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Appraising the evidence: what is measurement bias?

2008; BMJ; Volume: 11; Issue: 2 Linguagem: Inglês

10.1136/ebmh.11.2.36

1468-960X

Lisa Page, Max Henderson,

Clinical Reasoning and Diagnostic Skills

In a previous issue of Evidence Based Mental Health , we discussed the role that selection bias can have in introducing systematic error into studies (see Evid Based Ment Health 2007; 10 :67–8). In this article we discuss measurement (or information) bias—the other major type of systematic error commonly encountered in epidemiological research (fig 1). Important general points about bias include the following: Figure 1 A systematic approach to bias. Measurement bias occurs when information collected for use as a study variable is inaccurate. The incorrectly measured variable can be either a disease outcome or an exposure. Measurement bias can be further divided into random or non-random misclassification. We are more concerned with non-random misclassification, as this can spuriously inflate or reduce estimates of effect. Non-random misclassification can itself be divided into subtypes, including observer bias and recall bias . Random misclassification (also known as non-differential misclassification) is often thought of as less worrying than non-random misclassification. It occurs when either an exposure or a disease outcome is classified incorrectly in equal proportions for any subject group in a study—that is, it is random. Therefore if there are errors in the classification of a disease (for example, having schizophrenia), then for random misclassification to have occurred this must be unrelated to any exposures being examined (for example, being from an ethnic minority). Conversely any misclassification of exposure must be unrelated to disease status. When random misclassification occurs, …