Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study

Siddique, J; Daniels, MJ; Carroll, RJ; Raghunathan, TE; Stuart, EA; Freedman, LS

Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study

Siddique, J Daniels, MJ Carroll, RJ Raghunathan, TE Stuart, EA Freedman, LS

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Publication Type:: Journal Article
Citation:: Biometrics, 2019, 75 (3), pp. 927 - 937
Issue Date:: 2019-09-01

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Field	Value	Language
dc.contributor.author	Siddique, J	en_US
dc.contributor.author	Daniels, MJ	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.contributor.author	Raghunathan, TE	en_US
dc.contributor.author	Stuart, EA	en_US
dc.contributor.author	Freedman, LS	en_US
dc.date.available	2019-01-29	en_US
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Biometrics, 2019, 75 (3), pp. 927 - 937	en_US
dc.identifier.issn	0006-341X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136051
dc.description.abstract	© 2019 International Biometric Society In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.	en_US
dc.relation.ispartof	Biometrics	en_US
dc.relation.isbasedon	10.1111/biom.13044	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Sodium	en_US
dc.subject.mesh	Diet	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Longitudinal Studies	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Eating	en_US
dc.subject.mesh	Biomarkers	en_US
dc.subject.mesh	Patient Generated Health Data	en_US
dc.subject.mesh	Bias	en_US
dc.title	Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	75	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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	75	en_US

Abstract:

© 2019 International Biometric Society In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.

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http://hdl.handle.net/10453/136051