The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
How independent are “independent” effects? relative risk estimation when correlated exposures are measured imprecisely
Abstract
A relative risk estimate which relates an exposure to risk of disease will tend to be estimated too close to unity if that exposure is subject to random measurement error or intra-subject variability. "Independent" relative risk estimates, for the effect of one exposure after adjusting for confounding exposures, may be biased in either direction, depending on the amount of measurement imprecision in the exposure of interest and in the confounders. We describe two methods which estimate the bias in multivariate relative risk estimates due to the effect of measurement imprecision in one or more of the exposure variables in the model. Results from the two methods are compared in an example involving HDL cholesterol, triglycerides and coronary heart disease. In this example, the degree of bias in relative risk estimates is shown to be highly dependent on the amount of measurement imprecision ascribed to the exposures. It is concluded that when two exposures are substantially correlated, and one or both is subject to sizeable measurement imprecision, a study in which exposures are measured only once will be inadequate for investigating the independent effect of the exposures. Where feasible, epidemiologists should seek study populations where the correlation between the exposures is smaller.
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“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 [204]
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Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., Egger, M., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040297 · Cited by 8,613
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677
Self-citationVandenbroucke, J. P., Elm, E. V., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., Egger, M., & initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010-w1 · Cited by 3,543
Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., & Egger, M. (2014) · International Journal of Surgery · doi:10.1016/j.ijsu.2014.07.014 · Cited by 2,969
“]. 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 [”
Metadata
- Authors
- Phillips, A. N., & Smith, G. D.
- Affiliations
- The Royal Free Hospital (GB)
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Phillips, Andrew N.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 1991-01
- Volume
- 44
- Issue
- 11
- Pages
- 1223-1231
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2031403388
- Cited by
- 275
- References
- 35
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 12 · 2023: 9 · 2022: 8 · 2021: 3 · 2020: 10 · 2019: 8 · 2018: 3 · 2017: 8 · 2016: 8 · 2015: 7 · 2014: 2 · 2013: 5 · 2012: 5
- FWCI
- 22.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (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
- relative risk estimation, measurement error, confounding, HDL cholesterol, triglycerides, coronary artery disease
- MeSH terms
- Humans, Cholesterol, HDL, Mathematical Computing, Myocardial Infarction, Probability, Risk, Triglycerides, Bias, Confounding Factors, Epidemiologic, Multivariate Analysis
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