Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.

Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of "omics"-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate o...

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Autores principales:	Torfinn S Madssen, Guro F Giskeødegård, Age K Smilde, Johan A Westerhuis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d067c3da0fa84535a46f2c72fd2319c1
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spelling	oai:doaj.org-article:d067c3da0fa84535a46f2c72fd2319c12021-12-02T19:57:57ZRepeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.1553-734X1553-735810.1371/journal.pcbi.1009585https://doaj.org/article/d067c3da0fa84535a46f2c72fd2319c12021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009585https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of "omics"-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.Torfinn S MadssenGuro F GiskeødegårdAge K SmildeJohan A WesterhuisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11, p e1009585 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Torfinn S Madssen Guro F Giskeødegård Age K Smilde Johan A Westerhuis Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.
description	Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of "omics"-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.
format	article
author	Torfinn S Madssen Guro F Giskeødegård Age K Smilde Johan A Westerhuis
author_facet	Torfinn S Madssen Guro F Giskeødegård Age K Smilde Johan A Westerhuis
author_sort	Torfinn S Madssen
title	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.
title_short	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.
title_full	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.
title_fullStr	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.
title_full_unstemmed	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.
title_sort	repeated measures asca+ for analysis of longitudinal intervention studies with multivariate outcome data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d067c3da0fa84535a46f2c72fd2319c1
work_keys_str_mv	AT torfinnsmadssen repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata AT gurofgiskeødegard repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata AT ageksmilde repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata AT johanawesterhuis repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata
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Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data.

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