01 Introduction

Introduction

The goal of this vignette is to show most of all possibilies with aVT (for aVirtualTwins meaning adaptation of Virtual Twins method) package.

VT method (Jared Foster and al, 2011) has been created to find subgroup of patients with enhanced treatment effect, if it exists. Theorically, this method can be used for binary and continous outcome. This package only deals with binary outcome in a two arms clinical trial.

VT method is based on random forests and regression/classification trees.

I decided to use a simulated dataset called sepsis in order to show how aVT package can be used. Type ?sepsis to know more about this dataset. Anyway, the true subgroup is PRAPACHE <= 26 & AGE <= 49.80.

NOTE: This true subgroup is defined with the lower event rate (survival = 1) in treatement arm. Therefore in following examples we'll search the subgroup with the highest event rate, and we know it is PRAPACHE > 26 & AGE > 49.80.