Early Experience Analyzing Dietary Intake Data from the Canadian Community Health Survey-Nutrition Using the National Cancer Institute (NCI) Method.

BACKGROUND: One of the underpinning elements to support evidence-based decision-making in food and nutrition is the usual dietary intake of a population. It represents the long-run average consumption of a particular dietary component (i.e., food or nutrient). Variations in individual eating habits are observed from day-to-day and between individuals. The National Cancer Institute (NCI) method uses statistical modeling to account for these variations in estimation of usual intakes. This method was originally developed for nutrition survey data in the United States. The main objective of this study was to apply the NCI method in the analysis of Canadian nutrition surveys. METHODS: Data from two surveys, the 2004 and 2015 Canadian Community Health Survey-Nutrition were used to estimate usual dietary intake distributions from food sources using the NCI method. The effect of different statistical considerations such as choice of the model, covariates, stratification compared to pooling, and exclusion of outliers were assessed, along with the computational time to convergence. RESULTS: A flowchart to aid in model selection was developed. Different covariates (e.g., age/sex groups, cycle, weekday/weekend of the recall) were used to adjust the estimates of usual intakes. Moreover, larger differences in the ratio of within to between variation for a stratified analysis or a pooled analysis resulted in noticeable differences, particularly in the tails of the distribution of usual intake estimates. Outliers were subsequently removed when the ratio was larger than 10. For an individual age/sex group, the NCI method took 1 h-5 h to obtain results depending on the dietary component. CONCLUSION: Early experience in using the NCI method with Canadian nutrition surveys data led to the development of a flowchart to facilitate the choice of the NCI model to use. The ability of the NCI method to include covariates permits comparisons between both 2004 and 2015. This study shows that the improper application of pooling and stratification as well as the outlier detection can lead to biased results. This early experience can provide guidance to other researchers and ensures consistency in the analysis of usual dietary intake in the Canadian context.

International approaches to developing healthy eating patterns for national dietary guidelines.

As part of the revision of the 2007 Eating Well with Canada's Food Guide, a literature scan on statistical modeling approaches used in developing healthy eating patterns for national food guides was conducted. The scan included relevant literature and online searches, primarily since the 2007 Canada's Food Guide was released. Eight countries were identified as utilizing a statistical model or analysis to help inform their healthy eating pattern, defined as the amounts and types of food recommended, with many common characteristics noted. Detail on international modeling approaches is presented, highlighting similarities and differences as well as strengths and challenges.

Stepwise confidence intervals for monotone dose-response studies.

In dose-response studies, one of the most important issues is the identification of the minimum effective dose (MED), where the MED is defined as the lowest dose such that the mean response is better than the mean response of a zero-dose control by a clinically significant difference. Dose-response curves are sometimes monotonic in nature. To find the MED, various authors have proposed step-down test procedures based on contrasts among the sample means. In this article, we improve upon the method of Marcus and Peritz (1976, Journal of the Royal Statistical Society, Series B 38, 157-165) and implement the dose-response method of Hsu and Berger (1999, Journal of the American Statistical Association 94, 468-482) to construct the lower confidence bound for the difference between the mean response of any nonzero-dose level and that of the control under the monotonicity assumption to identify the MED. The proposed method is illustrated by numerical examples, and simulation studies on power comparisons are presented.