MIXED

Approach of linear mixed model in longitudinal data analysis using SAS. Linear mixed models is one of the best methodologies for the analysis of the longitudinal (repeated measures) data. One major advantage of this methodology is that it accommodates the complexities of typical longitudinal data sets. The analysis of linear mixed model methodology for the analysis of repeated measurements is becoming increasingly common due to the development of widely available software. This paper reviews and summarizes the methodology of the linear mixed model approach for the analysis of repeated measurements data using the SAS Software. PROC MIXED in SAS provides a very flexible environment in which models can be many types of repeated measures data. It can be repeated in time, space or both. Correlation among measurements made on the same subject or experimental units can be modeled using random effects and through the specification of a covariance structure. PROC MIXED provides a useful covariance structures or modeling both in time and space, including discrete and continuous increments of time and space.


References in zbMATH (referenced in 57 articles , 1 standard article )

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  1. Cao, Jiguo; Wang, Liangliang; Huang, Zhongwen; Gai, Junyi; Wu, Rongling: Functional mapping of multiple dynamic traits (2017)
  2. LaMotte, Lynn R.; Wells, Jeffrey D.: Inverse prediction for heteroscedastic response using mixed models software (2017)
  3. Patterson, Scott; Jones, Byron: Bioequivalence and statistics in clinical pharmacology. (2017)
  4. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  5. Bruyndonckx, Robin; Aerts, Marc; Hens, Niel: Simulation-based evaluation of the performance of the $F$ test in a linear multilevel model setting with sparseness at the level of the primary unit (2016)
  6. Zhang, Xinyu; Liang, Hua; Liu, Anna; Ruppert, David; Zou, Guohua: Selection strategy for covariance structure of random effects in linear mixed-effects models (2016)
  7. Luo, Jingqin; D’Angela, Gina; Gao, Feng; Ding, Jimin; Xiong, Chengjie: Bivariate correlation coefficients in family-type clustered studies (2015)
  8. Zhang, Peng; Luo, Dandan; Li, Pengfei; Sharpsten, Lucie; Medeiros, Felipe A.: Log-gamma linear-mixed effects models for multiple outcomes with application to a longitudinal glaucoma study (2015)
  9. Chen, Yan-liang; Tian, Mao-zai; Yu, Ke-ming; Pan, Jian-xin: Composite hierachical linear quantile regression (2014)
  10. Das, Kiranmoy; Daniels, Michael J.: A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data (2014)
  11. Chen, Xinjie; Zou, Guohua; Zhang, Xinyu: Frequentist model averaging for linear mixed-effects models (2013)
  12. Wang, Wei: Identifiability of linear mixed effects models (2013)
  13. Brien, C. J.; Bailey, R. A.; Tran, T. T.; Boland, J.: Quasi-Latin designs (2012)
  14. Gaugler, Trent; Akritas, Michael G.: Testing for interaction in two-way random and mixed effects models: the fully nonparametric approach (2011)
  15. Han, Jun: Variance least squares estimators for multivariate linear mixed model (2011)
  16. Little, Roderick: Calibrated Bayes, for statistics in general, and missing data in particular (2011)
  17. Tiwari, Pankaj; Shukla, Gaurav: Approach of linear mixed model in longitudinal data analysis using SAS (2011)
  18. Wang, Xueqin; Guo, Xiaobo; He, Mingguang; Zhang, Heping: Statistical inference in mixed models and analysis of twin and family data (2011)
  19. Livacic-Rojas, Pablo; Vallejo, Guillermo; Fernández, Paula: Analysis of type I error rates of univariate and multivariate procedures in repeated measures designs (2010)
  20. Perrett, Jamis J.: A SAS/IML companion for linear models. (2010)

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