title: Correcting for measurement error in binary and continuous variables using replicates.
abstract: Measurement error in exposures and confounders leads to bias in regression coefficients. It is possible to adjust for this bias if true values or independent replicates are observed on a subsample. We extend a method suitable for quantitative variables to the situation where both binary and quantitative variables are present. Binary variables with independent replicates introduce two extra problems: (i) the error is correlated with the true value, and (ii) the measurement error probabilities are unidentified if only two replicates are available. We show that - under plausible assumptions - adjustment for error in binary confounders does not need to address these problems. The regression coefficient for a binary exposure is overadjusted if methods for continuous variables are used. Correct adjustment is possible either if three replicates are available, or if further assumptions can be made; otherwise, bounds can be put on the correctly adjusted value, and these bounds are reasonably close together if the exposure has prevalence near 0.5.
authors.affiliation: Medical Statistics Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. ian.white@mrc-bsu.cam.ac.uk
authors.forename: I
authors.initials: I
authors.lastname: White
authors.forename: C
authors.initials: C
authors.lastname: Frost
authors.forename: S
authors.initials: S
authors.lastname: Tokunaga
mesh_terms: D015982:Bias; D002784:Cholesterol / Q000097:blood; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D012044:Regression Analysis; D013662:Tea / Q000378:metabolism
chemical_list: D013662:Tea; D002784:Cholesterol