Categorizing a continuous predictor subject to measurement error

Achic, BGB; Wang, T; Su, Y; Kipnis, V; Dodd, K; Carroll, RJ

Categorizing a continuous predictor subject to measurement error

Achic, BGB Wang, T Su, Y Kipnis, V Dodd, K Carroll, RJ

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Publication Type:: Journal Article
Citation:: Electronic Journal of Statistics, 2018, 12 (2), pp. 4032 - 4056
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Achic, BGB	en_US
dc.contributor.author	Wang, T	en_US
dc.contributor.author	Su, Y	en_US
dc.contributor.author	Kipnis, V	en_US
dc.contributor.author	Dodd, K	en_US
dc.contributor.author	Carroll, RJ https://orcid.org/0000-0002-5465-9682	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Electronic Journal of Statistics, 2018, 12 (2), pp. 4032 - 4056	en_US
dc.identifier.issn	1935-7524	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134277
dc.description.abstract	© 2018, Institute of Mathematical Statistics. All rights reserved. Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are presented and the methodology is applied to a nutrition data set. Discussion of alternative approaches is also included.	en_US
dc.relation.ispartof	Electronic Journal of Statistics	en_US
dc.relation.isbasedon	10.1214/18-EJS1489	en_US
dc.title	Categorizing a continuous predictor subject to measurement error	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	12	en_US
utslib.for	0104 Statistics	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	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

© 2018, Institute of Mathematical Statistics. All rights reserved. Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are presented and the methodology is applied to a nutrition data set. Discussion of alternative approaches is also included.

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http://hdl.handle.net/10453/134277