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1.  Taking Advantage of the Strengths of 2 Different Dietary Assessment Instruments to Improve Intake Estimates for Nutritional Epidemiology 
American Journal of Epidemiology  2012;175(4):340-347.
With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1–12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America’s Table Study (1997–1998), the authors found that 4–6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2–4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4–6 administrations of the 24HR should be considered along with administration of an FFQ.
PMCID: PMC3271815  PMID: 22273536
combining dietary instruments; data collection; dietary assessment; energy adjustment; epidemiologic methods; measurement error; nutrient density; nutrient intake
3.  Reactivity and its association with body mass index across days on food checklists 
Characterizing relationships between diet, body weight, and health is complicated by reporting errors in dietary intake data that are associated with body weight. The objectives of this study were to assess changes in reporting across days (reactivity) on food checklists and associations between reactivity and body mass index (BMI) using data from two cross-sectional studies: 1) the Recontacting Participants in the Observing Protein and Energy Nutrition study (n = 297), which was conducted in 2003–2004 and included a 7-day checklist and a 4-day food record (FR), and 2) the America’s Menu Daily Food Report Study (n=530), which was conducted in 1996 and included a 30-day checklist. Zero-inflated Poisson regression was used to assess effects of reporting day on frequency of consumption for the checklists and number of items reported for the FR. Interactions between day and BMI were tested using contrast statements. Frequency of reported consumption declined across days among males and females for total items and many of the eight food groups on the 7-day checklist; among females, the effect of reporting day differed by BMI category for the meat, fish, and poultry group. Smaller declines across days were observed for some of the 22 food groups on the 30-day checklist; no interactions with BMI were apparent. No reporting day effects were observed in the FR data. The results suggest inconsistent reactivity across days, possibly reflecting changes in reporting or consumption behavior. However, the effects are generally small and independent of body weight, suggesting that checklists are potentially useful for the study of body weight and diet.
PMCID: PMC3269781  PMID: 22308230
dietary assessment; food checklist; measurement error; reactivity; body mass index; obesity
4.  Using Regression Calibration Equations That Combine Self-Reported Intake and Biomarker Measures to Obtain Unbiased Estimates and More Powerful Tests of Dietary Associations 
American Journal of Epidemiology  2011;174(11):1238-1245.
The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001--2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature.
PMCID: PMC3224252  PMID: 22047826
bias (epidemiology); carotenoids; cataract; lutein; measurement error; sample size
5.  Associations between food patterns defined by cluster analysis and colorectal cancer incidence in the NIH-AARP Diet and Health Study 
To examine associations between food patterns, constructed with cluster analysis, and colorectal cancer incidence within the National Institutes of Health (NIH)–AARP Diet and Health Study.
A prospective cohort, aged 50–71 years at baseline in 1995–96, followed until the end of 2000.
Subjects and Method
Food patterns were constructed, separately in men (n=293 576) and women (n=198 730), with 181 food variables (daily intake frequency per 1 000 kilocalories) from a food frequency questionnaire. Four large clusters were identified in men and three in women. Cox proportional hazards regression examined associations between patterns and cancer incidence.
In men, a Vegetable and Fruit Pattern was associated with reduced colorectal cancer incidence (multivariate HR: 0.85 95%CI: 0.76, 0.94), when compared to less salutary food choices. Both the Vegetable and Fruit pattern and a Fat-Reduced Foods pattern were associated with reduced rectal cancer incidence in men. In women, a similar Vegetable and Fruit pattern was associated with colorectal cancer protection (age-adjusted HR: 0.82 95%CI: 0.70, 0.95), but the association was not statistically significant in multivariate analysis.
These results, together with findings from previous studies support the hypothesis that micronutrient dense, low-fat, high-fiber food patterns protect against colorectal cancer.
PMCID: PMC3500882  PMID: 18685556
food patterns; cluster analysis; colorectal cancer; prospective cohort
6.  Intake_epis_food(): An R Function for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods 
Journal of statistical software  2012;46(c03):1-17.
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.
PMCID: PMC3403723  PMID: 22837731
excess zero models; MCMC; nonlinear mixed models; R; R2WinBUGS; zero-inflation
7.  Dealing With Dietary Measurement Error in Nutritional Cohort Studies 
Dietary measurement error creates serious challenges to reliably discovering new diet–disease associations in nutritional cohort studies. Such error causes substantial underestimation of relative risks and reduction of statistical power for detecting associations. On the basis of data from the Observing Protein and Energy Nutrition Study, we recommend the following approaches to deal with these problems. Regarding data analysis of cohort studies using food-frequency questionnaires, we recommend 1) using energy adjustment for relative risk estimation; 2) reporting estimates adjusted for measurement error along with the usual relative risk estimates, whenever possible (this requires data from a relevant, preferably internal, validation study in which participants report intakes using both the main instrument and a more detailed reference instrument such as a 24-hour recall or multiple-day food record); 3) performing statistical adjustment of relative risks, based on such validation data, if they exist, using univariate (only for energy-adjusted intakes such as densities or residuals) or multivariate regression calibration. We note that whereas unadjusted relative risk estimates are biased toward the null value, statistical significance tests of unadjusted relative risk estimates are approximately valid. Regarding study design, we recommend increasing the sample size to remedy loss of power; however, it is important to understand that this will often be an incomplete solution because the attenuated signal may be too small to distinguish from unmeasured confounding in the model relating disease to reported intake. Future work should be devoted to alleviating the problem of signal attenuation, possibly through the use of improved self-report instruments or by combining dietary biomarkers with self-report instruments.
PMCID: PMC3143422  PMID: 21653922
8.  Validating an FFQ for intake of episodically consumed foods: application to the National Institutes of Health–AARP Diet and Health Study 
Public Health Nutrition  2011;14(7):1212-1221.
To develop a method to validate an FFQ for reported intake of episodically consumed foods when the reference instrument measures short-term intake, and to apply the method in a large prospective cohort.
The FFQ was evaluated in a sub-study of cohort participants who, in addition to the questionnaire, were asked to complete two non-consecutive 24 h dietary recalls (24HR). FFQ-reported intakes of twenty-nine food groups were analysed using a two-part measurement error model that allows for nonconsumption on a given day, using 24HR as a reference instrument under the assumption that 24HR is unbiased for true intake at the individual level.
The National Institutes of Health–AARP Diet and Health Study, a cohort of 567 169 participants living in the USA and aged 50–71 years at baseline in 1995.
A sub-study of the cohort consisting of 2055 participants.
Estimated correlations of true and FFQ-reported energy-adjusted intakes were 0·5 or greater for most of the twenty-nine food groups evaluated, and estimated attenuation factors (a measure of bias in estimated diet–disease associations) were 0·4 or greater for most food groups.
The proposed methodology extends the class of foods and nutrients for which an FFQ can be evaluated in studies with short-term reference instruments. Although violations of the assumption that the 24HR is unbiased could be inflating some of the observed correlations and attenuation factors, results suggest that the FFQ is suitable for testing many, but not all, diet–disease hypotheses in a cohort of this size.
PMCID: PMC3190597  PMID: 21486523
Diet; Food; Epidemiological methods; Questionnaires; Validation studies
9.  Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components 
There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.
We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole grains. We demonstrate numerically that our methods lead to increased speed of computation, converge to reasonable solutions, and have the flexibility to be used in either a frequentist or a Bayesian manner.
PMCID: PMC3406506  PMID: 22848190
Bayesian approach; latent variables; measurement error; mixed effects models; nutritional epidemiology; zero-inflated data
The annals of applied statistics  2011;5(2B):1456-1487.
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.
The methodology is illustrated through an application to 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. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.
PMCID: PMC3145332  PMID: 21804910
Bayesian methods; Dietary assessment; Latent variables; Measurement error; Mixed models; Nutritional epidemiology; Nutritional surveillance; Zero-Inflated Data
11.  Comparing 3 Dietary Pattern Methods—Cluster Analysis, Factor Analysis, and Index Analysis—With Colorectal Cancer Risk 
American Journal of Epidemiology  2009;171(4):479-487.
The authors compared dietary pattern methods—cluster analysis, factor analysis, and index analysis—with colorectal cancer risk in the National Institutes of Health (NIH)–AARP Diet and Health Study (n = 492,306). Data from a 124-item food frequency questionnaire (1995–1996) were used to identify 4 clusters for men (3 clusters for women), 3 factors, and 4 indexes. Comparisons were made with adjusted relative risks and 95% confidence intervals, distributions of individuals in clusters by quintile of factor and index scores, and health behavior characteristics. During 5 years of follow-up through 2000, 3,110 colorectal cancer cases were ascertained. In men, the vegetables and fruits cluster, the fruits and vegetables factor, the fat-reduced/diet foods factor, and all indexes were associated with reduced risk; the meat and potatoes factor was associated with increased risk. In women, reduced risk was found with the Healthy Eating Index-2005 and increased risk with the meat and potatoes factor. For men, beneficial health characteristics were seen with all fruit/vegetable patterns, diet foods patterns, and indexes, while poorer health characteristics were found with meat patterns. For women, findings were similar except that poorer health characteristics were seen with diet foods patterns. Similarities were found across methods, suggesting basic qualities of healthy diets. Nonetheless, findings vary because each method answers a different question.
PMCID: PMC2842201  PMID: 20026579
colorectal neoplasms; food habits; risk
12.  Interrelationships of added sugars intake, socioeconomic status, and race/ethnicity in adults in the United States: National Health Interview Survey 2005 (ADAJ-D-08-00562R1) 
The consumption of added sugars (e.g., white sugar, brown sugar, high-fructose corn syrup) displaces nutrient-dense foods in the diet. The intake of added sugars in the United States (US) is excessive. Little is known about the predictors of added sugars intake.
To examine the independent relationships of socioeconomic status and race/ethnicity with added sugars intake, and to evaluate the consistency of relationships using a short instrument to those from a different survey using more precise dietary assessment.
Cross-sectional, nationally representative, interviewer-administered survey
Adults (≥18 years) participating in the 2005 US National Health Interview Survey (NHIS) Cancer Control Supplement responding to 4 added sugars questions (n=28,948)
Statistical analyses performed
The intake of added sugars was estimated using validated scoring algorithms. Multivariate analysis incorporating sample weights and design effects was conducted. Least squares means and confidence intervals, and significance tests using Wald F statistics are presented. Analyses were stratified by gender and controlled for potential confounders.
The intake of added sugars was higher among males than females and inversely related to age, educational status, and family income. Asian-Americans had the lowest intake and Hispanics the next lowest intake. Among men, blacks had the highest intake, although whites and American Indians/Alaskan Natives (AI/ANs) also had high intakes. Among women, blacks and AI/ANs had the highest intakes. Intake of added sugars was inversely related to educational attainment in whites, blacks, Hispanic men, and AI/AN men, but was unrelated in Asian-Americans. These findings were generally consistent with relationships in NHANES 2003–04 (using one or two 24-hour dietary recalls).
Race/ethnicity, family income and educational status are independently associated with intake of added sugars. Groups with low income and education are particularly vulnerable to diets with high added sugars. Differences among race/ethnicity groups suggest that interventions to reduce intake of added sugars should be tailored. The NHIS added sugars questions with accompanying scoring algorithms appear to provide an affordable and useful means of assessing relationships between various factors and added sugars intake.
PMCID: PMC2743027  PMID: 19631043
13.  Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes 
Biometrics  2009;65(4):1003-1010.
Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575–1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food’s usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America’s Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.
PMCID: PMC2881223  PMID: 19302405
Dietary measurement error; Dietary survey; Episodically consumed foods; Excess zero models; Food frequency questionnaire; Fish; Individual usual intake; Mercury; Nonlinear mixed models; Regression calibration; 24-hour recall
14.  Binary Regression in Truncated Samples, with Application to Comparing Dietary Instruments in a Large Prospective Study 
Biometrics  2007;64(1):289-298.
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.
PMCID: PMC2714946  PMID: 17651458
Biased sampling; Breast cancer; Case–control studies; Comparison of instruments; Measurement error; Misspecified models; Nutritional epidemiology; Truncation; Women’s Health Initiative
15.  A comparison of regression calibration, moment reconstruction and imputation for adjusting for covariate measurement error in regression 
Statistics in medicine  2008;27(25):5195-5216.
Regression calibration (RC) is a popular method for estimating regression coefficients when one or more continuous explanatory variables, X, are measured with an error. In this method, the mismeasured covariate, W, is substituted by the expectation E(X|W), based on the assumption that the error in the measurement of X is non-differential. Using simulations, we compare three versions of RC with two other ‘substitution’ methods, moment reconstruction (MR) and imputation (IM), neither of which rely on the non-differential error assumption. We investigate studies that have an internal calibration sub-study. For RC, we consider (i) the usual version of RC, (ii) RC applied only to the ‘marker’ information in the calibration study, and (iii) an ‘efficient’ version (ERC) in which the estimators (i) and (ii) are combined. Our results show that ERC is preferable when there is non-differential measurement error. Under this condition, there are cases where ERC is less efficient than MR or IM, but they rarely occur in epidemiology. We show that the efficiency gain of usual RC and ERC over the other methods can sometimes be dramatic. The usual version of RC carries similar efficiency gains to ERC over MR and IM, but becomes unstable as measurement error becomes large, leading to bias and poor precision. When differential measurement error does pertain, then MR and IM have considerably less bias than RC, but can have much larger variance. We demonstrate our findings with an analysis of dietary fat intake and mortality in a large cohort study.
PMCID: PMC2676235  PMID: 18680172
differential measurement error; moment reconstruction; multiple imputation; non-differential measurement error; regression calibration
16.  A new method for estimating the usual intake of episodically-consumed foods with application to their distribution 
We propose a new statistical method that uses information from two 24-hour recalls (24HRs) 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 non-consumption days that arise 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 (FFQ), may supplement the information from two or more 24HRs 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.
The Eating at America's Table Study (EATS) 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.
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.
PMCID: PMC2517157  PMID: 17000190
Usual intake; Episodically-consumed foods; statistical methods

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