  The identification of patient subgroups with differential treatment effects
is the first step towards individualised treatments. A current draft guideline
by the EMA discusses potentials and problems in subgroup analyses and
formulated challenges to the development of appropriate statistical procedures
for the data-driven identification of patient subgroups. We introduce
model-based recursive partitioning as a procedure for the automated detection
of patient subgroups that are identifiable by predictive factors. The method
starts with a model for the overall treatment effect as defined for the primary
analysis in the study protocol and uses measures for detecting parameter
instabilities in this treatment effect. The procedure produces a segmented
model with differential treatment parameters corresponding to each patient
subgroup. The subgroups are linked to predictive factors by means of a decision
tree. The method is applied to the search for subgroups of patients suffering
from amyotrophic lateral sclerosis that differ with respect to their Riluzole
treatment effect, the only currently approved drug for this disease.
