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NDT Advance Access published online on September 22, 2007
Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfm433
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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 II: problems of study design

Pietro Ravani1,2, Patrick S. Parfrey1, Elizabeth Dicks1 and Brendan J. Barrett1

1Clinical Epidemiology Unit, Faculty of Medicine, Memorial University of Newfoundland, Canada and 2Divisione di Nefrologia, Azienda Istituti Ospitalieri di Cremona, Italy

Correspondence and offprint requests to: Pietro Ravani, MD, Divisione di Nefrologia, Azienda Istituti Ospitalieri di Cremona, Italy, Largo priori 1, Cremona, 26100, Italy. Email: p.ravani@ospedale.cremona.it

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



   Introduction
 
The aim of study design in any field of clinical inquiry is to limit bias and maximize reliability [1]. The present article introduces the types of study design currently recommended for assessing prognosis, therapy and diagnostic tests with nephrology examples. The concept of clinical relevance as opposed to statistical significance of study results is also briefly discussed.



   Study design
 
Hierarchy of evidence
Fundamental to evidence-based health care is the concept of ‘hierarchy of evidence’, deriving from different study designs addressing a given research question (Figure 1). Evidence grading is based on the idea that different designs vary in their susceptibility to bias and, therefore, in their ability to predict the true effectiveness of health care practices. For assessment of interventions, randomized controlled trials (RCTs) or systematic review of good quality, RCTs are at the top of the evidence pyramid, followed by longitudinal cohort, case-control and cross-sectional studies [2,3. . . [Full Text of this Article]

Additional biases in longitudinal designs
Experimental designs for intervention questions
Designs for diagnostic questions


   Maximizing the validity of non-experimental studies
 


   Research questions in genetic epidemiology
 


   Clinical relevance vs statistical significance
 


   Reporting
 

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