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Kidney Int. 2008 Apr;73(7):806-10. doi: 10.1038/sj.ki.5002787. Epub 2008 Jan 16.

Linear and logistic regression analysis.

Author information

1
CNR-IBIM, Clinical Epidemiology and Physiopathology of Renal Diseases and Hypertension of Reggio Calabria, Reggio Calabria, Italy. gtripepi@ibim.cnr.it

Abstract

In previous articles of this series, we focused on relative risks and odds ratios as measures of effect to assess the relationship between exposure to risk factors and clinical outcomes and on control for confounding. In randomized clinical trials, the random allocation of patients is hoped to produce groups similar with respect to risk factors. In observational studies, exposed and unexposed individuals may differ not only for the presence of the risk factor being tested but also for a series of other factors that are potentially related to the study outcome, thus making 'confounding' very likely. One of the most important uses of multivariate modeling is precisely that 'of controlling for confounding' to let emerge the effect of the risk factor of interest on the study outcome. In this paper, we will discuss linear regression analysis for the examination of continuous outcome data and logistic regression analysis for the study of categorical outcome data. Furthermore, we focus on the most important application of multiple linear and logistic regression analyses.

PMID:
18200004
DOI:
10.1038/sj.ki.5002787
[Indexed for MEDLINE]
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