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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Veröffentlicht in:	PLoS computational biology 2021-11, Vol.17 (11), p.e1009585
Hauptverfasser:	Madssen, Torfinn S, Giskeødegård, Guro F, Smilde, Age K, Westerhuis, Johan A
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysis Analysis of variance Antineoplastic Agents, Immunological - therapeutic use Bariatric Surgery Bevacizumab - therapeutic use Biology and Life Sciences Breast Neoplasms - drug therapy Clinical trials Confidence intervals Data analysis Data Interpretation, Statistical Datasets Design of experiments Experimental design Female Genomics Humans Intervention Linear models (Statistics) Linear regression models Longitudinal method Longitudinal Studies Medical research Medicine and Health Sciences Metabolomics Methods Missing data Multivariate analysis Physical Sciences Principal components analysis Proteomics Reproducibility of Results Research and Analysis Methods Time measurement Transcriptomics Variables Variance analysis Within-subjects design
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