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Principles of Exposure Measurement in EpidemiologyCollecting, Evaluating, and Improving Measures of Disease Risk Factors$
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Emily White, Bruce K. Armstrong, and Rodolfo Saracci

Print publication date: 2008

Print ISBN-13: 9780198509851

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780198509851.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 23 April 2021

Exposure measurement error and its effects

Exposure measurement error and its effects

(p.65) 3 Exposure measurement error and its effects
Principles of Exposure Measurement in Epidemiology

Emily White

Bruce K. Armstrong

Rodolfo Saracci

Oxford University Press

Exposure measurement error in an epidemiologic study can lead to substantial bias in the risk ratio or other measure of association between the exposure and outcome. In this chapter the parameters used to quantify the validity (degree of measurement error) of an exposure measure are described. These parameters are computed by comparing the measured exposure to a perfect measure of the true exposure in a population. Bias (difference between means of the measured and true exposure) and the validity coefficient (correlation between them) are appropriate for continuous exposure variables, and the misclassification matrix for categorical variables. The chapter then discusses how these parameters can be used to estimate the effects of this degree of measurement error on the bias in the risk ratio in the epidemiological study that will use (or has used) the measured exposure. The effects of both differential and non-differential measurement error are covered.

Keywords:   misclassification bias, information bias, recall bias, validity studies, validity coefficient, bias, precision, misclassification matrix, sensitivity, specificity

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