Statistical analysis of repeated measures data using SAS procedures

Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect me...

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Bibliographic Details
Published in:Journal of animal science 1998-04, Vol.76 (4), p.1216-1231
Main Authors: Littell, R.C. (University of Florida, Gainesville.), Henry, P.R, Ammerman, C.B
Format: Article
Language:English
Subjects:
AGNEAU
ANALYSIS OF COVARIANCE
ANALYSIS OF VARIANCE
Animal Nutritional Physiological Phenomena
Animal productions
Animals
APLICACIONES DEL ORDENADOR
APPLICATION DES ORDINATEURS
Biological and medical sciences
Cattle
Classical genetics, quantitative genetics, hybrids
COMPUTER APPLICATIONS
Computer Simulation
COMPUTER SOFTWARE
CORDERO
DIET
DIETA
DIRECT SIRE COMPARISONS
EVALUACION DE SEMENTALES
EVALUATION GENETIQUE DES PERES
EXCRECION
EXCRETION
Fundamental and applied biological sciences. Psychology
Genetics of eukaryotes. Biological and molecular evolution
LAMBS
Least-Squares Analysis
LOGICIEL
MAGNESIO
MAGNESIUM
METHODE STATISTIQUE
METODOS ESTADISTICOS
MINERAL NUTRIENTS
Models, Statistical
MULTIVARIATE ANALYSIS
NUTRIENTES MINERALES
PROGRAMAS DE ORDENADOR
REGIME ALIMENTAIRE
SIRE EVALUATION
Software
Statistical analysis
STATISTICAL METHODS
SUBSTANCE NUTRITIVE MINERALE
Terrestrial animal productions
Time Factors
Vertebrata
Vertebrates
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Description
Summary:Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time
ISSN:0021-8812
1525-3163
0021-8812
DOI:10.2527/1998.7641216x