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IJE Advance Access originally published online on January 19, 2009
International Journal of Epidemiology 2009 38(2):549-551; doi:10.1093/ije/dyn346
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved.

Commentary: Is structural equation modelling a step forward for epidemiologists?

Yu-Kang Tu

Biostatistics Unit, Centre for Epidemiology and Biostatistics, Leeds Institute for Genetics, Health and Therapeutics, and Leeds Dental Institute, University of Leeds, UK.

E-mail: y.k.tu@leeds.ac.uk

Accepted 27 November 2008

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

One of the major challenges for epidemiologists is to understand causal relationships between risk factors and health outcomes by analysing data from observational studies. Epidemiologists know too well that correlations, such as those in regression analysis, rarely mean causation, and it would be very desirable if there is a methodology for observational studies, analogous to randomization in experimental studies, which can discover causes and effects amongst variables or at least confirm or refute the proposed causal relationships. Randomized controlled trials (RCTs) are certainly the ‘gold standard’ of establishing causes and effects, but quite often it is either unethical or unfeasible to conduct RCTs to test causal relations in epidemiological research. Epidemiologists need a methodology, which is sort of a combination of the directed acyclic graphs (DAGs) for conceptual construction of causal models and regression analysis for testing those models. It is therefore surprising that structural equation modelling (SEM) has not . . . [Full Text of this Article]


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