APPROACH OF LINEAR MIXED MODEL IN LONGITUDINAL DATA ANALYSIS USING SAS

  • Pankaj Tiwari LES & IT Divison, Ivri, Izzatnagar, Bareilly
  • Gaurav Shukla LES & IT Divison, Ivri, Izzatnagar, Bareilly
Keywords: ML, REML, Random Effect, Variance Components, Covariance Structure, AIC, BIC, Likelihood Function

Abstract

Linear mixed model is one of the best methodologies for 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 development of widely available software. This paper reviews and summarizes the methodology of Linear Mixed Model approach for the analysis of repeated measurements data using SAS Software. PROC MIXED in SAS provides a very flexible environment in which model can be many type of repeated measures data. It can be repeated in time, space or both. Correlation among measurements made on same subject or experiment unit can be modeled using random effect and through the specification of a covariance structure. PROC MIXED provides a useful covariance structures or modeling both time and space, including discrete & continuous increments of time and space.

 

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Published
2011-11-11
Section
Articles