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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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 |
work_keys_str_mv |
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