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. 2022 Jul 23;191(8):1485-1495.
doi: 10.1093/aje/kwac035.

Misconceptions About the Direction of Bias From Nondifferential Misclassification

Jennifer J Yland, Amelia K Wesselink, Timothy L Lash, Matthew P Fox

Misconceptions About the Direction of Bias From Nondifferential Misclassification

Jennifer J Yland et al. Am J Epidemiol. .

Erratum in

Abstract

Measurement error is pervasive in epidemiologic research. Epidemiologists often assume that mismeasurement of study variables is nondifferential with respect to other analytical variables and then rely on the heuristic that "nondifferential misclassification will bias estimates towards the null." However, there are many exceptions to the heuristic for which bias towards the null cannot be assumed. In this paper, we compile and characterize 7 exceptions to this rule and encourage analysts to take a more critical and nuanced approach to evaluating and discussing bias from nondifferential mismeasurement.

Keywords: bias (epidemiology); epidemiologic methods; information bias; measurement error; nondifferential misclassification; statistics.

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Figures

Figure 1
Figure 1
Results from an observation-level simulation of nondifferential exposure misclassification, using 3 example data sets of varying sizes (Table 1) and a range of sensitivities. The figure shows density plots of the misclassified risk ratios (RRs) from 10,000 simulations. We simulated the classified version of exposure by drawing from binomial distributions with probabilities equal to the specified specificity and sensitivity. Each analysis was repeated at 3 different sample sizes but with the true RR equal to 2.0 and the same distribution of the exposure and outcome in each. A) Exposure specificity = 90%; exposure sensitivity = 80%; true RR = 2.0. B) Exposure specificity = 90%; exposure sensitivity = 70%; true RR = 2.0. C) Exposure specificity = 90%; exposure sensitivity = 60%; true RR = 2.0. The vertical dashed line denotes the true RR (RR = 2.0) in each panel. The mode of the density distribution for simulations with 100 participants is slightly shifted compared with simulations with 1,000 or 10,000 participants because binomial sampling does not allow for decimal fractions; smaller sample sizes are more sensitive to the discrete nature of the binomial distribution.
Figure 2
Figure 2
Rearrangement of exposure and outcome classification in a 2 × 2 table resulting from both exposure and outcome misclassification. Adapted from Kristensen (18).
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Comment in

References

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