{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:52:33Z","timestamp":1781369553150,"version":"3.54.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009585","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000}}],"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Norwegian Research School in Bioinformatics, Biostatistics and Systems Biology"},{"DOI":"10.13039\/100008730","name":"Kreftforeningen","doi-asserted-by":"publisher","award":["6834362"],"award-info":[{"award-number":["6834362"]}],"id":[{"id":"10.13039\/100008730","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of \u201comics\u201d-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009585","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T14:01:32Z","timestamp":1636466492000},"page":"e1009585","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":46,"title":["Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6150-8663","authenticated-orcid":true,"given":"Torfinn S.","family":"Madssen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-8824","authenticated-orcid":true,"given":"Guro F.","family":"Giske\u00f8deg\u00e5rd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3052-4644","authenticated-orcid":true,"given":"Age K.","family":"Smilde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6747-9779","authenticated-orcid":true,"given":"Johan A.","family":"Westerhuis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"issue":"5","key":"pcbi.1009585.ref001","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1093\/bib\/bbr071","article-title":"Generic framework for high-dimensional fixed-effects ANOVA","volume":"13","author":"AK Smilde","year":"2012","journal-title":"Brief Bioinform"},{"issue":"13","key":"pcbi.1009585.ref002","doi-asserted-by":"crossref","first-page":"3043","DOI":"10.1093\/bioinformatics\/bti476","article-title":"ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.","volume":"21","author":"AK Smilde","year":"2005","journal-title":"Bioinformatics"},{"issue":"6","key":"pcbi.1009585.ref003","doi-asserted-by":"crossref","first-page":"e2895","DOI":"10.1002\/cem.2895","article-title":"ASCA+ and APCA+: Extensions of ASCA and APCA in the analysis of unbalanced multifactorial designs","volume":"31","author":"M Thiel","year":"2017","journal-title":"Journal of Chemometrics"},{"issue":"6","key":"pcbi.1009585.ref004","doi-asserted-by":"crossref","first-page":"e3232","DOI":"10.1002\/cem.3232","article-title":"LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data","volume":"34","author":"M Martin","year":"2020","journal-title":"Journal of Chemometrics"},{"issue":"4","key":"pcbi.1009585.ref005","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s11306-009-0165-3","article-title":"Systems toxicology study of doxorubicin on rats using ultra performance liquid chromatography coupled with mass spectrometry based metabolomics","volume":"5","author":"J Wang","year":"2009","journal-title":"Metabolomics"},{"issue":"4","key":"pcbi.1009585.ref006","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1001\/jama.2015.19394","article-title":"Analyzing Repeated Measurements Using Mixed Models","volume":"315","author":"MA Detry","year":"2016","journal-title":"JAMA"},{"issue":"2","key":"pcbi.1009585.ref007","doi-asserted-by":"crossref","first-page":"20160021","DOI":"10.1515\/jci-2016-0021","article-title":"Lord\u2019s Paradox Revisited\u2013(Oh Lord! 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