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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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Self-citationLife course approaches to socioeconomic differentials in health
Smith, G. D., & Lynch, J. (2004) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198578154.003.0004 · Cited by 92
Self-citationEarly Life Exposures and Adult Cancer Risk
Clarke, M. A., & Joshu, C. E. (2017) · Epidemiologic Reviews · doi:10.1093/epirev/mxx004 · Cited by 92
Systematic reviews and meta-analysis
Egger, M., Smith, G. D., & Sterne, J. A. C. (2009) · Oxford University Press eBooks · doi:10.1093/med/9780199218707.003.0039 · Cited by 90
Self-citationHazelwood, E., Sanderson, E. C. M., Tan, V. Y., Ruth, K. S., Frayling, T. M., Dimou, N. L., Gunter, M. J., Dossus, L., Newton, C. L., Ryan, N. A. J., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02322-3 · Cited by 83
Self-citationEstimating the effects of misclassification
Mertens, T. E. (1993) · The Lancet · doi:10.1016/0140-6736(93)92820-j · Cited by 80
Body mass index and psychiatric disorders: a Mendelian randomization study
Hartwig, F. P., Bowden, J., de Mola, C. L., Tovo‐Rodrigues, L., Smith, G. D., & Horta, B. L. (2016) · Scientific Reports · doi:10.1038/srep32730 · Cited by 77
Self-citationBurchfiel, C. M., Laws, A., Benfante, R., Goldberg, R. J., Hwang, L., Chiu, D., Rodríguez, B. L., Curb, J. D., & Sharp, D. S. (1995) · Circulation · doi:10.1161/01.cir.92.6.1430 · Cited by 78
Bias in relative odds estimation owing to imprecise measurement of correlated exposures
Phillips, A. N., & Smith, G. D. (1992) · Statistics in Medicine · doi:10.1002/sim.4780110712 · Cited by 108
Self-citationHarari, G., Green, M. S., & Zelber‐Sagi, S. (2015) · Occupational and Environmental Medicine · doi:10.1136/oemed-2014-102613 · Cited by 79
Psychological Distress and Mortality: Are Women More Vulnerable?
Ferraro, K. F., & Nuriddin, T. A. (2006) · Journal of Health and Social Behavior · doi:10.1177/002214650604700303 · Cited by 76
“It is also possible that part of the inconsistency in conclusions from previous research stems from divergent measures of psychological distress and the measurement error associated with each scale (Phillips and Smith 1991).”
ResultAnxiety, depression, and prognosis after myocardial infarction
Lane, D. A., Carroll, D., & Lip, G. Y. H. (2003) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2003.08.018 · Cited by 76
McInerny, G., & Purves, D. W. (2011) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00077.x · Cited by 74
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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