Valid statistical approaches for clustered data: A Monte Carlo simulation study
暂无分享,去创建一个
Dexin Shi | Kristen A. McLaurin | Amanda J. Fairchild | Rosemarie M. Booze | Charles F. Mactutus | Dexin Shi | C. Mactutus | R. Booze | K. McLaurin
[1] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[2] D. Bates,et al. Linear and Nonlinear Mixed Effects Models [R package nlme version 3.1-149] , 2020 .
[3] Eva Forssell-Aronsson,et al. Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens , 2020, Scientific Reports.
[4] W Holmes Finch,et al. Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters , 2019, Educational and psychological measurement.
[5] Timo B. Roettger. Researcher degrees of freedom in phonetic research , 2019, Laboratory Phonology: Journal of the Association for Laboratory Phonology.
[6] Brian A. Nosek,et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015 , 2018, Nature Human Behaviour.
[7] Bernhard Voelkl,et al. Reproducibility of preclinical animal research improves with heterogeneity of study samples , 2018, PLoS biology.
[8] Donald R. Williams,et al. Between-litter variation in developmental studies of hormones and behavior: Inflated false positives and diminished power , 2017, Frontiers in Neuroendocrinology.
[9] Stanley E Lazic,et al. What exactly is ‘N’ in cell culture and animal experiments? , 2017, bioRxiv.
[10] Camilla L. Nord,et al. Power-up: A Reanalysis of 'Power Failure' in Neuroscience Using Mixture Modeling , 2017, The Journal of Neuroscience.
[11] Machelle D. Wilson,et al. Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models , 2017, Journal of Neuroscience Methods.
[12] Andrew Gelman,et al. Measurement error and the replication crisis , 2017, Science.
[13] Laura M. Stapleton,et al. Modeling Clustered Data with Very Few Clusters , 2016, Multivariate behavioral research.
[14] V. Garovic,et al. Reinventing Biostatistics Education for Basic Scientists , 2016, PLoS biology.
[15] U. Dirnagl,et al. Where Have All the Rodents Gone? The Effects of Attrition in Experimental Research on Cancer and Stroke , 2016, PLoS biology.
[16] Matthijs Verhage,et al. Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives , 2015, BMC Neuroscience.
[17] O. Steward,et al. Rigor or Mortis: Best Practices for Preclinical Research in Neuroscience , 2014, Neuron.
[18] Matthijs Verhage,et al. A solution to dependency: using multilevel analysis to accommodate nested data , 2014, Nature Neuroscience.
[19] Katie Lidster,et al. Two Years Later: Journals Are Not Yet Enforcing the ARRIVE Guidelines on Reporting Standards for Pre-Clinical Animal Studies , 2014, PLoS biology.
[20] Daniel M McNeish,et al. Modeling sparsely clustered data: design-based, model-based, and single-level methods. , 2014, Psychological methods.
[21] Jaykaran Charan,et al. How to calculate sample size in animal studies? , 2013, Journal of pharmacology & pharmacotherapeutics.
[22] K. Slocombe,et al. Pseudoreplication: a widespread problem in primate communication research , 2013, Animal Behaviour.
[23] J. Ioannidis,et al. Evaluation of Excess Significance Bias in Animal Studies of Neurological Diseases , 2013, PLoS biology.
[24] Brian A. Nosek,et al. Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.
[25] David L. Vaux,et al. Research methods: Know when your numbers are significant , 2012, Nature.
[26] Stanley E Lazic,et al. Improving basic and translational science by accounting for litter-to-litter variation in animal models , 2013, BMC Neuroscience.
[27] C. Begley,et al. Drug development: Raise standards for preclinical cancer research , 2012, Nature.
[28] F. Prinz,et al. Believe it or not: how much can we rely on published data on potential drug targets? , 2011, Nature Reviews Drug Discovery.
[29] Armelle Nugier,et al. Data with Hierarchical Structure: Impact of Intraclass Correlation and Sample Size on Type-I Error , 2011, Front. Psychology.
[30] William N. Venables,et al. Modern Applied Statistics with S , 2010 .
[31] Hester F. Lingsma,et al. Covariate adjustment increases statistical power in randomized controlled trials. , 2010, Journal of clinical epidemiology.
[32] Sally Galbraith,et al. A Study of Clustered Data and Approaches to Its Analysis , 2010, The Journal of Neuroscience.
[33] D. Howells,et al. Publication Bias in Reports of Animal Stroke Studies Leads to Major Overstatement of Efficacy , 2010, PLoS biology.
[34] Charles A. Scherbaum,et al. Estimating Statistical Power and Required Sample Sizes for Organizational Research Using Multilevel Modeling , 2009 .
[35] Kevin Arceneaux,et al. Modeling Certainty with Clustered Data: A Comparison of Methods , 2009, Political Analysis.
[36] J. Ioannidis,et al. Why Current Publication Practices May Distort Science , 2008, PLoS medicine.
[37] P. Clarke,et al. When can group level clustering be ignored? Multilevel models versus single-level models with sparse data , 2008, Journal of Epidemiology & Community Health.
[38] Stephen G West,et al. Doctoral training in statistics, measurement, and methodology in psychology: replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America. , 2008, The American psychologist.
[39] Karl P. Pfeiffer,et al. The Use of Statistics in Medical Research , 2007 .
[40] Gregory R. Hancock,et al. Structural equation modeling : a second course , 2006 .
[41] R. Fisher. Statistical methods for research workers , 1927, Protoplasma.
[42] Ewout W Steyerberg,et al. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. , 2004, Journal of clinical epidemiology.
[43] Walter Krämer,et al. Review of Modern applied statistics with S, 4th ed. by W.N. Venables and B.D. Ripley. Springer-Verlag 2002 , 2003 .
[44] B. Muthén,et al. How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power , 2002 .
[45] W. Pan,et al. Small‐sample adjustments in using the sandwich variance estimator in generalized estimating equations , 2002, Statistics in medicine.
[46] M. Fay,et al. Small‐Sample Adjustments for Wald‐Type Tests Using Sandwich Estimators , 2001, Biometrics.
[47] J. Crabbe,et al. Genetics of mouse behavior: interactions with laboratory environment. , 1999, Science.
[48] S. Raudenbush. Statistical analysis and optimal design for cluster randomized trials , 1997 .
[49] Z. Feng,et al. A comparison of statistical methods for clustered data analysis with Gaussian error. , 1996, Statistics in medicine.
[50] J. Kromrey,et al. Detecting Unit of Analysis Problems in Nested Designs: Statistical Power and Type I Error Rates of the F Test for Groups-within-Treatments Effects , 1996 .
[51] V. M. Chinchilli,et al. Small sample characteristics of generalized estimating equations , 1995 .
[52] B. Pearce,et al. Principles and pitfalls in the analysis of prenatal treatment effects in multiparous species. , 1992, Neurotoxicology and teratology.
[53] Anthony S. Bryk,et al. Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .
[54] N. Jewell,et al. Covariate adjustment. , 1991, Biometrics.
[55] Bengt Muthén,et al. On structural equation modeling with data that are not missing completely at random , 1987 .
[56] C. Álvarez-Dardet,et al. [The use of statistics in medical publications: an international comparison]. , 1986, Medicina clinica.
[57] S. Zeger,et al. Longitudinal data analysis using generalized linear models , 1986 .
[58] K Y Liang,et al. Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.
[59] Victor Denenberg,et al. Statistics and experimental design for behavioral and biological researchers , 1976 .
[60] M. D. Hogan,et al. Selection of the experimental unit in teratology studies. , 1975, Teratology.
[61] J. Haseman,et al. Selection of appropriate experimental units in teratology , 1974 .
[62] Jacob Cohen,et al. The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability , 1973 .
[63] C. S. Weil. Selection of the valid number of sampling units and a consideration of their combination in toxicological studies involving reproduction, teratogenesis or carcinogenesis. , 1970, Food and cosmetics toxicology.
[64] Seymour Geisser,et al. Statistical Principles in Experimental Design , 1963 .
[65] D. Sholl,et al. Pattern Discrimination and the Visual Cortex , 1953, Nature.
[66] D. Sholl. Dendritic organization in the neurons of the visual and motor cortices of the cat. , 1953, Journal of anatomy.