Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process

Potgieter, CJ; Wei, R; Kipnis, V; Freedman, LS; Carroll, RJ

Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process

Potgieter, CJ Wei, R Kipnis, V Freedman, LS Carroll, RJ

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Publication Type:: Journal Article
Citation:: Biometrics, 2016, 72 (4), pp. 1369 - 1377
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Potgieter, CJ	en_US
dc.contributor.author	Wei, R	en_US
dc.contributor.author	Kipnis, V	en_US
dc.contributor.author	Freedman, LS	en_US
dc.contributor.author	Carroll, RJ https://orcid.org/0000-0002-5465-9682	en_US
dc.date.available	2016-02-01	en_US
dc.date.issued	2016-12-01	en_US
dc.identifier.citation	Biometrics, 2016, 72 (4), pp. 1369 - 1377	en_US
dc.identifier.issn	0006-341X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/98700
dc.description.abstract	© 2016, The International Biometric Society For the classical, homoscedastic measurement error model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to measurement error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects modeling process, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. This general model includes classical measurement errors, Berkson measurement errors, mixtures of Berkson and classical errors and problems that are not measurement error problems, but also cases where the data-generating process for true exposure is a complex, nonlinear random effects modeling process. The methods are illustrated using the National Institutes of Health–AARP Diet and Health Study where the latent variable is a dietary pattern score called the Healthy Eating Index-2005. We also show how our general model includes methods used in radiation epidemiology as a special case. Simulations are used to illustrate the methods.	en_US
dc.relation.ispartof	Biometrics	en_US
dc.relation.isbasedon	10.1111/biom.12524	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Nutrition Surveys	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Logistic Models	en_US
dc.subject.mesh	Regression Analysis	en_US
dc.subject.mesh	Feeding Behavior	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.title	Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	72	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	72	en_US

Abstract:

© 2016, The International Biometric Society For the classical, homoscedastic measurement error model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to measurement error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects modeling process, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. This general model includes classical measurement errors, Berkson measurement errors, mixtures of Berkson and classical errors and problems that are not measurement error problems, but also cases where the data-generating process for true exposure is a complex, nonlinear random effects modeling process. The methods are illustrated using the National Institutes of Health–AARP Diet and Health Study where the latent variable is a dietary pattern score called the Healthy Eating Index-2005. We also show how our general model includes methods used in radiation epidemiology as a special case. Simulations are used to illustrate the methods.

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http://hdl.handle.net/10453/98700