  We consider the problem of identifying sub-groups of participants in a
clinical trial that have enhanced treatment effect. Recursive partitioning
methods that recursively partition the covariate space based on some measure of
between groups treatment effect difference are popular for such sub-group
identification. The most commonly used recursive partitioning method, the
classification and regression tree algorithm, first creates a large tree by
recursively partitioning the covariate space using some splitting criteria and
then selects the final tree from all subtrees of the large tree. In the context
of subgroup identification, calculation of the splitting criteria and the
evaluation measure used for final tree selection rely on comparing differences
in means between the treatment and control arm. When covariates are prognostic
for the outcome, covariate adjusted estimators have the ability to improve
efficiency compared to using differences in means between the treatment and
control group. This manuscript develops two covariate adjusted estimators that
can be used to both make splitting decisions and for final tree selection. The
performance of the resulting covariate adjusted recursive partitioning
algorithm is evaluated using simulations and by analyzing a clinical trial that
evaluates if motivational interviews improve treatment engagement for substance
abusers.
