Our methodological perspective assumes that individual patients respond differently to pharmacological treatments and that thorough (quantitative) knowledge of effect modifiers will help predicting individual responses to medicines (personalized medicine). Validated decision support could efficiently complement clinical guidelines and support the healthcare professional when interpreting complex patient data, weighing the benefit and risks of multiple treatment options, and trying to incorporate patient preferences to finally design a personalized treatment plan. The ultimate goal would be to select the most efficacious therapies (to avoid nonresponse), avert adverse drug events (to avoid harm), and thus reduce costs and improve relevant endpoints (i.e., survival or quality of life) for patients and other stakeholders.