Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes

Handle URI:
http://hdl.handle.net/10754/598853
Title:
Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes
Authors:
Kipnis, Victor; Midthune, Douglas; Buckman, Dennis W.; Dodd, Kevin W.; Guenther, Patricia M.; Krebs-Smith, Susan M.; Subar, Amy F.; Tooze, Janet A.; Carroll, Raymond J.; Freedman, Laurence S.
Abstract:
Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.
Citation:
Kipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, et al. (2009) Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes. Biometrics 65: 1003–1010. Available: http://dx.doi.org/10.1111/j.1541-0420.2009.01223.x.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
3-Mar-2009
DOI:
10.1111/j.1541-0420.2009.01223.x
PubMed ID:
19302405
PubMed Central ID:
PMC2881223
Type:
Article
ISSN:
0006-341X
Sponsors:
R.J.C.’s research was supported by a grant from the NationalCancer Institute (CA 57030) and by Award KUS-CI-016-04,made by King Abdullah University of Science and Technology.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorKipnis, Victoren
dc.contributor.authorMidthune, Douglasen
dc.contributor.authorBuckman, Dennis W.en
dc.contributor.authorDodd, Kevin W.en
dc.contributor.authorGuenther, Patricia M.en
dc.contributor.authorKrebs-Smith, Susan M.en
dc.contributor.authorSubar, Amy F.en
dc.contributor.authorTooze, Janet A.en
dc.contributor.authorCarroll, Raymond J.en
dc.contributor.authorFreedman, Laurence S.en
dc.date.accessioned2016-02-25T13:42:28Zen
dc.date.available2016-02-25T13:42:28Zen
dc.date.issued2009-03-03en
dc.identifier.citationKipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, et al. (2009) Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes. Biometrics 65: 1003–1010. Available: http://dx.doi.org/10.1111/j.1541-0420.2009.01223.x.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid19302405en
dc.identifier.doi10.1111/j.1541-0420.2009.01223.xen
dc.identifier.urihttp://hdl.handle.net/10754/598853en
dc.description.abstractDietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.en
dc.description.sponsorshipR.J.C.’s research was supported by a grant from the NationalCancer Institute (CA 57030) and by Award KUS-CI-016-04,made by King Abdullah University of Science and Technology.en
dc.publisherWiley-Blackwellen
dc.subject24-hour recallen
dc.subjectDietary measurement erroren
dc.subjectDietary surveyen
dc.subjectEpisodically consumed foodsen
dc.subjectExcess zero modelsen
dc.subjectFishen
dc.subjectFood frequency questionnaireen
dc.subjectIndividual usual intakeen
dc.subjectMercuryen
dc.subjectNonlinear mixed modelsen
dc.subjectRegression calibrationen
dc.subject.meshModels, Statisticalen
dc.subject.meshEatingen
dc.titleModeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomesen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.identifier.pmcidPMC2881223en
dc.contributor.institutionBiometry, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Boulevard, EPN-3131, Bethesda, Maryland 20892-7354, USA. kipnisv@mail.nih.goven
kaust.grant.numberKUS-CI-016-04en

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