Subgroup Analysis Alpha Apply adaptive weights Class Clustering Variables Cluster Effect Type Continuous Clustering Variables Criterion for Stopping Model Selection CV Partitioning Method Dependent Variable Direction of Enhanced Treatment Effect Fixed Main Effects Input SAS Data Set JMP Script Output File Name K for K-Fold CV List-Style Specification of Class Clustering Variables List-Style Specification of Continuous Clustering Variables List-Style Specification of Predictor Class Variables List-Style Specification of Predictor Continuous Variables Main Effects Selection Method Max Number of Variables to Consider for Splitting a Node Maximum Depth of Tree Maximum Number of Steps Maximum Number of Trees Minimum Number of Observations Required for a Branch Minimum Number of Observations Required for a Categorical Value Maximum Order of Interactions Model Selection Method Number of Bins for Continuous Variables Number of Principal Components to Use for Propensity Scoring Number of Resamples to Use for Computing Significance Output Data Set Prefix Output Folder Predictor Class Variables Predictor Continuous Variables Random Number Seed Random Number Seed for Forest Regard missing values as valid for prediction Relaxation Factor for Alpha SL for Adding Variables SL for Keeping Variables Study Treatment Control Level Treatment Variable Type of Dependent Variables Use Interactions between treatment and predictor variables Variable Selection Method Variability Assumption Weight Variable