Bias in relative odds estimation owing to imprecise measurement of correlated exposures
Abstract
A series of graphs is presented that show the estimated degree of bias in logistic coefficient estimates for two correlated continuous exposures measured with imprecision. These graphs indicate that even when the correlation coefficient between the exposure of interest and a correlated exposure is as low as 0·2, imprecision in the measurement of the latter exposure can result in at least as serious bias in the logistic coefficient estimate for the exposure of interest as measurement imprecision in the exposure of interest itself. The implications for the design and interpretation of epidemiological studies are discussed.
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When correlated risk factors are measured with different degrees of imprecision, the adjusted relative risk associated with them can be biased towards or away from unity.
“However, when correlated risk factors are measured with different degrees of imprecision, the adjusted relative risk associated with them can be biased towards or away from unity [ 204 – 206 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [205]
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References 21 · Cited by 108
- Authors
- Phillips, A. N., & Smith, G. D.
- Affiliations
- The Royal Free Hospital (GB)
- London School of Hygiene & Tropical Medicine (GB)
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1992-01
- Volume
- 11
- Issue
- 7
- Pages
- 953-961
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2136818924
- Cited by
- 108
- References
- 21
- Citations by year
- 2024: 3 · 2022: 1 · 2021: 2 · 2020: 4 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 5 · 2012: 2
- FWCI
- 12.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- 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
- measurement error, logistic regression, bias, continuous exposure, research design, epidemiological interpretation
- MeSH terms
- Epidemiologic Methods, Humans, Research Design, Bias, Confounding Factors, Epidemiologic, Logistic Models, Odds Ratio
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