A review of statistical methods for dietary pattern analysis

• Published in: Nutrition journal Vol. 20; no. 1; p. 37
• Main Authors: Zhao, Junkang, Li, Zhiyao, Gao, Qian, Zhao, Haifeng, Chen, Shuting, Huang, Lun, Wang, Wenjie, Wang, Tong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.04.2021; BioMed Central; BMC
• Subjects: Cardiovascular disease; Clustering analysis; computer software; Data analysis; Data mining; Data processing; Diet; Dietary guidelines; Dietary patterns; Dietary quality scores; eating habits; Epidemiology; Evaluation; Factor analysis; Food; food quality; Health aspects; Mortality; Nutrition; Principal component analysis; Principal components analysis; Product/Service Evaluations; Questionnaires; Review; Software; Statistical analysis; Statistical methods; Statistics; Treelet transform; China; Least absolute shrinkage and selection operator; Dietary patterns; Factor analysis; Treelet transform; Dietary quality scores; Clustering analysis; Data mining; Reduced rank regression; Compositional data analysis; Principal component analysis
• ISSN: 1475-2891; 1475-2891
• DOI: 10.1186/s12937-021-00692-7

Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately. This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation. While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods' performance in terms of reproducibility, validity, and ability to predict different outcomes. Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.