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Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research

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Yu, Zhaoxia, Guindani, Michele, Grieco, Steven F., Chen, Lujia, Holmes, Todd C., Xu, Xiangmin·2022·Neuron
DOI 10.1016/j.neuron.2021.10.030PMC PMC8763600
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Abstract

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

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  • For all experiments involving animals, linear mixed-effects models (LMEMs) were used to account for the nonindependence of cells nested within animals while testing for treatment or genotype effects.

    “For all experiments involving animals, linear mixed-effects models (LMEMs) were used to account for the nonindependence of cells nested within animals while testing for treatment or genotype effects (88).”

    From Control of microglial dynamics by the Arp2/3 complex and the autism- and schizophrenia-associated protein CYFIP1 (2026) · cites this paper as [88]

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Metadata

Authors
Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X.
Affiliations
University of California, Irvine (US)
Corresponding author
  • Yu, Zhaoxia
  • Xu, Xiangmin
ORCID
Venue
Neuron
Publisher
Elsevier BV
Published
2022-01
Volume
110
Issue
1
Pages
21-35
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2021.10.030
PMC
PMC8763600
OpenAlex
W3213971514
Cited by
563
References
17
Citations by year
2026: 114 · 2025: 149 · 2024: 125 · 2023: 116 · 2022: 56
FWCI
13.93
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Science Foundation (SES 1659921)
  • National Institutes of Health (R01EY028212, R01MH105427, R01NS104897, R35GM127102)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agriculture, Soil, Plant Science
Keywords
mixed models, linear mixed models, generalized mixed models, correlated data, repeated measures, clustered data, experimental design, statistical validity, reproducibility, neuroscience data analysis
MeSH terms
Analysis of Variance, Neurosciences, Research Design, Reproducibility of Results, Models, Statistical, Linear Models

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