Measurement Bias

Lead Author(s): Jeff Martin, MD

Technical Definition: It is the bias, or deviation from the truth, that it is caused when any measurement collected about or from subjects is not completely valid (i.e., not completely accurate). Inaccuracy in the measurement of any kind of variable, be it an exposure variable, an outcome variable, or a confounder variable can lead to measurement bias.

AKA: What are the other terms you might find for measurement bias? Measurement bias is also known as misclassification bias, information bias, or identification bias. Misclassification bias is a good term because misclassification of a variable is the immediate result of an error in measurement.

Non-Differential Misclassification - Bias Towards the Null Hypothesis

As shown in the table below, the effect of non-differential misclassification is predictably always towards the null hypothesis, in other words an attenuation of the measure of association.
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The effects of differential misclassification can go in either direction and depend very much on the individual circumstances of the problem. The bias can either be away from or towards the null hypothesis.

Non-Differential Misclassification of Exposure and Outcome Variables

When we are using odds ratios there is a predictable bias towards the null hypothesis for both non-differential misclassification of exposure and outcome.

How common do you think this is? But what does conservative really mean?

Non-Differential Misclassification of Exposure Variables - Sensitivity

In the imperfect sensitivity example of non-differential misclassification of exposure variables some truly exposed persons are misclassified as unexposed (false negative). Because this happens equally among diseased and non-diseased persons, it is called non-differential misclassification.

The illustrates the effect of non-differential misclassification of exposure in the presence of 2 exposure categories and 70% sensitivity - the bias is towards the null hypothesis in other words, towards 1.0

Non-Differential Misclassification of Exposure Variables - Specificity

In the imperfect specificity example of non-differential misclassification of exposure variables some truly unexposed persons are misclassified as exposed (false positive). Because this happens equally among diseased and non-diseased persons, it is called non-differential misclassification.

This illustrates the effect of non-differential misclassification of exposure because of imperfect specificity in the presence of 2 exposure categories - the bias is towards the null hypothesis , towards 1.0.

Non-Differential Misclassification of Exposure Variables - Imperfect Sensitivity and Specificity

Both imperfect sensitivity and specificity are far more common than imperfect sensitivity or imperfect specificity alone as illustrated in these examples.

A key component affecting the actual sensitivity and specificity of the exposure measurement is the extent of the bias.

Non-Differential Misclassification - Magnitude of Effect of Bias on OR

The table below gives some more examples of what happens with non-differential misclassification of exposure.
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In a scenario where the true Odds Ratio is 4.0, if sensitivity is 90% and specificity is 85% and the prevalence of exposure in the controls is 20%, Keeping specificity fixed but decreasing sensitivity to 60% If you keep sensitivity fixed at 90% but drop specificity from 95% to 60%, again with true prevalence of exposure in the controls of 20%, These last three rows illustrate how problems of misclassification are magnified when the prevalence of exposure is smaller. Hence, when the prevalence of exposure is about 8%, even a seemingly very respectable 90% sensitivity and specificity results in an OR of 2.2, much smaller than the true 4.0.

These are graphical examples of bias from Copeland.

Flegal plots the effect of non-differential misclassification of exposure in a cohort study.

Non-Differential Misclassification of Outcome Variables

When the degree of misclassification of outcome is the same in the exposed vs unexposed groups, i.e. independent of exposure, this is called non differential misclassification of outcome.

Flegal plots the effect of non-differential misclassification of outcome and the effect of incidence.

Non-Differential Misclassification of Outcome Variables in a Cohort or Cross-Sectional Study

When we are using odds ratios there is a predictable bias towards the null hypothesis for both non-differential misclassification of exposure and outcome.

Differential Misclassification of Exposure and Outcome Variables

The table below shows some examples of what can happen in the presence of differential misclassification of exposure.

0121_misclass_table.JPG

Assume that we are looking at an odds ratio in a case-control study and that the true odds ratio is 3.9 and that the prevalence of exposure in the controls is 10%. The bottom line is that unlike non-differential misclassification where the bias is predictably towards the null hypothesis, in the presence of differential misclassification anything can happen with biases both towards and away from the null hypothesis. It all depends upon the individual situation.

An example of differential misclassification of the exposure variables can be seen in the Nurses Health Study of recall bias in retrospective assessment of melanoma risk.

Examples of differential exposure during pregnancy and congential malformations also provide further evidence of misclassification bias.

Measurement Bias in Analytic Studies

In the face of systematic error in an interval scale measurement in an outcome variable, whether or not there is bias depends upon the measure of association in question.

On the other hand, if there are problems in reproducibility or the result of random error, the measure of association will be smaller by a factor of the reproducibility of the exposure measurement.