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Alfaras I, Ejima K, Vieira Ligo Teixeira C, Di Germanio C, Mitchell SJ, Hamilton S, Ferrucci L, Price NL, Allison DB, Bernier M, de Cabo R. Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data. Cell Rep 2021; 36:109560. [PMID: 34407413 PMCID: PMC8449850 DOI: 10.1016/j.celrep.2021.109560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/30/2021] [Accepted: 07/27/2021] [Indexed: 01/14/2023] Open
Abstract
We assess the degree of phenotypic variation in a cohort of 24-month-old male C57BL/6 mice. Because murine studies often use small sample sizes, if the commonly relied upon assumption of a normal distribution of residuals is not met, it may inflate type I error rates. In this study, 3-20 mice are resampled from the empirical distributions of 376 mice to create plasmodes, an approach for computing type I error rates and power for commonly used statistical tests without assuming a normal distribution of residuals. While all of the phenotypic and metabolic variables studied show considerable variability, the number of animals required to achieve adequate power is markedly different depending on the statistical test being performed. Overall, this work provides an analysis with which researchers can make informed decisions about the sample size required to achieve statistical power from specific measurements without a priori assumptions of a theoretical distribution.
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Affiliation(s)
- Irene Alfaras
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Keisuke Ejima
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN 47405, USA; Graduate School of Medicine, The University of Tokyo, Tokyo 1130033, Japan
| | - Camila Vieira Ligo Teixeira
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Clara Di Germanio
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Sarah J Mitchell
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Samuel Hamilton
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Nathan L Price
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - David B Allison
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN 47405, USA
| | - Michel Bernier
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Rafael de Cabo
- Translational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
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