Measurement error models with interactions

Midthune, D; Carroll, RJ; Freedman, LS; Kipnis, V

Measurement error models with interactions

Midthune, D Carroll, RJ

Freedman, LS Kipnis, V

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Publication Type:: Journal Article
Citation:: Biostatistics, 2016, 17 (2), pp. 277 - 290
Issue Date:: 2016-04-01

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Field	Value	Language
dc.contributor.author	Midthune, D	en_US
dc.contributor.author	Carroll, RJ https://orcid.org/0000-0002-5465-9682	en_US
dc.contributor.author	Freedman, LS	en_US
dc.contributor.author	Kipnis, V	en_US
dc.date.available	2015-10-07	en_US
dc.date.issued	2016-04-01	en_US
dc.identifier.citation	Biostatistics, 2016, 17 (2), pp. 277 - 290	en_US
dc.identifier.issn	1465-4644	en_US
dc.identifier.uri	http://hdl.handle.net/10453/98235
dc.description.abstract	© 2015 Published by Oxford University Press 2015. An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate ($W$) is a linear function of the unobserved true covariate ($X$) plus other covariates ($Z$) in the regression model. In this paper, we consider models for $W$ that include interactions between $X$ and $Z$. We derive the conditional distribution of $X$ given $W$ and $Z$ and use it to extend the method of regression calibration to this class of measurement error models. We apply the model to dietary data and test whether self-reported dietary intake includes an interaction between true intake and body mass index. We also perform simulations to compare the model to simpler approximate calibration models.	en_US
dc.relation.ispartof	Biostatistics	en_US
dc.relation.isbasedon	10.1093/biostatistics/kxv043	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Body Mass Index	en_US
dc.subject.mesh	Calibration	en_US
dc.subject.mesh	Diet	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Regression Analysis	en_US
dc.subject.mesh	Research Design	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.title	Measurement error models with interactions	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	17	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0604 Genetics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

© 2015 Published by Oxford University Press 2015. An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate ($W$) is a linear function of the unobserved true covariate ($X$) plus other covariates ($Z$) in the regression model. In this paper, we consider models for $W$ that include interactions between $X$ and $Z$. We derive the conditional distribution of $X$ given $W$ and $Z$ and use it to extend the method of regression calibration to this class of measurement error models. We apply the model to dietary data and test whether self-reported dietary intake includes an interaction between true intake and body mass index. We also perform simulations to compare the model to simpler approximate calibration models.

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http://hdl.handle.net/10453/98235