Abstract
We consider a Bayesian analysis using WinBUGS to estimate the distribution of usual intake for episodically consumed foods and energy (calories). The model uses measures of nutrition and energy intakes via a food frequency questionnaire (FFQ) along with repeated 24 hour recalls and adjusting covariates. In order to estimate the usual intake of the food, we phrase usual intake in terms of person-specific random effects, along with day-to-day variability in food and energy consumption. Three levels are incorporated in the model. The first level incorporates information about whether an individual in fact reported consumption of a particular food item. The second level incorporates the amount of intake from those individuals who reported consumption of the food, and the third level incorporates the energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations. This R function reports to users point estimates and credible intervals for parameters in the model, samples from their posterior distribution, samples from the distribution of usual intake and usual energy intake, trace plots of parameters and summary statistics of usual intake, usual energy intake and energy adjusted usual intake.
Highlights
There are many statistical challenges when modeling food intakes reported on two or more 24 hours recalls
Some of the challenges involve the presence of measurement error because estimating the distribution of usual intake of nutrients and foods in the population involves monitoring and measuring such intakes over time and their associated recall biases
It is difficult to estimate nutrition intake with recall surveys when one of these recalls incorporate excess zero measurements. Data of this nature is often modeled with measurement error models with zero inflated data
Summary
There are many statistical challenges when modeling food intakes reported on two or more 24 hours recalls. In the second part of the model the amount of that episodically consumed food per day is modeled using linear regression on a transformed scale with a person-specific effect. These two parts are linked allowing that the two person-specific effects are correlated as well as by allowing common covariates in both parts of the model. An extension into a three-part method that incorporates the estimation of the amount of energy intake consumed per day is described in detail by Kipnis et al (2010) These authors estimated this three-part method using nonlinear mixed effects models with likelihoods computed by adaptive Gaussian quadrature in SAS software. The function and corresponding algorithms are explained and an example is provided
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