The effects of measurement error in response variables and tests of association of explanatory variables in change models

Norbert Yanez, Richard A. Kronmal, Lynn R. Shemanski

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Biomedical studies often measure variables with error. Examples in the literature include investigation of the association between the change in some outcome variable (blood pressure, cholesterol level etc.) and a set of explanatory variables (age, smoking status etc.). Typically, one fits linear regression models to investigate such associations. With the outcome variable measured with error, a problem occurs when we include the baseline value of the outcome variable as a covariate. In such instances, one can find a relationship between the observed change in the outcome and the explanatory variables even when there is no association between these variables and the true change in the outcome variable. We present a simple method of adjusting for a common measurement error bias that tends to be overlooked in the modelling of associations with change. Additional information (for example, replicates, instrumental variables) is needed to estimate the variance of the measurement error to perform this bias correction.

Original languageEnglish (US)
Pages (from-to)2597-2606
Number of pages10
JournalStatistics in Medicine
Volume17
Issue number22
DOIs
StatePublished - Nov 30 1998
Externally publishedYes

Fingerprint

Measurement Error
Linear Models
Smoking
Cholesterol
Blood Pressure
Model
Bias Correction
Instrumental Variables
Linear Regression Model
Covariates
Baseline
Tend
Modeling
Estimate

ASJC Scopus subject areas

  • Epidemiology

Cite this

The effects of measurement error in response variables and tests of association of explanatory variables in change models. / Yanez, Norbert; Kronmal, Richard A.; Shemanski, Lynn R.

In: Statistics in Medicine, Vol. 17, No. 22, 30.11.1998, p. 2597-2606.

Research output: Contribution to journalArticle

@article{a443e021bc904649a04c824f24db5249,
title = "The effects of measurement error in response variables and tests of association of explanatory variables in change models",
abstract = "Biomedical studies often measure variables with error. Examples in the literature include investigation of the association between the change in some outcome variable (blood pressure, cholesterol level etc.) and a set of explanatory variables (age, smoking status etc.). Typically, one fits linear regression models to investigate such associations. With the outcome variable measured with error, a problem occurs when we include the baseline value of the outcome variable as a covariate. In such instances, one can find a relationship between the observed change in the outcome and the explanatory variables even when there is no association between these variables and the true change in the outcome variable. We present a simple method of adjusting for a common measurement error bias that tends to be overlooked in the modelling of associations with change. Additional information (for example, replicates, instrumental variables) is needed to estimate the variance of the measurement error to perform this bias correction.",
author = "Norbert Yanez and Kronmal, {Richard A.} and Shemanski, {Lynn R.}",
year = "1998",
month = "11",
day = "30",
doi = "10.1002/(SICI)1097-0258(19981130)17:22<2597::AID-SIM940>3.0.CO;2-G",
language = "English (US)",
volume = "17",
pages = "2597--2606",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "22",

}

TY - JOUR

T1 - The effects of measurement error in response variables and tests of association of explanatory variables in change models

AU - Yanez, Norbert

AU - Kronmal, Richard A.

AU - Shemanski, Lynn R.

PY - 1998/11/30

Y1 - 1998/11/30

N2 - Biomedical studies often measure variables with error. Examples in the literature include investigation of the association between the change in some outcome variable (blood pressure, cholesterol level etc.) and a set of explanatory variables (age, smoking status etc.). Typically, one fits linear regression models to investigate such associations. With the outcome variable measured with error, a problem occurs when we include the baseline value of the outcome variable as a covariate. In such instances, one can find a relationship between the observed change in the outcome and the explanatory variables even when there is no association between these variables and the true change in the outcome variable. We present a simple method of adjusting for a common measurement error bias that tends to be overlooked in the modelling of associations with change. Additional information (for example, replicates, instrumental variables) is needed to estimate the variance of the measurement error to perform this bias correction.

AB - Biomedical studies often measure variables with error. Examples in the literature include investigation of the association between the change in some outcome variable (blood pressure, cholesterol level etc.) and a set of explanatory variables (age, smoking status etc.). Typically, one fits linear regression models to investigate such associations. With the outcome variable measured with error, a problem occurs when we include the baseline value of the outcome variable as a covariate. In such instances, one can find a relationship between the observed change in the outcome and the explanatory variables even when there is no association between these variables and the true change in the outcome variable. We present a simple method of adjusting for a common measurement error bias that tends to be overlooked in the modelling of associations with change. Additional information (for example, replicates, instrumental variables) is needed to estimate the variance of the measurement error to perform this bias correction.

UR - http://www.scopus.com/inward/record.url?scp=0032583151&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032583151&partnerID=8YFLogxK

U2 - 10.1002/(SICI)1097-0258(19981130)17:22<2597::AID-SIM940>3.0.CO;2-G

DO - 10.1002/(SICI)1097-0258(19981130)17:22<2597::AID-SIM940>3.0.CO;2-G

M3 - Article

C2 - 9839350

AN - SCOPUS:0032583151

VL - 17

SP - 2597

EP - 2606

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 22

ER -