Statistical Science

Estimating the Distribution of Dietary Consumption Patterns

Raymond J. Carroll

Full-text: Open access

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.

Article information

Source
Statist. Sci., Volume 29, Number 1 (2014), 2-8.

Dates
First available in Project Euclid: 9 May 2014

Permanent link to this document
https://projecteuclid.org/euclid.ss/1399645722

Digital Object Identifier
doi:10.1214/12-STS413

Mathematical Reviews number (MathSciNet)
MR3201840

Zentralblatt MATH identifier
1332.62406

Keywords
Bayesian methods dietary assessment latent variables measurement error mixed models nutritional epidemiology nutritional surveillance zero-inflated data

Citation

Carroll, Raymond J. Estimating the Distribution of Dietary Consumption Patterns. Statist. Sci. 29 (2014), no. 1, 2--8. doi:10.1214/12-STS413. https://projecteuclid.org/euclid.ss/1399645722


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References

  • Fungwe, T., Guenther, P. M., Juan, W. Y., Hiza, H. and Lino, M. (2009). The quality of children’s diets in 2003–04 as measured by the Healthy Eating Index-2005. In Nutrition Insight 43. USDA Center for Nutrition Policy and Promotion, Alexandria, VA.
  • Guenther, P. M., Reedy, J. and Krebs-Smith, S. M. (2008). Development of the Healthy Eating Index-2005. Journal of the American Dietetic Association 108 1896–1901.
  • Guenther, P. M., Reedy, J., Krebs-Smith, S. M. and Reeve, B. B. (2008). Evaluation of the Healthy Eating Index-2005. Journal of the American Dietetic Association 108 1854–1864.
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  • Zhang, S., Krebs-Smith, S. M., Midthune, D., Perez, A., Buckman, D. W., Kipnis, V., Freedman, L. S., Dodd, K. W. and Carroll, R. J. (2011a). Fitting a bivariate measurement error model for episodically consumed dietary components. Int. J. Biostat. 7 Art. 1, 34.
  • Zhang, S., Midthune, D., Guenther, P. M., Krebs-Smith, S. M., Kipnis, V., Dodd, K. W., Buckman, D. W., Tooze, J. A., Freedman, L. and Carroll, R. J. (2011b). A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment. Ann. Appl. Stat. 5 1456–1487.

See also

  • Discussion of: Estimating the Distribution of Dietary Consumption Patterns.
  • Reply to the Discussion of: Estimating the Distribution of Dietary Consumption Patterns.