Effect of measurement error on epidemiological studies of environmental and occupational exposures
Open accessAbstract
Random error (misclassification) in exposure measurements usually biases a relative risk, regression coefficient, or other effect measure towards the null value (no association). The most important exception is Berkson type error, which causes little or no bias. Berkson type error arises, in particular, due to use of group average exposure in place of individual values. Random error in exposure measurements, Berkson or otherwise, reduces the power of a study, making it more likely that real associations are not detected. Random error in confounding variables compromises the control of their effect, leaving residual confounding. Random error in a variable that modifies the effect of exposure on health--for example, an indicator of susceptibility--tends to diminish the observed modification of effect, but error in the exposure can create a supurious appearance of modification. Methods are available to correct for bias (but not generally power loss) due to measurement error, if information on the magnitude and type of error is available. These methods can be complicated to use, however, and should be used cautiously as "correction" can magnify confounding if it is present.
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In all of these articles, bias was characterized as misclassification of exposure to contaminants, an issue noted elsewhere to be a source of Type II error in environmental health research.
“In all of these articles, bias was characterized as misclassification of exposure to contaminants, an issue that has been noted elsewhere to be a source of Type II error in environmental health research.58”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [58]
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Research works printed but not linked: 6
- Armstrong BK, White E, Saracci R. Principles of exposure measurement in epidemiology. Oxford: Oxford University Press, 1992 .
- Lagakos SW. EVects of mismodelling and mismeasuring explanatory variables on tests of their association with a response variable. Stat Med 1988;7:257-74.
- Sorahan T, Gilthorpe MS. Non-diVerential misclassification of exposure always leads to an underestimation of risk: an incorrect conclusion. Occup Environ Med 1994;51:839-40.
- Wacholder S, Hartge P, Lubin JH, et al. Non-diVerential misclassification and bias towards the null: a clarification. Occup Environ Med 1995;52:557-8.
- Armstrong BG. The eVects of measurement errors on rela- tive risk regressions. Am J Epidemiol 1989;132:1176-84.
- de-Klerk NH, English DR, Armstrong BK. A review of the eVects of random measurement error on relative risk estimates in epidemiological studies. Int J Epidemiol 1989;18:705-12.
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“We believe that potential misclassifications in attributing crashes to driver performance are not associated with temperature, and therefore, they are not expected to introduce bias but rather to reduce statistical power to detect the association (Armstrong 1998).”
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- Authors
- Armstrong, B. G.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Armstrong, B G
- ORCID
- Armstrong, B G
- Venue
- Occupational and Environmental Medicine
- Publisher
- BMJ
- Published
- 1998-10
- Volume
- 55
- Issue
- 10
- Pages
- 651-656
- ISSN
- 0007-1072 (ISSN-L)
- 1351-0711
- 1470-7926
- PMC
- PMC1757516
- OpenAlex
- W2109474247
- Cited by
- 682
- References
- 0
- Citations by year
- 2026: 41 · 2025: 41 · 2024: 36 · 2023: 32 · 2022: 38 · 2021: 47 · 2020: 56 · 2019: 35 · 2018: 26 · 2017: 23 · 2016: 29 · 2015: 26 · 2014: 28 · 2013: 32 · 2012: 26
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Occupational and Environmental Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- measurement error, exposure misclassification, Berkson error, statistical power, residual confounding, confounding, effect modification, susceptibility, bias correction, environmental exposure, occupational exposure
- MeSH terms
- Environmental Exposure, Humans, Random Allocation, Risk, Bias, Occupational Exposure
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