Random measurement error: Why worry? An example of cardiovascular risk factors
Open accessAbstract
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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The impact of including asymptomatic cases in the unexposed cohort is uncertain and may not necessarily attenuate effect estimate.
“The impact of including asymptomatic cases in the unexposed cohort is uncertain and may not necessarily attenuate effect estimate. 43”
From Risk of cardiovascular outcomes among women with endometriosis in the United Kingdom: a retrospective matched cohort study (2021) · cites this paper as [43]
The Discussion and conclusion says random measurement error can attenuate, exaggerate, or leave an exposure–outcome estimate unaffected, so attenuation is not inevitable. It does not address the specific situation of asymptomatic cases being included in an unexposed cohort; the effect of that situation remains unsupported by this source. (LLM, conf=0.84)
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- Authors
- Brakenhoff, T. B., van Smeden, M., Visseren, F. L. J., & Groenwold, R. H. H.
- Affiliations
- University Medical Center Utrecht (NL)
- Corresponding author
- Brakenhoff, Timo B.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-02-09
- Volume
- 13
- Issue
- 2
- Pages
- e0192298
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5806872
- OpenAlex
- W2791384215
- Cited by
- 52
- References
- 33
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 12 · 2020: 10 · 2019: 9 · 2018: 2
- FWCI
- 6.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion, cc-by)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (917.16.430)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- random error, classical measurement error, attenuation bias, confounding, cardiovascular risk factors, big data, routine care data, direction of effect, exposure measurement error
- MeSH terms
- Aged, Cardiovascular Diseases, Female, Humans, Male, Middle Aged, Risk Factors
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