## 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.

Original language | English |
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Pages (from-to) | 3441-3457 |

Number of pages | 17 |

Journal | Statistics in Medicine |

Volume | 20 |

Issue number | 22 |

DOIs | |

Publication status | Published - Nov 30 2001 |

## All Science Journal Classification (ASJC) codes

- Epidemiology
- Statistics and Probability