Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants.

ABSTRACT: The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high-dimensionality of omics-type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this artic

SUBMITTER: Ma J 

PROVIDER: S-EPMC7341533 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature