Intake_epis_food(): An R Function for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods

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 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 reported consumption of a particular food item. The second level incorporates the amount of food consumption equalling to zero if not consumed, and the third level incorporates the amount of energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations which can be thought as mean estimates for frequentists. 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

A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment

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. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS446 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Binary regression in truncated samples, with application to comparing dietary instruments in a large prospective study. Biometrics

Summary. We examine two issues of importance in nutritional epidemiology: the relationship between dietary fat intake and breast cancer, and the comparison of different dietary assessment instruments, in our case the food frequency questionnaire (FFQ) and the multiple-day food record (FR). The data we use come from women participants in the control group of the Dietary Modification component of the Women's Health Initiative (WHI) Clinical Trial. The difficulty with the analysis of this important data set is that it comes from a truncated sample, namely those women for whom fat intake as measured by the FFQ amounted to 32% or more of total calories. We describe methods that allow estimation of logistic regression parameters in such samples, and also allow comparison of different dietary instruments. Because likelihood approaches that specify the full multivariate distribution can be difficult to implement, we develop approximate methods for both our main problems that are simple to compute and have high efficiency. Application of these approximate methods to the WHI study reveals statistically significant fat and breast cancer relationships when a FR is the instrument used, and demonstrate a marginally significant advantage of the FR over the FFQ in the local power to detect such relationships

Biometrics

this article we introduce a method that, like regression calibration, involves substitution of an estimated value for X in the regression model, but in which the first and second moments of the substituted value are consistent estimates of the first and second moments of X. Because of this central property, we call the method "moment reconstruction." One important advantage of the moment reconstruction approach is that it retains the simplicity of regression calibration, allowing use of standard software, while providing consistent estimation in nonlinear models, when covariates are normally distributed (see below). Other methods, such as corrected score methods (e.g., Huang andW ang, 2001) and full likelihood methods (e.g., Schafer, 1993), provide consistent estimation in more general situations, but require specialized software for implementation. A second advantage of the method is that it enables direct estimation of other regression model parameters such as the residual variance or classification error rates (see our example). A third advantage of the method is that it remains valid under certain types of differential measurement error, unlike other methods currently proposed for use in nonlinear model

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Background Most large cohort studies have used a food frequency questionnaire (FFQ) for assessing dietary intake. Several biomarker studies, however, have cast doubt on whether the FFQ has sufficient precision to allow detection of moderate but important diet-disease associations. We use data from the Observing Protein and Energy Nutrition (OPEN) study to compare the performance of a FFQ with that of a 24-hour recall (24HR)