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American Journal of Epidemiology Advance Access originally published online on September 15, 2009
American Journal of Epidemiology 2009 170(8):959-962; doi:10.1093/aje/kwp293
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American Journal of Epidemiology © The Author 2009. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

INVITED COMMENTARY

Invited Commentary: Causal Diagrams and Measurement Bias

Miguel A. Hernán and Stephen R. Cole

Correspondence to Dr. Miguel Hernán, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115 (e-mail: miguel_hernan{at}post.harvard.edu).

Received for publication November 25, 2008. Accepted for publication March 9, 2009.

Causal inferences about the effect of an exposure on an outcome may be biased by errors in the measurement of either the exposure or the outcome. Measurement errors of exposure and outcome can be classified into 4 types: independent nondifferential, dependent nondifferential, independent differential, and dependent differential. Here the authors describe how causal diagrams can be used to represent these 4 types of measurement bias and discuss some problems that arise when using measured exposure variables (e.g., body mass index) to make inferences about the causal effects of unmeasured constructs (e.g., "adiposity"). The authors conclude that causal diagrams need to be used to represent biases arising not only from confounding and selection but also from measurement.

bias (epidemiology); body mass index; causality; confounding factors (epidemiology)

Abbreviations: BMI, body mass index


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