Visualization and interpretation of individualized treatment rule results

This chapter presents a framework to estimate patient-level treatment effects from real-world data and facilitate the visualization and interpretation of the heterogeneity of treatment effects and individualized treatment decision rules. In the previous chapters, the study of precision medicine has been approached in different ways. Despite the various nomenclatures and models used, the essence of such studies lies in translating data into actionable knowledge. To convey meaningful messages to clinicians and researchers during the decision-making in practice and to accelerate research dissemination, it is essential that the results of the individualized treatment rules are correctly derived and clearly presented. This chapter will demonstrate the importance and good practices of visualization and interpretation of individualized treatment rules through an example of real-world data in multiple sclerosis with the application of novel individualized approaches that aim to assess the heterogeneity of treatment effects. R code and a tutorial are available online to accompany this chapter.