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Nephrology Dialysis Transplantation 2008 23(5):1484-1492; doi:10.1093/ndt/gfn138
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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 V: extended analytic models

Pietro Ravani1,2, Patrick Parfrey2, Veeresh Gadag3, Fabio Malberti1 and Brendan Barrett2

1 Divisione di Nefrologia e Dialisi, Azienda Instituti Ospitalieri di Cremona, Cremona, Italy 2 Clinical Epidemiology Unit 3 Division 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

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   Introduction
 
In some study designs the same epidaemiological unit is observed more than once. For example, in a cross-sectional study of the radial artery flow rate, several outcome values can be recorded on the same subject under different experimental conditions (e.g. exposure to different vasoactive substances). Some longitudinal studies typically monitor participants over time and both predictors (e.g. blood pressure) and outcomes (e.g. left ventricular mass index) are measured on different occasions in the same subject. In other designs, observations can fall into groups (clustered data), such as single measurements taken on a paired organ (e.g. the eye or the kidney) or single observations on different members of the same hospital/region or family. More complex designs may lead to a combination of clustering and repeated/longitudinal measurements (Table 1). All these designs generate correlated outcome data (Figure 1).


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  		Table 1 Examples of correlated (panel) data sets. Each study . . . [Full Text of this Article]

 


   Extended generalized linear models
 
Fixed and random effects modelling
Variance correction
Model choice


   Extended survival models
 
Correlated survival times
Risk sets for survival analysis
Variance-corrected models
Frailty models
Model choice
Time-dependent effects and time-varying covariates


   Special topics
 


   Conclusion
 

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