Invited commentary: structural equation models and epidemiologic analysis

In this commentary, structural equation models (SEMs) are discussed as a tool for epidemiologic analysis. Such models are related to and compared with other analytic approaches often used in epidemiology, including regression analysis, causal diagrams, causal mediation analysis, and marginal structu...

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Bibliographic Details
Published in:American journal of epidemiology 2012-10, Vol.176 (7), p.608-612
Main Author: VanderWeele, Tyler J
Format: Article
Language:English
Subjects:
Causality
Epidemiology
Fatigue - complications
Female
Humans
Male
Models, Statistical
Multivariate analysis
Occupational Injuries - etiology
Practice of Epidemiology
Regression analysis
Sleep Deprivation - complications
Work Schedule Tolerance
Workload
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Summary:In this commentary, structural equation models (SEMs) are discussed as a tool for epidemiologic analysis. Such models are related to and compared with other analytic approaches often used in epidemiology, including regression analysis, causal diagrams, causal mediation analysis, and marginal structural models. Several of these other approaches in fact developed out of the SEM literature. However, SEMs themselves tend to make much stronger assumptions than these other techniques. SEMs estimate more types of effects than do these other techniques, but this comes at the price of additional assumptions. Many of these assumptions have often been ignored and not carefully evaluated when SEMs have been used in practice. In light of the strong assumptions employed by SEMs, the author argues that they should be used principally for the purposes of exploratory analysis and hypothesis generation when a broad range of effects are potentially of interest.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kws213