Correcting for measurement error in fractional polynomial models using Bayesian modelling and regression calibration, with an application to alcohol and mortality.

ABSTRACT: Exposure measurement error can result in a biased estimate of the association between an exposure and outcome. When the exposure-outcome relationship is linear on the appropriate scale (e.g. linear, logistic) and the measurement error is classical, that is the result of random noise, the result is attenuation of the effect. When the relationship is non-linear, measurement error distorts the true shape of the association. Regression calibration is a commonly used method for correcting for measurement error, in which each individual's unknown true exposure in the outcome regression model is replaced by its expectation conditional on the error-prone measure and any fully measured covariates. Regression calibration is simple to execute when the exposure is untransformed in the linear predictor

SUBMITTER: Gray CM 

PROVIDER: S-EPMC6511281 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature