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Nat Neurosci. 2014 Apr;17(4):491-6. doi: 10.1038/nn.3648. Epub 2014 Mar 26.

A solution to dependency: using multilevel analysis to accommodate nested data.

Author information

1
Section Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands.
2
1] Section Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands. [2] Section Functional Genomics, Department Clinical Genetics, VU Medical Center, Amsterdam, The Netherlands.
3
Center for Neuroscience, Swammerdam Institute for Life Sciences, Science Park, University of Amsterdam, Amsterdam, The Netherlands.
4
Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.
5
1] Section Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands. [2] Section Complex Trait Genetics, Department of Clinical Genetics, VU Medical Center, Amsterdam, The Netherlands.

Abstract

In neuroscience, experimental designs in which multiple observations are collected from a single research object (for example, multiple neurons from one animal) are common: 53% of 314 reviewed papers from five renowned journals included this type of data. These so-called 'nested designs' yield data that cannot be considered to be independent, and so violate the independency assumption of conventional statistical methods such as the t test. Ignoring this dependency results in a probability of incorrectly concluding that an effect is statistically significant that is far higher (up to 80%) than the nominal α level (usually set at 5%). We discuss the factors affecting the type I error rate and the statistical power in nested data, methods that accommodate dependency between observations and ways to determine the optimal study design when data are nested. Notably, optimization of experimental designs nearly always concerns collection of more truly independent observations, rather than more observations from one research object.

PMID:
24671065
DOI:
10.1038/nn.3648
[Indexed for MEDLINE]

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