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Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores
American Journal of Epidemiology
|September 30, 2020
Summary
Estimating exposure-outcome associations can be biased by measurement error. This study proposes a new method to reduce bias and improve correction efficiency for exposure measurement error, especially when covariates predict exposure.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical modeling
Background:
- Classical measurement error in continuous exposure variables can bias estimates of exposure-outcome associations, particularly when adjusting for confounders.
- Covariate-adjusted estimates are susceptible to bias when covariates are predictors of the exposure.
Purpose of the Study:
- To propose and evaluate a novel approach for estimating marginal exposure-outcome associations in the presence of classical exposure measurement error.
- To demonstrate that the proposed method offers reduced bias compared to covariate-conditional estimates.
- To show improved efficiency in correcting for measurement error when validation data are available.
Main Methods:
- A disease score-based standardization approach is used to estimate marginal exposure-outcome associations.
- The method is evaluated through simulations and an empirical example from the Orinda Longitudinal Study of Myoma.
Main Results:
- The proposed marginal estimate demonstrates less bias due to classical measurement error than the covariate-conditional estimate when covariates predict exposure.
- The marginal estimate allows for more efficient correction of measurement error using validation data.
Conclusions:
- The proposed disease score-based standardization method effectively reduces bias from classical exposure measurement error.
- This approach offers a more efficient way to correct for measurement error in epidemiological studies, enhancing the reliability of association estimates.
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