Statistical issues and limitations in personalized medicine research with clinical trials

We discuss using clinical trial data to construct and evaluate rules that use baseline covariates to assign different treatments to different patients. Given such a candidate personalization rule, we first note that its performance can often be evaluated without actually applying the rule to subjects, and a class of estimators is characterized from a statistical efficiency standpoint. We also point out a recently noted reduction of the rule construction problem to a classification task and extend results in this direction. Together these facts suggest a natural form of cross-validation in which a personalized medicine rule can be constructed from clinical trial data using standard classification tools and then evaluated in a replicated trial. Because replication is often required by the FDA to provide evidence of safety and efficacy before pharmaceutical drugs can be marketed, there are abundant data with which to explore the potential benefits of more tailored therapy. We constructed and evaluated personalized medicine rules using simulations based on two active-controlled randomized clinical trials of antibacterial drugs for the treatment of skin and skin structure infections. Unfortunately we present negative results that did not suggest benefit from personalization. We discuss the implications of this finding and why statistical approaches to personalized medicine problems will often face difficult challenges.