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 43 articles , 1 standard article )

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  1. Chen, Yan-liang; Tian, Mao-zai; Yu, Ke-ming; Pan, Jian-xin: Composite hierachical linear quantile regression (2014)
  2. Chen, Xinjie; Zou, Guohua; Zhang, Xinyu: Frequentist model averaging for linear mixed-effects models (2013)
  3. Wang, Wei: Identifiability of linear mixed effects models (2013)
  4. Brien, C.J.; Bailey, R.A.; Tran, T.T.; Boland, J.: Quasi-Latin designs (2012)
  5. Gaugler, Trent; Akritas, Michael G.: Testing for interaction in two-way random and mixed effects models: the fully nonparametric approach (2011)
  6. Han, Jun: Variance least squares estimators for multivariate linear mixed model (2011)
  7. Little, Roderick: Calibrated Bayes, for statistics in general, and missing data in particular (2011)
  8. Tiwari, Pankaj; Shukla, Gaurav: Approach of linear mixed model in longitudinal data analysis using SAS (2011)
  9. Wang, Xueqin; Guo, Xiaobo; He, Mingguang; Zhang, Heping: Statistical inference in mixed models and analysis of twin and family data (2011)
  10. Livacic-Rojas, Pablo; Vallejo, Guillermo; Fernández, Paula: Analysis of type I error rates of univariate and multivariate procedures in repeated measures designs (2010)
  11. Perrett, Jamis J.: A SAS/IML companion for linear models. (2010)
  12. Vallejo, G.; Fernández, M.P.; Livacic-Rojas, P.E.: Analysis of unbalanced factorial designs with heteroscedastic data (2010)
  13. Feng, Rui; Zhou, Gongfu; Zhang, Meizhuo; Zhang, Heping: Analysis of twin data using SAS (2009)
  14. Tian, Maozai; Tang, Manlai; Chan, Pingshing: Semiparametric quantile modelling of hierarchical data (2009)
  15. Wang, Jun; Schaalje, G.Bruce: Model selection for linear mixed models using predictive criteria (2009)
  16. Taskinen, Antti; Sirviö, Hannu; Bruen, Michael: Modelling effects of spatial variability of saturated hydraulic conductivity on autocorrelated overland flow data: Linear mixed model approach (2008)
  17. Volaufová, Júlia; LaMotte, Lynn R.: Comparison of approximate tests of fixed effects in linear repeated measures design models with covariates (2008)
  18. Al-Marshadi, Ali Hussein: The new TSME method of covariance estimator for repeated measures crossover design (2007)
  19. LaMotte, Lynn Roy: A direct derivation of the REML likelihood function (2007)
  20. Sithole, Jabu S.; Jones, Peter W.: Bivariate longitudinal model for detecting prescribing change in two drugs simultaneously with correlated errors (2007)

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