Title: Individual Treatment Effect Prediction Using Model-Based Random Forests

Authors: Heidi Seibold, Achim Zeileis, Torsten Hothorn

Affiliation: Universität Innsbruck

Abstract:
Established statistical procedures for the analysis of primary endpoints in
randomized clinical trials assume that there is a universal (i.e., constant)
treatment effect that applies to all patients in the trial and - even more
importantly - to all future patients potentially to be treated with the novum
under consideration. Methods for subgroup analysis relax this assumption and
allow the treatment effect to depend on patient characteristics. This
means that the treatment effect and thus the potential benefit or harm of a
specific therapy is different in subgroups of patients who have been diagnosed
with the same disease. We introduce model-based recursive partitioning as a
procedure for the automated detection of patient subgroups that are
identifiable by patient characteristics. For more individualized treatment
effects we propose the use of ensembles of model-based trees, i.e., model-based 
random forests.