Advanced Dietary Analysis and Modeling: A Deep Dive into the National Cancer Institute Method

Hanqi Luo; Kevin W Dodd; Charles D Arnold; Reina Engle-Stone

doi:10.1093/jn/nxac144

Advanced Dietary Analysis and Modeling: A Deep Dive into the National Cancer Institute Method

J Nutr. 2022 Nov;152(11):2615-2625. doi: 10.1093/jn/nxac144. Epub 2022 Jun 29.

Authors

Hanqi Luo¹, Kevin W Dodd², Charles D Arnold³, Reina Engle-Stone³

Affiliations

¹ Institute for Global Nutrition, University of California, Davis, CA, USA; Department of Nutrition, University of California, Davis, CA, USA; Hubert Department of Global Health, Rollings School of Public Health, Emory University, Atlanta, GA, USA. Electronic address: Hanqi.luo@emory.edu.
² National Cancer Institute, NIH, Bethesda, MD, USA.
³ Institute for Global Nutrition, University of California, Davis, CA, USA; Department of Nutrition, University of California, Davis, CA, USA.

Abstract

Background: The National Cancer Institute (NCI) method has been used widely by researchers to make inferences about usual dietary intake distributions of foods and nutrients based on a limited number of 24-h dietary recalls (24-HRs). Although the NCI method does not provide individual estimates of usual intake, it can be used to address many research questions, including modeling effects of nutrition interventions on population distributions of usual intake. Software for implementing the NCI method, and corresponding code examples, is publicly available in the form of SAS macros but little formal guidance exists for conducting advanced analyses.

Objectives: We aim to present advanced techniques for working with NCI macros to conduct both basic and advanced dietary analyses and modeling.

Method: We first present the 3 basic building blocks of analyses using the NCI method: 1) data set preparation, 2) application of the MIXTRAN macro to estimate parameters of the usual intake distribution, including effects of covariates, after transformation of 24-HRs to approximate normality, and 3) application of the DISTRIB macro to estimate the distribution of usual nutrient intake. Then, we illustrate how researchers can employ these building blocks to answer questions beyond typical descriptive analyses.

Results: Researchers can adapt the building blocks to: 1) account for factors such as demographic changes or nutrition interventions such as food fortification, 2) estimate the prevalence of dietary inadequacy via the full probability method, 3) incorporate nutrient intake from sources not always captured by 24-HRs, such as dietary supplements and human milk, and 4) carry out multiple subgroup analyses. This article describes the theoretical basis and operational guidance for these techniques.

Conclusion: With this article as a detailed resource, researchers can leverage the basic NCI building blocks to investigate a wide range of questions about usual dietary intake distribution.

Keywords: 24-h recalls; dietary intake; fortification; modeling; supplements.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Diet
Dietary Supplements
Energy Intake
Humans
National Cancer Institute (U.S.)
Neoplasms*
United States