Evidence map›Paper›PMID 34407413›Full record

ArticleCell reports2021

Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.

Irene Alfaras, Keisuke Ejima, Camila Vieira Ligo Teixeira, Clara Di Germanio, Sarah J Mitchell, Samuel Hamilton, Luigi Ferrucci, Nathan L Price, David B Allison, Michel Bernier and 1 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in Cell reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.0field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Workshop Report-Heterogeneity and Successful Aging Part I: Heterogeneity in Aging-Challenges and Opportunities.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
  4. Quantifying the Impact of Co-Housing on Murine Aging Studies.bioRxiv : the preprint server for biology · 2024
    Article
  5. Review
  6. Article
  7. Article
  8. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 2 countries.

Irene AlfarasTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Keisuke EjimaDepartment 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 TeixeiraTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Clara Di GermanioTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Sarah J MitchellTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Samuel HamiltonTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Luigi FerrucciTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Nathan L PriceTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
David B AllisonDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN 47405, USA.
Michel BernierTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA.
Rafael de CaboTranslational Gerontology Branch, National Institute on Aging Intramural Program, National Institutes of Health, Baltimore, MD 21224, USA. Electronic address: decabora@grc.nia.nih.gov.
National Institutes of Health · USNational Institute on Aging · USIndiana University Bloomington · US

Funding

Why is the prevalence of obesity so high in U.S. Southern States? Regional predictors of BMI and obesity treatment response.P30DK056336 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BARBARA A GOWER · 2000 to 2026
$31.9M
Sex hormones and arthritis in a long lived animal modelP30AG050886 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DAVID B ALLISON · 2015 to 2026
$11.3M
Aging Intervention ProgramZIAAG000363 · NIA · NATIONAL INSTITUTE ON AGING · PI DE CABO, RAFAEL · 2009 to 2025
$9.0M
Nathan Shock Centers Coordinating CenterU24AG056053 · NIA · AMERICAN FEDERATION FOR AGING RESEARCH · PI STEVEN N. AUSTAD, Stephanie Lederman · 2017 to 2026
$6.1M
Study of Longitudinal Aging in MiceZIAAG000335 · NIA · NATIONAL INSTITUTE ON AGING · PI DE CABO, RAFAEL · 2017 to 2025
$5.0M
Strengthening Causal Inference in Behavioral Obesity ResearchR25HL124208 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI ALLISON, DAVID B, BROWN, ANDREW W · 2014 to 2023
$1.8M
The Mathematical Sciences in Obesity ResearchR25DK099080 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DAVID B ALLISON, Andrew W Brown · 2013 to 2026
$1.2M
Aging Intervention ProgramZ01AG000363 · NIA · NATIONAL INSTITUTE ON AGING · PI DE CABO, RAFAEL · 2008 to 2008
$628k
Intramural NIH HHS Z01 AG000363Intramural NIH HHS ZIA AG000335NHLBI NIH HHS R25 HL124208NIA NIH HHS P30 AG050886NIA NIH HHS U24 AG056053NIDDK NIH HHS P30 DK056336NIDDK NIH HHS R25 DK099080
6 · The paper itself

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.

Indexed as

Models, StatisticalResearchAgingAnimalsBehavior, AnimalComputer SimulationMaleMiceMice, Inbred C57BLPhenotypeaging phenotypesgate speedKaplan-Meiermouse agingmouse phenotypesplasmodepower calculationrespiratory exchange ratiosurvivaltype I error

Identifiers

PMID34407413
PMCPMC8449850
OpenAlexW3194023341

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.