Yoichi Ito, Professor, Center for Medical and Health Data Science Research, Institute of Statistical Mathematics, Inter-University Research Institute Corporation, Information and Systems Research Organization
It is known that when data is measured over time for each individual or in clusters, the data within individuals or within clusters will be correlated. If this correlation is ignored when performing analysis, the error will be underestimated and valid results will not be obtained. The standard model for such data is the mixed effects model, which can be used in JMP Pro. The mixed effects model is a general-purpose model, and it is necessary to specify the appropriate model depending on how the data was measured and what effects you want to estimate.
In this presentation, I will explain how to analyze some typical data using JMP Pro, and also explain the differences with SAS's MIXED procedure.
