Type
ArticleAuthors
Carroll, Raymond J.KAUST Grant Number
KUS-CI-016-04Date
2014-02Permanent link to this record
http://hdl.handle.net/10754/598235
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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.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).Publisher
Institute of Mathematical StatisticsJournal
Statistical SciencePubMed ID
25309033PubMed Central ID
PMC4189114ae974a485f413a2113503eed53cd6c53
10.1214/12-STS413
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