Random Coefficient Models: How to Model Longitudinal and Hierarchical Data in JMP® Pro
Feb 3, 2016 5:55 AM
Random Coefficient Models.jrn
Elizabeth Claassen, PhD, JMP Research Statistician Tester, SAS
Chris Gotwalt, PhD, JMP Director of Statistical Research and Development, SAS
The addition of the Mixed Model personality of Fit Model (Fit Mixed) has greatly expanded the ability of JMP Pro to analyse longitudinal data that consists of repeated measurements taken over time. It is perhaps not yet fully appreciated how useful the platform is across a wide variety of subject matter areas, including the social sciences, product reliability and agriculture. The same underlying methodology in the platform can be used to model individual growth/degradation curves or to model experimental units that are organised in a hierarchy, such as students within schools or individual plants within plots. Because the methods for modelling effects over time developed separately in several different disciplines, there can be big differences in terminology for what are basically the same models. For example, random coefficient models, hierarchical linear models (HLMs) and hierarchical Bayes models are essentially the same models, and all can be fit using Fit Mixed in JMP Pro 11 and JMP Pro 12. In this presentation we demonstrate the capabilities of Fit Mixed for random coefficient models using examples from the pharmaceutical industry and social sciences. This includes making the “translation” from SAS PROC MIXED for JMP users accustomed to fitting mixed models in SAS.