### 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 |
---|---|

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 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Epidemiology
- Statistics and Probability

### Cite this

*Statistics in Medicine*,

*20*(22), 3441-3457. https://doi.org/10.1002/sim.908

**Correcting for measurement error in binary and continuous variables using replicates.** / White, Ian; Frost, Chris; Tokunaga, Shoji.

Research output: Contribution to journal › Article

*Statistics in Medicine*, vol. 20, no. 22, pp. 3441-3457. https://doi.org/10.1002/sim.908

}

TY - JOUR

T1 - Correcting for measurement error in binary and continuous variables using replicates

AU - White, Ian

AU - Frost, Chris

AU - Tokunaga, Shoji

PY - 2001/11/30

Y1 - 2001/11/30

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0035976553&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035976553&partnerID=8YFLogxK

U2 - 10.1002/sim.908

DO - 10.1002/sim.908

M3 - Article

C2 - 11746328

AN - SCOPUS:0035976553

VL - 20

SP - 3441

EP - 3457

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 22

ER -