Evidence map›Paper›PMID 35146334›Full record

ReviewGeneral psychiatry2022

Partial least squares regression and principal component analysis: similarity and differences between two popular variable reduction approaches.

Chenyu Liu, Xinlian Zhang, Tanya T Nguyen, Jinyuan Liu, Tsungchin Wu, Ellen Lee, Xin M Tu

Abstract readReview
In one paragraph

Review in General psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–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

26 citing papers in PubMed.

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  7. Unveiling the coupling effect of spNature communications · 2025
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  13. Predictive modeling of ARDS mortality integrating biomarker/cytokine, clinical and metabolomic data.Translational research : the journal of laboratory and clinical medicine · 2025
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  14. The Ratio of spAngewandte Chemie (International ed. in English) · 2025
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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

7 authors.

Chenyu LiuDivision of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, California, USA.
Xinlian ZhangDivision of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, California, USA.
Tanya T NguyenDepartment of Psychiatry, Stein Institute for Research on Aging, UC San Diego, La Jolla, California, USA.
Jinyuan LiuDivision of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, California, USA.
Tsungchin WuDivision of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, California, USA.
Ellen LeeDepartment of Psychiatry, Stein Institute for Research on Aging, UC San Diego, La Jolla, California, USA.
Xin M TuDivision of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, California, USA.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
Inflammatory Mechanisms Linking Sleep Disturbances and Cognitive Deficits in Schizophrenia in Later LifeK23MH119375 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEE, ELLEN EUN-OK · 2019 to 2023
$1.0M
Characterizing the gut microbiome in schizophrenia: Relationship to age, cognition, & disease-related factorsK23MH118435 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NGUYEN, TANYA T · 2019 to 2020
$375k
NCATS NIH HHS UL1 TR001442
6 · The paper itself

Abstract

In many statistical applications, composite variables are constructed to reduce the number of variables and improve the performances of statistical analyses of these variables, especially when some of the variables are highly correlated. Principal component analysis (PCA) and factor analysis (FA) are generally used for such purposes. If the variables are used as explanatory or independent variables in linear regression analysis, partial least squares (PLS) regression is a better alternative. Unlike PCA and FA, PLS creates composite variables by also taking into account the response, or dependent variable, so that they have higher correlations with the response than composites from their PCA and FA counterparts. In this report, we provide an introduction to this useful approach and illustrate it with data from a real study.

Indexed as

biostatisticslinear modelsstatistics as topic

Identifiers

PMID35146334
PMCPMC8796256

What OpenQuestion holds

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LicenceCC BY-NC
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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.