for Clinical Biometrics, Vienna, Austria

3 Department of Medical Informatics, ERA-EDTA Registry, Academic Medical Center, Amsterdam, The Netherlands

4 Medical University of Vienna, Vienna, Austria

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BMC Nephrology 2014, 15:45  doi:10.1186/1471-2369-15-45

Published: 14 March 2014

Abstract

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.

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