Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants

摘要: The evolution of "informatics" technologies has the potential to generate massive databases, but extent which personalized medicine may be effectuated depends on these rich databases utilized advance understanding disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology dimension reduction. Yet, statistical methods proposed address challenges arising with high-dimensionality omics-type data predominately rely linear models emphasize associations deriving from prognostic biomarkers. Existing are often limited discovering predictive biomarkers that interact fail elucidate power their resultant selection rules. In this article, we present a Bayesian method is devised integrate both characteristics particular patient's disease. appropriately characterizes structural constraints inherent biomarkers, hence properly utilizes complementary sources information selection. illustrated through case study lower grade glioma. Theoretical considerations explored demonstrate manner in impacted by features. Additionally, simulations based an actual leukemia provided ascertain method's performance respect rules derived competing methods.