  We present methodological advances in understanding the effectiveness of
personalized medicine models and supply easy-to-use open-source software.
Personalized medicine involves the systematic use of individual patient
characteristics to determine which treatment option is most likely to result in
a better outcome for the patient on average. Why is personalized medicine not
done more in practice? One of many reasons is because practitioners do not have
any easy way to holistically evaluate whether their personalization procedure
does better than the standard of care. Our software, "Personalized Treatment
Evaluator" (the R package PTE), provides inference for improvement
out-of-sample in many clinical scenarios. We also extend current methodology by
allowing evaluation of improvement in the case where the endpoint is binary or
survival. In the software, the practitioner inputs (1) data from a single-stage
randomized trial with one continuous, incidence or survival endpoint and (2) a
functional form of a model for the endpoint constructed from domain knowledge.
The bootstrap is then employed on data unseen during model fitting to provide
confidence intervals for the improvement for the average future patient
(assuming future patients are similar to the patients in the trial). One may
also test against a null scenario where the hypothesized personalization are
not more useful than a standard of care. We demonstrate our method's promise on
simulated data as well as on data from a randomized comparative trial
investigating two treatments for depression.
