### Abstract

Measurement error is well known to cause bias in estimated regression coefficients and a loss of power for detecting associations. Methods commonly used to correct for bias often require auxiliary data. We develop a solution for investigating associations between the change in an imprecisely measured outcome and precisely measured predictors, adjusting for the baseline value of the outcome when auxiliary data are not available. We require the specification of ranges for the reliability or the measurement error variance. The solution allows one to investigate the associations for change and to assess the impact of the measurement error.

Language | English (US) |
---|---|

Pages | 2667-2680 |

Number of pages | 14 |

Journal | Communications in Statistics - Theory and Methods |

Volume | 46 |

Issue number | 6 |

DOIs | |

State | Published - Mar 19 2017 |

### Fingerprint

### Keywords

- Errors in variables
- linear regression
- measurement error variance
- measurement reliability
- method of moments
- sensitivity analysis

### ASJC Scopus subject areas

- Statistics and Probability

### Cite this

*Communications in Statistics - Theory and Methods*,

*46*(6), 2667-2680. DOI: 10.1080/03610926.2015.1040508

**Assessing the impact of measurement error in modeling change in the absence of auxiliary data.** / Yanez, N. David; Aljasser, Ibrahim; Andre, Mose; Hu, Chengcheng; Juraska, Michal; Lumley, Thomas.

Research output: Research - peer-review › Article

*Communications in Statistics - Theory and Methods*, vol 46, no. 6, pp. 2667-2680. DOI: 10.1080/03610926.2015.1040508

}

TY - JOUR

T1 - Assessing the impact of measurement error in modeling change in the absence of auxiliary data

AU - Yanez,N. David

AU - Aljasser,Ibrahim

AU - Andre,Mose

AU - Hu,Chengcheng

AU - Juraska,Michal

AU - Lumley,Thomas

PY - 2017/3/19

Y1 - 2017/3/19

N2 - Measurement error is well known to cause bias in estimated regression coefficients and a loss of power for detecting associations. Methods commonly used to correct for bias often require auxiliary data. We develop a solution for investigating associations between the change in an imprecisely measured outcome and precisely measured predictors, adjusting for the baseline value of the outcome when auxiliary data are not available. We require the specification of ranges for the reliability or the measurement error variance. The solution allows one to investigate the associations for change and to assess the impact of the measurement error.

AB - Measurement error is well known to cause bias in estimated regression coefficients and a loss of power for detecting associations. Methods commonly used to correct for bias often require auxiliary data. We develop a solution for investigating associations between the change in an imprecisely measured outcome and precisely measured predictors, adjusting for the baseline value of the outcome when auxiliary data are not available. We require the specification of ranges for the reliability or the measurement error variance. The solution allows one to investigate the associations for change and to assess the impact of the measurement error.

KW - Errors in variables

KW - linear regression

KW - measurement error variance

KW - measurement reliability

KW - method of moments

KW - sensitivity analysis

UR - http://www.scopus.com/inward/record.url?scp=85002244033&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85002244033&partnerID=8YFLogxK

U2 - 10.1080/03610926.2015.1040508

DO - 10.1080/03610926.2015.1040508

M3 - Article

VL - 46

SP - 2667

EP - 2680

JO - Communications in Statistics - Theory and Methods

T2 - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 6

ER -