Document Detail


A structural equation modelling approach to the analysis of change.
MedLine Citation:
PMID:  18705795     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Analysis of change is probably the most commonly adopted study design in medical and dental research when comparing the efficacy of two or more treatment modalities. The most commonly used methods for testing the difference in treatment efficacy are the two-sample t-test and the analysis of covariance (ANCOVA). It has been suggested that ancova should be used in the analysis of change for data from randomized controlled trials (RCTs) as a result of its greater statistical power. However, it is less well known that although both methods will give rise to similar results in the analysis of change for RCTs, there are different assumptions behind these methods in terms of the relationship between baseline value and the subsequent change, and the results may therefore differ if baseline values are not balanced between groups. This article uses structural equation modelling as a conceptual framework to explain the assumptions behind these methods, and two examples are used to show when the two methods yield similar results and why, in some non-randomized studies, the two methods might give substantially different results, known as 'Lord's paradox' in the statistical literature. For the appropriate interpretation of non-randomized studies, the assumptions underlying these methods therefore need to be taken into consideration.
Authors:
Yu-Kang Tu; Vibeke Baelum; Mark S Gilthorpe
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  European journal of oral sciences     Volume:  116     ISSN:  1600-0722     ISO Abbreviation:  Eur. J. Oral Sci.     Publication Date:  2008 Aug 
Date Detail:
Created Date:  2008-08-18     Completed Date:  2008-11-10     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9504563     Medline TA:  Eur J Oral Sci     Country:  Denmark    
Other Details:
Languages:  eng     Pagination:  291-6     Citation Subset:  D; IM    
Affiliation:
Department of Periodontology, Leeds Dental Institute, University of Leeds, Leeds, UK. y.k.tu@leeds.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Controlled Clinical Trials as Topic / statistics & numerical data*
Dental Research / methods*
Humans
Models, Statistical*
Multivariate Analysis
Outcome Assessment (Health Care) / methods
Randomized Controlled Trials as Topic / statistics & numerical data*
Regression Analysis

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


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