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Correcting for measurement error in binary and continuous variables using replicates.

I White·C Frost·S Tokunaga
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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.

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BiasCholesterolComputer SimulationCross-Sectional StudiesData Interpretation, StatisticalFemaleHumansMaleMultivariate AnalysisRegression AnalysisTeaJournal Article

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I White, C Frost, S Tokunaga. 2001-11-30. Correcting for measurement error in binary and continuous variables using replicates.. https://doi.org/10.1002/sim.908

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