TY - JOUR

T1 - Estimating the Distribution of Dietary Consumption Patterns

AU - Carroll, Raymond J.

N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2014/2

Y1 - 2014/2

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/598235

UR - http://projecteuclid.org/euclid.ss/1399645722

UR - http://www.scopus.com/inward/record.url?scp=84901393016&partnerID=8YFLogxK

U2 - 10.1214/12-STS413

DO - 10.1214/12-STS413

M3 - Article

C2 - 25309033

VL - 29

SP - 2

EP - 8

JO - Statistical Science

JF - Statistical Science

SN - 0883-4237

IS - 1

ER -