NDT Advance Access originally published online on November 19, 2007
Nephrology Dialysis Transplantation 2007 22(12):3422-3430; doi:10.1093/ndt/gfm777
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© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Clinical research of kidney diseases III: Principles of regression and modelling
1Divisione di Nefrologia e Dialisi, Azienda Instituti Ospitalieri di Cremona, Cremona, Italy, 2Clinical Epidemiology Unit and 3Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, Canada
Correspondence and offprint requests to: Pietro Ravani, Divisione di Nefrologia, Azienda Istituti Ospitalieri di Cremona, Italy, Largo priori 1, Cremona, 26100, Italy. E-mail: pietro.ravani@med.mun.ca
Keywords: confounding; interaction; interval estimate; point estimate; regression models
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Introduction |
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Inappropriate data analysis is a source of measurement error in clinical studies [1]. Descriptive methods (graphs, summary statistics and relational plots) are used to assess variable distributions, identify possible outliers and reveal the form of the relationship of interest. For example, in a study of hyperparathyroidism in chronic kidney disease, researchers are interested in the sample mean and standard deviation (SD) of both parathyroid hormone and kidney function levels, and in the form of their possible relationship (i.e. whether it is present across all variable levels and whether it can be described by a line, a curve, etc.). The next step is to extend the conclusions beyond the immediate sample (inference) and estimate, for example, the amount of parathyroid hormone increase as kidney function declines. Statistical models are used to test whether an input–output relationship is supported by observed data and assess its direction and strength
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Regression analysis |
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Role of statistics
Concept of function
Regression methods
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Statistical models |
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Definition
Model choice
Multivariable versus univariable analysis
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Modelling issues |
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Confounding
Definition.
Control.
Interaction
Definition.
Statistical assessment versus epidemiological interpretation of interaction.
Measurement scale and biological implications.
Analysis power
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Reporting |
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