Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research
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 model...
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| Published in | Neuron (Cambridge, Mass.) Vol. 110; no. 1; pp. 21 - 35 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
05.01.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0896-6273 1097-4199 1097-4199 |
| DOI | 10.1016/j.neuron.2021.10.030 |
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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.
In this Primer, Yu et al. introduce linear and generalized mixed-effects models for improved statistical analysis in neuroscience research and provide clear instruction on how to recognize when they are needed and how to apply them. |
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| AbstractList | 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. 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.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. 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. In this Primer, Yu et al. introduce linear and generalized mixed-effects models for improved statistical analysis in neuroscience research and provide clear instruction on how to recognize when they are needed and how to apply them. |
| Author | Holmes, Todd C. Guindani, Michele Chen, Lujia Yu, Zhaoxia Xu, Xiangmin Grieco, Steven F. |
| AuthorAffiliation | 1. Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425 2. Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275 4. Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715 7. The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697 6. Department of Computer Science, University of California, Irvine, Irvine, CA 92697-3435 5. Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA 92697-4025 3. Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, CA 92697-4560 |
| AuthorAffiliation_xml | – name: 1. Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425 – name: 4. Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715 – name: 3. Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, CA 92697-4560 – name: 7. The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697 – name: 5. Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA 92697-4025 – name: 2. Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275 – name: 6. Department of Computer Science, University of California, Irvine, Irvine, CA 92697-3435 |
| Author_xml | – sequence: 1 givenname: Zhaoxia surname: Yu fullname: Yu, Zhaoxia email: zhaoxia@ics.uci.edu organization: Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA 92697-3425, USA – sequence: 2 givenname: Michele surname: Guindani fullname: Guindani, Michele organization: Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA 92697-3425, USA – sequence: 3 givenname: Steven F. surname: Grieco fullname: Grieco, Steven F. organization: Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA – sequence: 4 givenname: Lujia surname: Chen fullname: Chen, Lujia organization: Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA – sequence: 5 givenname: Todd C. orcidid: 0000-0001-8152-8832 surname: Holmes fullname: Holmes, Todd C. organization: Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, CA 92697- 4560, USA – sequence: 6 givenname: Xiangmin orcidid: 0000-0002-5828-1533 surname: Xu fullname: Xu, Xiangmin email: xiangmix@uci.edu organization: Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34784504$$D View this record in MEDLINE/PubMed |
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| Issue | 1 |
| Keywords | clustered data generalized linear mixed-effects model linear mixed-effects model linear regression model Bayesian analysis repeated measures |
| Language | English |
| License | Copyright © 2021 Elsevier Inc. All rights reserved. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Z.Y., S.F.G., M.G., L.C., T.C.H. and X.X. prepared the figures and wrote the manuscript. X.X. conceived and oversaw this work. Author contributions |
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| SubjectTerms | Analysis of Variance Bayesian analysis clustered data generalized linear mixed-effects model linear mixed-effects model Linear Models linear regression model Models, Statistical Neurosciences repeated measures Reproducibility of Results Research Design |
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| Title | Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research |
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