Processes | Subgroup Analysis | Local Control

Local Control
Local Control estimates treatment effects within clusters of subjects as described in Lopiano, Obenchain and Young (2014) 1 . You can use this process interactively to select the number of clusters and cumulatively compare results. This kind of analysis is useful for handling subgroups in observational or experimental data.
What do I need?
One wide format data set is required to run the Local Control process. This data set must contain one column containing the dependent response variable , one column containing the treatment variable, and multiple columns to be used as predictor variables.
The adsl_dii.sas7bdat data set, partially shown below, details results for 902 subjects. Subjects are listed in rows, demographic information, trial details, and findings and results are listed in columns. The ARM column lists the treatment variable. The DEATHFL column lists the dependent variable . The predictor variables are spread across 310 columns.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .
Refer to the Local Control output documentation for detailed descriptions of the output and guides to interpreting your results.

Lopiano, K. K., Obenchain, R. L. and Young, S. S. (2014) Fair treatment comparisons in observational research. Statistical Analytic Data Mining , 7: 376–384. doi:10.1002/sam.11235