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. 2006 Oct;106(10):1575-87.
doi: 10.1016/j.jada.2006.07.003.

A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution

Janet A Tooze  1 , Douglas Midthune, Kevin W Dodd, Laurence S Freedman, Susan M Krebs-Smith, Amy F Subar, Patricia M Guenther, Raymond J Carroll, Victor Kipnis
Affiliations

A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution

Janet A Tooze et al. J Am Diet Assoc. 2006 Oct.

Abstract

Objective: We propose a new statistical method that uses information from two 24-hour recalls to estimate usual intake of episodically consumed foods.

Statistical analyses performed: The method developed at the National Cancer Institute (NCI) accommodates the large number of nonconsumption days that occur with foods by separating the probability of consumption from the consumption-day amount, using a two-part model. Covariates, such as sex, age, race, or information from a food frequency questionnaire, may supplement the information from two or more 24-hour recalls using correlated mixed model regression. The model allows for correlation between the probability of consuming a food on a single day and the consumption-day amount. Percentiles of the distribution of usual intake are computed from the estimated model parameters.

Results: The Eating at America's Table Study data are used to illustrate the method to estimate the distribution of usual intake for whole grains and dark-green vegetables for men and women and the distribution of usual intakes of whole grains by educational level among men. A simulation study indicates that the NCI method leads to substantial improvement over existing methods for estimating the distribution of usual intake of foods.

Conclusions: The NCI method provides distinct advantages over previously proposed methods by accounting for the correlation between probability of consumption and amount consumed and by incorporating covariate information. Researchers interested in estimating the distribution of usual intakes of foods for a population or subpopulation are advised to work with a statistician and incorporate the NCI method in analyses.

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Figures

Figure 1
Figure 1
Figure 1a. Average probability of whole grains consumption by Diet History Questionnaire (DHQ) whole grains frequency group for women in the Eating at America's Table Study. Figure 1b. Average whole grains consumption-day amount (servings) by Diet History Questionnaire (DHQ) whole grains frequency group for women in the Eating at America's Table Study.
Figure 1
Figure 1
Figure 1a. Average probability of whole grains consumption by Diet History Questionnaire (DHQ) whole grains frequency group for women in the Eating at America's Table Study. Figure 1b. Average whole grains consumption-day amount (servings) by Diet History Questionnaire (DHQ) whole grains frequency group for women in the Eating at America's Table Study.
Figure 2
Figure 2
Estimated distributions of usual intake of whole grains for women in Eating at America's Table Study using different methods. The spike at zero for the 4-day mean (within-person mean of 4 24-hour recalls) represents 7.5% of the distribution. (ISUF: Iowa State University Foods method; NCI: National Cancer Institute method with correlated random effects and a food frequency questionnaire as a covariate.)
Figure 3
Figure 3
Bias of percentile estimates from simulations based on whole grains for women (from the Eating at America's Table Study). The dashed line at zero represents no bias. (2 day mean: within-person mean (WPM) of 2 days of simulated 24-hour recalls; ISUF: Iowa State University Foods method; NCI: National Cancer Institute method, specifying whether the person-specific effects are correlated or uncorrelated and whether the simulated Food Propensity Questionnaire (FPQ) is used as a covariate in the model.)
Figure 4
Figure 4
Smoothed distribution curves from simulations based on whole grains for women (from the Eating at America's Table Study). The spike at zero for the two-day mean represents 18% of the distribution. The 365-day mean represents true usual intake. (2 day mean: within-person mean (WPM) of 2 days of simulated 24-hour recalls; ISUF: Iowa State University Foods method; NCI: National Cancer Institute method, specifying whether the person-specific effects are correlated or uncorrelated and whether the simulated Food Propensity Questionnaire (FPQ) is used as a covariate in the model.)
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Comment in

References

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