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

Stat Methods Med Res

Department of Statistics, TU Dortmund University, Dortmund, Germany.

Published: March 2021

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 -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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8008424PMC
http://dx.doi.org/10.1177/0962280220970228DOI Listing

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