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A note on correlated errors in exposure and outcome in logistic regression
1Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway. magne.thoresen@medsin.uio.no
American Journal of Epidemiology
|June 20, 2007
Summary
Dependent misclassification in exposure and health status can bias study results. This research explores how correlated errors in continuous exposure and outcome measurements affect association estimates, offering a method for consistent effect estimation.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Measurement errors in exposure and health status are common in cross-sectional and questionnaire-based studies.
- These errors can be related due to systematic reporting biases by subjects.
Purpose of the Study:
- To investigate the impact of dependent misclassification on the estimated association between continuous exposure and health outcomes.
- To derive explicit expressions for bias in different scenarios of correlated measurement errors.
Main Methods:
- Utilized a threshold model assuming an underlying linear relation between exposure and response.
- Incorporated correlated errors for continuous exposure and outcome, with covariates measured without error.
Main Results:
- Derived explicit formulas for bias in the estimated exposure-outcome association.
- Demonstrated that correlated errors can lead to both over- and underestimation of the true relationship.
Conclusions:
- Dependent misclassification significantly biases exposure-outcome association estimates.
- A study design is proposed that allows for consistent estimation of the true effect, mitigating bias.
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