Methods to adjust for misclassification in the quantiles for the generalized linear model with measurement error in continuous exposures.

ABSTRACT: In many biomedical studies, covariates of interest may be measured with errors. However, frequently in a regression analysis, the quantiles of the exposure variable are often used as the covariates in the regression analysis. Because of measurement errors in the continuous exposure variable, there could be misclassification in the quantiles for the exposure variable. Misclassification in the quantiles could lead to bias estimation in the association between the exposure variable and the outcome variable. Adjustment for misclassification will be challenging when the gold standard variables are not available. In this paper, we develop two regression calibration estimators to reduce bias in effect estimation. The first estimator is normal likelihood-based. The second estimator is linearizatio

SUBMITTER: Wang CY 

PROVIDER: S-EPMC4826813 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature