Reflection on modern methods: five myths about measurement error in epidemiological research

van Smeden, Maarten; Lash, Timothy L; Groenwold, Rolf H H

doi:10.1093/ije/dyz251

Cited by 173Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Reflection on modern methods: five myths about measurement error in epidemiological research

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van Smeden, Maarten, Lash, Timothy L, Groenwold, Rolf H H·2020·International Journal of Epidemiology

DOI 10.1093/ije/dyz251PMC PMC7124512

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Abstract

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study’s inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

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Measurement errors can reduce the model's predictive performance and potential usefulness.
“This means that the model's predictive performance and potential usefulness could be reduced.49”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [49]
Methods for correcting measurement errors typically require additional data and assumptions.
“While methods for correcting measurement errors have been proposed, they typically require additional data and assumptions.49”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [49]

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Authors: van Smeden, M., Lash, T. L., & Groenwold, R. H. H.
Affiliations: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Emory University (US)
Leiden University Medical Center (NL)
ORCID: Lash, Timothy L
Groenwold, Rolf H H
Venue: International Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 2019-12-10
Volume: 49
Issue: 1
Pages: 338-347
ISSN: 0300-5771 (ISSN-L)
1464-3685
DOI: 10.1093/ije/dyz251
PMC: PMC7124512
OpenAlex: W2994811916
Cited by: 173
References: 101
Citations by year: 2026: 20 · 2025: 17 · 2024: 31 · 2023: 37 · 2022: 17 · 2021: 28 · 2020: 22 · 2019: 1
FWCI: 13.26
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
Locations: International Journal of Epidemiology (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Leiden Repository (Leiden University) (submittedVersion, cc-by-nc)
Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc)
Funders: Netherlands Organization for Scientific Research (R01LM013049)
National Library of Medicine
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: measurement error, attenuation bias, effect estimates, measurement error correction, quantitative bias analysis, data quality
MeSH terms: Epidemiologic Methods, Humans, Models, Statistical, Epidemiologic Research Design, Bias, Epidemiologic Studies

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