Tuning parameter calibration for personalized prediction in medicine

Abstract

Personalized prediction is of high interest in medicine; potential applications include the prediction of individual drug responses or risks of complications. But typical statistical pipelines such as ridge estimation combined with cross-validation ignore the heterogeneity among the patients and, therefore, are not suited for personalized prediction. We, therefore, introduce an alternative ridge-type pipeline that can minimize the prediction error of each patient individually. We show that our pipeline is optimal in terms of oracle inequalities, fast, and highly effective both in simulations and on real data.