Evidence map›Paper›PMID 33874970›Full record

ReviewNutrition journal2021

A review of statistical methods for dietary pattern analysis.

Junkang Zhao, Zhiyao Li, Qian Gao, Haifeng Zhao, Shuting Chen, Lun Huang, Wenjie Wang, Tong Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nutrition journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 189 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
189citing papers in PubMed, 10 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

189 citing papers in PubMed, 10 syntheses or guidelines pooled it.

  1. Empirically derived dietary patterns and prostate cancer risk: a systematic review and meta-analysis of prospective cohorts.European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) · 2026
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129 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Junkang ZhaoDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Zhiyao LiDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Qian GaoDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Haifeng ZhaoDepartment of Nutrition & Food Hygiene, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Shuting ChenDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Lun HuangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Wenjie WangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China.
Tong WangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan, 030001, Shanxi province, China. tongwang@sxmu.edu.cn.ORCID 0000-0002-9403-7167

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDietary 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.

methodsThis 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.

resultsWhile 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.

conclusionSelection 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.

Indexed as

DietFeeding BehaviorFactor Analysis, StatisticalHumansPrincipal Component AnalysisReproducibility of ResultsClustering analysisCompositional data analysisData miningDietary patternsDietary quality scoresFactor analysisLeast absolute shrinkage and selection operatorPrincipal component analysisReduced rank regressionTreelet transform

Identifiers

PMID33874970
PMCPMC8056502

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.