Abstract

Personalized medicine today is primarily addressing efficacy. Here we investigate and illustrate the role that precision medicine could play in drug safety by supporting the identification of subjects at high risk of harm by an otherwise safe and efficacious treatment. Predicting potential harm requires high sensitivity of a classification rule. After reviewing some case studies from the literature we discuss the appropriateness of two methods to identify sub-groups of patients at risk. To illustrate the methods we reanalyze one of the examples and provide performance results of the methods by simulations. It turns out that basing a predictor of the most appropriate treatment on the individual treatment estimator can achieve results superior to those obtained from within-subgroup significance tests when sensitivity and relevance are the main concerns. In conclusion, significance tests should not be the first choice approach to identify subgroups of subjects at risk of harm of an otherwise safe intervention.

Authors and Affiliations