Document Detail


Linear regression analysis for comparing two measurers or methods of measurement: but which regression?
MedLine Citation:
PMID:  20337658     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
1. There are two reasons for wanting to compare measurers or methods of measurement. One is to calibrate one method or measurer against another; the other is to detect bias. Fixed bias is present when one method gives higher (or lower) values across the whole range of measurement. Proportional bias is present when one method gives values that diverge progressively from those of the other. 2. Linear regression analysis is a popular method for comparing methods of measurement, but the familiar ordinary least squares (OLS) method is rarely acceptable. The OLS method requires that the x values are fixed by the design of the study, whereas it is usual that both y and x values are free to vary and are subject to error. In this case, special regression techniques must be used. 3. Clinical chemists favour techniques such as major axis regression ('Deming's method'), the Passing-Bablok method or the bivariate least median squares method. Other disciplines, such as allometry, astronomy, biology, econometrics, fisheries research, genetics, geology, physics and sports science, have their own preferences. 4. Many Monte Carlo simulations have been performed to try to decide which technique is best, but the results are almost uninterpretable. 5. I suggest that pharmacologists and physiologists should use ordinary least products regression analysis (geometric mean regression, reduced major axis regression): it is versatile, can be used for calibration or to detect bias and can be executed by hand-held calculator or by using the loss function in popular, general-purpose, statistical software.
Authors:
John Ludbrook
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Publication Detail:
Type:  Journal Article     Date:  2010-03-12
Journal Detail:
Title:  Clinical and experimental pharmacology & physiology     Volume:  37     ISSN:  1440-1681     ISO Abbreviation:  Clin. Exp. Pharmacol. Physiol.     Publication Date:  2010 Jul 
Date Detail:
Created Date:  2010-07-19     Completed Date:  2010-11-02     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0425076     Medline TA:  Clin Exp Pharmacol Physiol     Country:  Australia    
Other Details:
Languages:  eng     Pagination:  692-9     Citation Subset:  IM    
Affiliation:
The University of Melbourne, Parkville, Victoria, Australia. ludbrook@bigpond.net.au
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MeSH Terms
Descriptor/Qualifier:
Bias (Epidemiology)
Laboratory Techniques and Procedures / statistics & numerical data*
Least-Squares Analysis
Linear Models
Monte Carlo Method
Pharmacological Processes*
Physiological Processes*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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