Methods to adjust for misclassification in the quantiles for the generalized linear model with measurement error in continuous exposures. (22nd November 2015)

• Record Type: Journal Article
• Authors: Wang, Ching‐Yun; Dieu Tapsoba, Jean De; Duggan, Catherine; Campbell, Kristin L; McTiernan, Anne
• Is Part Of: Statistics in medicine. Volume 35:Number 10(2016)
• Page Start: 1676
• Page End: 1688
• Publication Date: 2015-11-22
• Subjects: measurement error -- misclassification -- regression calibration; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6812
• Languages: English
• ISSNs: 0277-6715

Abstract

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 linearization‐based, and it provides a simple and practical correction. Finite sample performance is examined via a simulation study. We apply the methods to a four‐arm randomized clinical trial that tested exercise and weight loss interventions in women aged 50–75years. Copyright © 2015 John Wiley & Sons, Ltd.