Small sample sizes: A big data problem in high-dimensional data analysis

In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-...

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Bibliographic Details
Published inStatistical methods in medical research Vol. 30; no. 3; pp. 687 - 701
Main Authors Konietschke, Frank, Schwab, Karima, Pauly, Markus
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.03.2021
Subjects
Multiple contrast tests
max t-test
simultaneous confidence intervals
resampling
repeated measures
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Summary:In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-type statistics (multiple contrast tests) in high-dimensional designs (repeated measures or multivariate) with small sample sizes. A randomization-based approach is developed to approximate the distribution of the maximum statistic. Extensive simulation studies confirm that the new method is particularly suitable for analyzing data sets with small sample sizes. A real data set illustrates the application of the methods.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280220970228