Original article
How independent are “independent” effects? relative risk estimation when correlated exposures are measured imprecisely

https://doi.org/10.1016/0895-4356(91)90155-3Get rights and content

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.

References (33)

  • D.A. Savitz et al.

    Estimating and correcting for confounder misclassification

    Am J Epidemiol

    (1989)
  • K.Y. Fung et al.

    Methodological issues in case-control studies III: The effect of joint misclassification of risk factors and confounding factors upon estimation and power

    Int J Epidemiol

    (1984)
  • S. Greenland et al.

    Confounding and misclassification

    Am J Epidemiol

    (1985)
  • S. Greenland

    The effect of misclassification in the presence of covariates

    Am J Epidemiol

    (1980)
  • A. Tzonou et al.

    Misclassification in case-control studies with two dichotomous risk factors

    Rev Epidemiol Same Publ

    (1986)
  • K. Liu

    Measurement error and its impact on partial correlation and multiple linear regression analysis

    Am J Epidemdol

    (1988)
  • Cited by (237)

    • Non-linear association of serum molybdenum and linear association of serum zinc with nonalcoholic fatty liver disease: Multiple-exposure and Mendelian randomization approach

      2020, Science of the Total Environment
      Citation Excerpt :

      MR method uses genetic variants as IV to estimate the effect of an exposure on an outcome, consequently making causal inferences (Haycock et al., 2016). This method could well solve the limitations of traditional epidemiological study design (cohort study, case-control study, etc.), such as potential or unmeasured confounders, reverse causation bias, and measurement error, which severely constrained the ability to infer causality (Phillips and Smith, 1991; Smith and Ebrahim, 2002). Previous studies have identified five zinc-related SNPs, but not molybdenum, in European populations.

    • Residual risks and evolving atherosclerotic plaques

      2023, Molecular and Cellular Biochemistry
    View all citing articles on Scopus
    View full text