Estimating the Distribution of Dietary Consumption Patterns

Handle URI:
http://hdl.handle.net/10754/598235
Title:
Estimating the Distribution of Dietary Consumption Patterns
Authors:
Carroll, Raymond J.
Abstract:
In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. We were interested in estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States, using a national survey and incorporating survey weights. We developed a highly nonlinear, multivariate zero-inflated data model with measurement error to address this question. Standard nonlinear mixed model software such as SAS NLMIXED cannot handle this problem. We found that taking a Bayesian approach, and using MCMC, resolved the computational issues and doing so enabled us to provide a realistic distribution estimate for the HEI-2005 total score. While our computation and thinking in solving this problem was Bayesian, we relied on the well-known close relationship between Bayesian posterior means and maximum likelihood, the latter not computationally feasible, and thus were able to develop standard errors using balanced repeated replication, a survey-sampling approach.
Citation:
Carroll RJ (2014) Estimating the Distribution of Dietary Consumption Patterns. Statist Sci 29: 2–8. Available: http://dx.doi.org/10.1214/12-STS413.
Publisher:
Institute of Mathematical Statistics
Journal:
Statistical Science
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Feb-2014
DOI:
10.1214/12-STS413
PubMed ID:
25309033
PubMed Central ID:
PMC4189114
Type:
Article
ISSN:
0883-4237
Sponsors:
Carroll's research was supported by a grant from the National Cancer Institute (R27-CA057030). He thanks the co-authors of Zhang et al. (2011b) for their work on the project. This publication is based in part on work supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST), and also in part by the Spanish Ministry of Science and Innovation (project MTM 2011-22664 which is co-funded by FEDER).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorCarroll, Raymond J.en
dc.date.accessioned2016-02-25T13:17:07Zen
dc.date.available2016-02-25T13:17:07Zen
dc.date.issued2014-02en
dc.identifier.citationCarroll RJ (2014) Estimating the Distribution of Dietary Consumption Patterns. Statist Sci 29: 2–8. Available: http://dx.doi.org/10.1214/12-STS413.en
dc.identifier.issn0883-4237en
dc.identifier.pmid25309033en
dc.identifier.doi10.1214/12-STS413en
dc.identifier.urihttp://hdl.handle.net/10754/598235en
dc.description.abstractIn the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. We were interested in estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States, using a national survey and incorporating survey weights. We developed a highly nonlinear, multivariate zero-inflated data model with measurement error to address this question. Standard nonlinear mixed model software such as SAS NLMIXED cannot handle this problem. We found that taking a Bayesian approach, and using MCMC, resolved the computational issues and doing so enabled us to provide a realistic distribution estimate for the HEI-2005 total score. While our computation and thinking in solving this problem was Bayesian, we relied on the well-known close relationship between Bayesian posterior means and maximum likelihood, the latter not computationally feasible, and thus were able to develop standard errors using balanced repeated replication, a survey-sampling approach.en
dc.description.sponsorshipCarroll's research was supported by a grant from the National Cancer Institute (R27-CA057030). He thanks the co-authors of Zhang et al. (2011b) for their work on the project. This publication is based in part on work supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST), and also in part by the Spanish Ministry of Science and Innovation (project MTM 2011-22664 which is co-funded by FEDER).en
dc.publisherInstitute of Mathematical Statisticsen
dc.subjectMeasurement erroren
dc.subjectDietary Assessmenten
dc.subjectMixed Modelsen
dc.subjectNutritional Epidemiologyen
dc.subjectLatent Variablesen
dc.subjectBayesian Methodsen
dc.subjectZero-inflated Dataen
dc.subjectNutritional Surveillanceen
dc.titleEstimating the Distribution of Dietary Consumption Patternsen
dc.typeArticleen
dc.identifier.journalStatistical Scienceen
dc.identifier.pmcidPMC4189114en
dc.contributor.institutionTexas A&M University.en
kaust.grant.numberKUS-CI-016-04en

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