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NDT Advance Access originally published online on January 8, 2008
Nephrology Dialysis Transplantation 2008 23(2):475-482; doi:10.1093/ndt/gfm880
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© The Author [2008]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org


Clinical research of kidney diseases IV: standard regression models

Pietro Ravani1, Patrick Parfrey2, Sean Murphy2, Veeresh Gadag3 and Brendan Barrett2

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, Largo priori 1, Cremona 26100, Italy. E-mail: pietro.ravani@med.mun.ca

Keywords: Generalized linear models; linear regression; logistic regression; Poisson regression; survival analysis

The first 150 words of the full text of this article appear below.



   Introduction
 
Statistical modelling is similar to the engineering concept of the study outcome being a mixture of signal and noise. For example, the signal of a model of left ventricular mass (LVM) as a function of systolic blood pressure (SBP) [1] is the average change in LVM as SBP changes (systematic component). The noise is what remains to be explained of LVM variability once the effect of SBP has been taken into account (random component). Statisticians assess the characteristics of these two elements in different ways, to establish whether a model is appropriate [2].

The present review introduces two popular families of standard regression models: generalized linear models and models for time-to-event data. The conditions that make each model appropriate are summarized along with the epidemiological meaning of its coefficients (parameters). The interested reader is referred to specific textbooks for details on model specification and assumption verification . . . [Full Text of this Article]



   Generalized linear models
 
Linear model for quantitative continuous responses
Logistic model for qualitative responses
Poisson model for quantitative discrete responses


   Models for time-to-event data
 
Survival data
Key requirements for survival analysis
Functions of time-to-event data
Cox's model


   Appendix A
 


   Appendix B
 


   Appendix C
 

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Home page
 Nephrol Dial TransplantHome page
P. Ravani, P. Parfrey, V. Gadag, F. Malberti, and B. Barrett
Clinical research of kidney diseases V: extended analytic models
Nephrol. Dial. Transplant., May 1, 2008; 23(5): 1484 - 1492.
[Full Text] [PDF]