Cross‐sectional studies – what are they good for?

Cross‐sectional studies – what are they good for? Cross‐sectional studies serve many purposes, and the cross‐sectional design is the most relevant design when assessing the prevalence of disease, attitudes and knowledge among patients and health personnel, in validation studies comparing, for example, different measurement instruments, and in reliability studies. This paper describes the use of cross‐sectional studies and provides examples within obstetrics and gynecology. Caveats are also described; for example, when cross‐sectional data is used for analytical purposes of associations between an exposure and an outcome, authors and readers should be careful not to make causal inferences, unless the exposure may safely be assumed to be stable over time and not influenced by experiencing the outcome. In such cases, analyses are also subject to selection and information bias as well as confounding. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Obstetricia Et Gynecologica Scandinavica Wiley

Cross‐sectional studies – what are they good for?

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 Acta Obstetricia et Gynecologica Scandinavica
ISSN
0001-6349
eISSN
1600-0412
D.O.I.
10.1111/aogs.13331
Publisher site
See Article on Publisher Site

Abstract

Cross‐sectional studies serve many purposes, and the cross‐sectional design is the most relevant design when assessing the prevalence of disease, attitudes and knowledge among patients and health personnel, in validation studies comparing, for example, different measurement instruments, and in reliability studies. This paper describes the use of cross‐sectional studies and provides examples within obstetrics and gynecology. Caveats are also described; for example, when cross‐sectional data is used for analytical purposes of associations between an exposure and an outcome, authors and readers should be careful not to make causal inferences, unless the exposure may safely be assumed to be stable over time and not influenced by experiencing the outcome. In such cases, analyses are also subject to selection and information bias as well as confounding.

Journal

Acta Obstetricia Et Gynecologica ScandinavicaWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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