A review of statistical methods for dietary pattern analysis

Nutrition Journal

Junkang Zhao¹  Lun Huang¹  Wenjie Wang¹  Qian Gao¹  Zhiyao Li¹  Shuting Chen¹  Tong Wang¹  Haifeng Zhao² 

[1] Department of Health Statistics, School of Public Health, Shanxi Medical University;Department of Nutrition & Food Hygiene, School of Public Health, Shanxi Medical University;

关键词: Dietary patterns;    Dietary quality scores;    Principal component analysis;    Factor analysis;    Clustering analysis;    Treelet transform;   

DOI  :  10.1186/s12937-021-00692-7

来源: DOAJ

【 摘 要 】

Abstract Background 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. Methods 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. Results 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. Conclusion 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.

【 授权许可】

Unknown