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A review of statistical methods for dietary pattern analysis
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 analysi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056502/ https://www.ncbi.nlm.nih.gov/pubmed/33874970 http://dx.doi.org/10.1186/s12937-021-00692-7 |
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author | Zhao, Junkang Li, Zhiyao Gao, Qian Zhao, Haifeng Chen, Shuting Huang, Lun Wang, Wenjie Wang, Tong |
author_facet | Zhao, Junkang Li, Zhiyao Gao, Qian Zhao, Haifeng Chen, Shuting Huang, Lun Wang, Wenjie Wang, Tong |
author_sort | Zhao, Junkang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8056502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80565022021-04-20 A review of statistical methods for dietary pattern analysis Zhao, Junkang Li, Zhiyao Gao, Qian Zhao, Haifeng Chen, Shuting Huang, Lun Wang, Wenjie Wang, Tong Nutr J Review 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. BioMed Central 2021-04-19 /pmc/articles/PMC8056502/ /pubmed/33874970 http://dx.doi.org/10.1186/s12937-021-00692-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Zhao, Junkang Li, Zhiyao Gao, Qian Zhao, Haifeng Chen, Shuting Huang, Lun Wang, Wenjie Wang, Tong A review of statistical methods for dietary pattern analysis |
title | 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_short | A review of statistical methods for dietary pattern analysis |
title_sort | review of statistical methods for dietary pattern analysis |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056502/ https://www.ncbi.nlm.nih.gov/pubmed/33874970 http://dx.doi.org/10.1186/s12937-021-00692-7 |
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