  A plethora of disparate statistical methods have been proposed for subgroup
identification to help tailor treatment decisions for patients. However a
majority of them do not have corresponding R packages and the few that do
pertain to particular statistical methods or provide little means of evaluating
whether meaningful subgroups have been found. Recently, the work of Chen, Tian,
Cai, and Yu (2017) unified many of these subgroup identification methods into
one general, consistent framework. The goal of the personalized package is to
provide a corresponding unified software framework for subgroup identification
analyses that provides not only estimation of subgroups, but evaluation of
treatment effects within estimated subgroups. The personalized package allows
for a variety of subgroup identification methods for many types of outcomes
commonly encountered in medical settings. The package is built to incorporate
the entire subgroup identification analysis pipeline including propensity score
diagnostics, subgroup estimation, analysis of the treatment effects within
subgroups, and evaluation of identified subgroups. In this framework, different
methods can be accessed with little change in the analysis code. Similarly, new
methods can easily be incorporated into the package. Besides familiar
statistical models, the package also allows flexible machine learning tools to
be leveraged in subgroup identification. Further estimation improvements can be
obtained via efficiency augmentation.
