A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design

The partial potential impact fraction describes the proportion of disease cases that can be prevented if the distribution of modifiable continuous exposures is shifted in a population, while other risk factors are not modified. It is a useful quantity for evaluating the burden of disease in epidemiologic and public health studies. When exposures are measured with error, the partial potential impact fraction estimates may be biased, which necessitates methods to correct for the exposure measurement error. Motivated by the health professionals follow-up study, we develop a Bayesian approach to adjust for exposure measurement error when estimating the partial potential impact fraction under the main study/internal validation study design. We adopt the reclassification approach that leverages the strength of the main study/internal validation study design and clarifies transportability assumptions for valid inference. We assess the finite-sample performance of both the point and credible interval estimators via extensive simulations and apply the proposed approach in the health professionals follow-up study to estimate the partial potential impact fraction for colorectal cancer incidence under interventions exploring shifting the distributions of red meat, alcohol, and/or folate intake.

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A Method to Adjust for Measurement Error in Three Exposures Measured with Correlated Errors in the Absence of Internal Validation Study

10.21203/rs.2.16959/v1 ◽

2019 ◽

Author(s):

Alexander K. Muoka ◽

George Agogo ◽

Oscar Ngesa ◽

Henry Mwambi

Keyword(s):

Measurement Error ◽

Validation Study ◽

Real Data ◽

Epidemiological Studies ◽

Correlated Errors ◽

Exposure Measurement ◽

Internal Validation ◽

Exposure Measurement Error ◽

Correlated Exposures

Abstract Difficulty in obtaining the correct measurement for an individual's long-term exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We proposed a method (trivariate method) that adjusts for measurement error in three correlated exposures in the absence of internal validation study and illustrated the method using real data. We compared the results from the proposed method with those obtained using a method that ignores measurement error and a method that ignores correlations between the errors and true exposures (the univariate method). It was found that ignoring measurement error leads to bias and underestimates the standard error. It was also found that the magnitude of adjustment in the trivariate method is sensitive to the magnitude of measurement error, sign and correlation between the errors. We conclude that the proposed method can be used to adjust for bias in the outcome-exposure association in a case where three exposures are measured with correlated errors in the absence of an internal validation study. The method is useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

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