Autoren:	Todorov, Hristo Searle-White, Emily Gerber, Susanne
Titel:	Applying univariate vs. multivariate statistics to investigate therapeutic efficacy in (pre)clinical trials : a Monte Carlo simulation study on the example of a controlled preclinical neurotrauma trial
Online-Publikationsdatum:	23-Jun-2020
Erscheinungsdatum:	2020
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	Background Small sample sizes combined with multiple correlated endpoints pose a major challenge in the statistical analysis of preclinical neurotrauma studies. The standard approach of applying univariate tests on individual response variables has the advantage of simplicity of interpretation, but it fails to account for the covariance/correlation in the data. In contrast, multivariate statistical techniques might more adequately capture the multi-dimensional pathophysiological pattern of neurotrauma and therefore provide increased sensitivity to detect treatment effects. Results We systematically evaluated the performance of univariate ANOVA, Welch’s ANOVA and linear mixed effects models versus the multivariate techniques, ANOVA on principal component scores and MANOVA tests by manipulating factors such as sample and effect size, normality and homogeneity of variance in computer simulations. Linear mixed effects models demonstrated the highest power when variance between groups was equal or variance ratio was 1:2. In contrast, Welch’s ANOVA outperformed the remaining methods with extreme variance heterogeneity. However, power only reached acceptable levels of 80% in the case of large simulated effect sizes and at least 20 measurements per group or moderate effects with at least 40 replicates per group. In addition, we evaluated the capacity of the ordination techniques, principal component analysis (PCA), redundancy analysis (RDA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLS-DA) to capture patterns of treatment effects without formal hypothesis testing. While LDA suffered from a high false positive rate due to multicollinearity, PCA, RDA, and PLS-DA were robust and PLS-DA outperformed PCA and RDA in capturing a true treatment effect pattern. Conclusions Multivariate tests do not provide an appreciable increase in power compared to univariate techniques to detect group differences in preclinical studies. However, PLS-DA seems to be a useful ordination technique to explore treatment effect patterns without formal hypothesis testing.
DDC-Sachgruppe:	570 Biowissenschaften 570 Life sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 10 Biologie
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-411
URN:	urn:nbn:de:hebis:77-publ-598739
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	PLOS ONE 15 3
Seitenzahl oder Artikelnummer:	e0230798
Verlag:	PLOS
Verlagsort:	San Francisco, California, US
Erscheinungsdatum:	2020
ISSN:	1932-6203
URL der Originalveröffentlichung:	http://dx.doi.org/10.1371/journal.pone.0230798
DOI der Originalveröffentlichung:	10.1371/journal.pone.0230798
Enthalten in den Sammlungen:	JGU-Publikationen