Interpretability of individualized treatment regimens

Music City Center 

Individualized treatment regimen (ITR) and subgroup identification are active research areas in clinical trial biostatistics. Recent advances have leveraged modern machine learning methods to identify patient characteristics that predict a stronger benefit of experimental therapies compared to the current standard of care. However, many of these methods produce black-box solutions. While some researchers have proposed simplifying treatment recommendation rules using tree-based methods, the results have been less than satisfactory. This presentation will discuss interpretability tools for treatment recommendations based on Phase III clinical trials. We will reflect on the feasibility of achieving the holy grail of individualized medicine and explore possible paths leading to it.

individualized medicine, subgroup identification, interpretable machine learning