Correction for covariate measurement error in generalized linear models--a bootstrap approach

A two-phase bootstrap method is proposed for correcting covariate measurement error. Two data sets are needed: validation data for approximating the measurement model and data with a response variable. Bootstrap samples from both the data sets validation data are taken. Parameter estimates of the generalized linear model are calculated using expectations of the measurement model from the validation data as explanatory variables. The method is compared through simulation in logistic regression with the correction method proposed by Rosner, Willet, and Spiegelman (1991, Statistics in Medicine 8, 1051-1069). A real data example is also presented.