A review of statistical methods for dietary pattern analysis

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...

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Main Authors: Junkang Zhao, Zhiyao Li, Qian Gao, Haifeng Zhao, Shuting Chen, Lun Huang, Wenjie Wang, Tong Wang
Format: Article
Language:English
Published: BMC 2021-04-01
Series:Nutrition Journal
Subjects:
Online Access:https://doi.org/10.1186/s12937-021-00692-7
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spelling doaj-b3b6455b4b1143a09b9ef3e5138b92672021-04-25T11:26:12ZengBMCNutrition Journal1475-28912021-04-0120111810.1186/s12937-021-00692-7A review of statistical methods for dietary pattern analysisJunkang Zhao0Zhiyao Li1Qian Gao2Haifeng Zhao3Shuting Chen4Lun Huang5Wenjie Wang6Tong Wang7Department of Health Statistics, School of Public Health, Shanxi Medical UniversityDepartment of Health Statistics, School of Public Health, Shanxi Medical UniversityDepartment of Health Statistics, School of Public Health, Shanxi Medical UniversityDepartment of Nutrition & Food Hygiene, School of Public Health, Shanxi Medical UniversityDepartment of Health Statistics, School of Public Health, Shanxi Medical UniversityDepartment of Health Statistics, School of Public Health, Shanxi Medical UniversityDepartment of Health Statistics, School of Public Health, Shanxi Medical UniversityDepartment of Health Statistics, School of Public Health, Shanxi Medical UniversityAbstract 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.https://doi.org/10.1186/s12937-021-00692-7Dietary patternsDietary quality scoresPrincipal component analysisFactor analysisClustering analysisTreelet transform
collection DOAJ
language English
format Article
sources DOAJ
author Junkang Zhao
Zhiyao Li
Qian Gao
Haifeng Zhao
Shuting Chen
Lun Huang
Wenjie Wang
Tong Wang
spellingShingle Junkang Zhao
Zhiyao Li
Qian Gao
Haifeng Zhao
Shuting Chen
Lun Huang
Wenjie Wang
Tong Wang
A review of statistical methods for dietary pattern analysis
Nutrition Journal
Dietary patterns
Dietary quality scores
Principal component analysis
Factor analysis
Clustering analysis
Treelet transform
author_facet Junkang Zhao
Zhiyao Li
Qian Gao
Haifeng Zhao
Shuting Chen
Lun Huang
Wenjie Wang
Tong Wang
author_sort Junkang Zhao
title A review of statistical methods for dietary pattern analysis
title_short A review of statistical methods for dietary pattern analysis
title_full A review of statistical methods for dietary pattern analysis
title_fullStr A review of statistical methods for dietary pattern analysis
title_full_unstemmed A review of statistical methods for dietary pattern analysis
title_sort review of statistical methods for dietary pattern analysis
publisher BMC
series Nutrition Journal
issn 1475-2891
publishDate 2021-04-01
description 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.
topic Dietary patterns
Dietary quality scores
Principal component analysis
Factor analysis
Clustering analysis
Treelet transform
url https://doi.org/10.1186/s12937-021-00692-7
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