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

• Type of Material: Article
• Authors: Maarten van Smeden, Leiden University; Timothy Lash, Emory University; Rolf H. H. Groenwold, Leiden University
• Language: English
• Date: 2020-02-01
• Publisher: Oxford University Press
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/ije/dyz251
• Title of Journal or Parent Work: International Journal of Epidemiology
• Volume: 49
• Issue: 1
• Start Page: 338
• End Page: 347
• Grant/Funding Information: T.L.L. was supported, in part, by R01LM013049 from the US National Library of Medicine.; R.H.H.G. was funded by the Netherlands Organization for Scientific Research (NWO-Vidi project 917.16.430).
• Keywords: Measurement error; Life Sciences & Biomedicine; Always bias; bias; bias corrections; Multiple imputation; misclassification; Impact; Science & Technology; Public, Environmental & Occupational Health; Association; Nondifferential misclassification; misconceptions; Blood pressure; Exposure measurement error; Variables; Regression; Dietary measurement error
• Research Categories: Health Sciences, Epidemiology; Engineering, Biomedical; Biology, Biostatistics; Biology, Bioinformatics

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.