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

Subanesthetic Ketamine Reactivates Adult Cortical Plasticity to Restore Vision from Amblyopia

(2020) Steven F. Grieco et al.   CURRENT BIOLOGY

A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology

(2020) Ziqiang Wei et al.   PLoS Computational Biology

Three Recommendations for Improving the Use of p-Values

(2019) Daniel J. Benjamin et al.   AMERICAN STATISTICIAN

What exactly is ‘N’ in cell culture and animal experiments?

(2018) Stanley E. Lazic et al.   PLOS BIOLOGY

On p-Values and Bayes Factors

(2018) Leonhard Held et al.   Annual Review of Statistics and Its Application

Balancing Type I error and power in linear mixed models

(2017) Hannes Matuschek et al.   JOURNAL OF MEMORY AND LANGUAGE

Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models

(2017) Machelle D. Wilson et al.   JOURNAL OF NEUROSCIENCE METHODS

The ASA's Statement onp-Values: Context, Process, and Purpose

(2016) Ronald L. Wasserstein et al.   AMERICAN STATISTICIAN

The anova to mixed model transition

(2016) Matthieu P. Boisgontier et al.   NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

SIMR: an R package for power analysis of generalized linear mixed models by simulation

(2016) Peter Green et al.   Methods in Ecology and Evolution

A rank-sum test for clustered data when the number of subjects in a group within a cluster is informative

(2015) Sandipan Dutta et al.   BIOMETRICS

The Economics of Reproducibility in Preclinical Research

(2015) Leonard P. Freedman et al.   PLOS BIOLOGY

The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data

(2014) Ronald Margolis et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

Biomedical research: increasing value, reducing waste

(2014) Malcolm R Macleod et al.   LANCET

A solution to dependency: using multilevel analysis to accommodate nested data

(2014) Emmeke Aarts et al.   NATURE NEUROSCIENCE

Rigor or Mortis: Best Practices for Preclinical Research in Neuroscience

(2014) Oswald Steward et al.   NEURON

Rescuing US biomedical research from its systemic flaws

(2014) B. Alberts et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Random effects structure for confirmatory hypothesis testing: Keep it maximal

(2013) Dale J. Barr et al.   JOURNAL OF MEMORY AND LANGUAGE

Split Hamiltonian Monte Carlo

(2013) Babak Shahbaba et al.   STATISTICS AND COMPUTING

A call for transparent reporting to optimize the predictive value of preclinical research

(2012) Story C. Landis et al.   NATURE

Believe it or not: how much can we rely on published data on potential drug targets?

(2011) Florian Prinz et al.   NATURE REVIEWS DRUG DISCOVERY

A Study of Clustered Data and Approaches to Its Analysis

(2010) S. Galbraith et al.   JOURNAL OF NEUROSCIENCE

Bayesian inference for generalized linear mixed models

(2009) Y. Fong et al.   BIOSTATISTICS

Survey of the Quality of Experimental Design, Statistical Analysis and Reporting of Research Using Animals

(2009) Carol Kilkenny et al.   PLoS One

Generalized linear mixed models: a practical guide for ecology and evolution

(2009) Benjamin M. Bolker et al.   TRENDS IN ECOLOGY & EVOLUTION

Mixed-effects modeling with crossed random effects for subjects and items

(2008) R.H. Baayen et al.   JOURNAL OF MEMORY AND LANGUAGE