Non-differential misclassification of exposure always leads to an underestimate of risk: an incorrect conclusion

In most epidemiological surveys, there will be some errors of measurement or classification of exposure. For example, for a binary exposure variable, some exposed subjects may be classified as non-exposed, and some non-exposed subjects may be classified as exposed. Non-differential misclassification...

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Bibliographic Details
Published in:Occupational and environmental medicine (London, England) England), 1994-12, Vol.51 (12), p.839-840
Main Authors: Sorahan, T, Gilthorpe, M S
Format: Article
Language:English
Subjects:
Biological and medical sciences
Cohort Studies
Computer software
Disease risk
Epidemiology
Estimation bias
General aspects
Medical sciences
Methodology
Occupational Exposure - adverse effects
Probability
Public health. Hygiene
Public health. Hygiene-occupational medicine
Risk Assessment
Sensitivity and Specificity
Short Report
Statistical median
Underestimates
Citations: Items that cite this one
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Summary:In most epidemiological surveys, there will be some errors of measurement or classification of exposure. For example, for a binary exposure variable, some exposed subjects may be classified as non-exposed, and some non-exposed subjects may be classified as exposed. Non-differential misclassification of exposure is present if, irrespective of disease, all exposed and non-exposed subjects have the same probability of being misclassified (these two probabilities may be different, one must be not zero). It is now commonplace to find statements in epidemiological textbooks, journal articles, and teaching materials to the effect that non-differential misclassification of exposure always leads to an underestimate of risk. Rothman, for example, states that "such misclassification can introduce a bias, but the bias is always in the direction of underestimating the effect", and Checkoway et al state "non-differential misclassification of exposure will bias the effect estimate toward the null value." This contradicts our expectation that studies may overestimate as well as underestimate effects, and we therefore tested these ideas by computer simulations relevant to study settings.
ISSN:1351-0711
1470-7926
DOI:10.1136/oem.51.12.839