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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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“When a variable is measured with imprecision, some of the variance might be erroneously attributed to other associated variables that are assessed more precisely (8, 13, 57).”
BackgroundSelf-citationRE: “CIGARETTE SMOKING AND SUICIDE: A PROSPECTIVE STUDY OF 300,000 MALE ACTIVE-DUTY ARMY SOLDIERS”
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“27 The conventional approach to analyzing if treatment effects vary across patients is to stratify patients on single characteristics.”
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“Misunderstanding arises from the apparent weakness of triglycerides as an epidemiological exposure for CVD risk, largely due to its high within-person variability compared with biologically related and more stable factors such as HDL.(10) Therefore, allowing for multivariate models in which triglycerides compete with…”
BackgroundInfluential
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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