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Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 35, no 20, 3623-3644 p.Article in journal (Refereed) Published
Abstract [en]

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised.

Place, publisher, year, edition, pages
2016. Vol. 35, no 20, 3623-3644 p.
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-283203DOI: 10.1002/sim.6945ISI: 000380728800013PubMedID: 26999657OAI: oai:DiVA.org:uu-283203DiVA: diva2:918773
Swedish National Infrastructure for Computing (SNIC), SNIC 2014-1-340
Available from: 2016-04-11 Created: 2016-04-11 Last updated: 2016-09-14Bibliographically approved

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Thulin, Måns
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