Subgroup Analysis

Alpha

Apply adaptive weights

Categorical Clustering Variables

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 Categorical Clustering Variables

List-Style Specification of Class Clustering Variables

List-Style Specification of Continuous Clustering Variables

List-Style Specification of Predictor Categorical 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 Categorical Variables

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

Target Variable

Treatment Control Level

Treatment Variable

Type of Dependent Variables

Use Interactions between treatment and predictor variables

Variable Selection Method

Variability Assumption

Weight Variable