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. 2018 Feb 9;13(2):e0192298.
doi: 10.1371/journal.pone.0192298. eCollection 2018.

Random measurement error: Why worry? An example of cardiovascular risk factors

Timo B Brakenhoff  1 , Maarten van Smeden  1 , Frank L J Visseren  2 , Rolf H H Groenwold  1
Affiliations

Random measurement error: Why worry? An example of cardiovascular risk factors

Timo B Brakenhoff et al. PLoS One. .

Abstract

With the increased use of data not originally recorded for research, such as routine care data (or 'big data'), measurement error is bound to become an increasingly relevant problem in medical research. A common view among medical researchers on the influence of random measurement error (i.e. classical measurement error) is that its presence leads to some degree of systematic underestimation of studied exposure-outcome relations (i.e. attenuation of the effect estimate). For the common situation where the analysis involves at least one exposure and one confounder, we demonstrate that the direction of effect of random measurement error on the estimated exposure-outcome relations can be difficult to anticipate. Using three example studies on cardiovascular risk factors, we illustrate that random measurement error in the exposure and/or confounder can lead to underestimation as well as overestimation of exposure-outcome relations. We therefore advise medical researchers to refrain from making claims about the direction of effect of measurement error in their manuscripts, unless the appropriate inferential tools are used to study or alleviate the impact of measurement error from the analysis.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Relative bias of the exposure-outcome relation when the exposure and confounder contain random measurement error.
The relative bias is expressed as a % of the adjusted exposure-outcome relation when there is no ME (reference standard; see Table 2). The amount of added ME is expressed as a percentage of the total variance of the variable. In (a) and (b) ME is added to the exposure, SBP, and to a confounder; DBP in (a) or ABI in (b). In (c) ME is added to the exposure, CIMT, and a confounder, SBP. Age and sex were additionally included as confounders for all multivariable analyses. Red colors indicate and an underestimation of the exposure-outcome relation due to ME, whereas blue colors indicate an overestimation. ME = measurement error; SBP = systolic blood pressure; DBP = diastolic blood pressure; ABI = ankle-brachial index; CIMT = carotid intima media thickness.
See this image and copyright information in PMC

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