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

Zhaoxia Yu; Michele Guindani; Steven F Grieco; Lujia Chen; Todd C Holmes; Xiangmin Xu

doi:10.1016/j.neuron.2021.10.030

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

Neuron. 2022 Jan 5;110(1):21-35. doi: 10.1016/j.neuron.2021.10.030. Epub 2021 Nov 15.

Authors

Zhaoxia Yu¹, Michele Guindani², Steven F Grieco³, Lujia Chen³, Todd C Holmes⁴, Xiangmin Xu⁵

Affiliations

¹ Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA 92697-3425, USA; The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697, USA. Electronic address: zhaoxia@ics.uci.edu.
² Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA 92697-3425, USA.
³ Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA.
⁴ Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, CA 92697- 4560, USA; The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697, USA.
⁵ Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA; Department of Biomedical Engineering, University of California, Irvine, Irvine, CA 92697-2715, USA; Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA 92697-4025, USA; Department of Computer Science, University of California, Irvine, Irvine, CA 92697-3435, USA; The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697, USA. Electronic address: xiangmix@uci.edu.

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.

Keywords: Bayesian analysis; clustered data; generalized linear mixed-effects model; linear mixed-effects model; linear regression model; repeated measures.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Review

MeSH terms

Analysis of Variance
Linear Models
Models, Statistical
Neurosciences*
Reproducibility of Results
Research Design*

Abstract

Publication types

MeSH terms

Grants and funding