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

• Published in: PLoS computational biology Vol. 17; no. 11; p. e1009585
• Main Authors: Madssen, Torfinn S., Giskeødegård, Guro F., Smilde, Age K., Westerhuis, Johan A.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.11.2021; Public Library of Science (PLoS)
• Subjects: 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; Netherlands
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009585

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.