Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection.

ABSTRACT: The goal of the optimal treatment regime is maximizing treatment benefits via personalized treatment assignments based on the observed patient and treatment characteristics. Parametric regression-based outcome learning approaches require exploring complex interplay between the outcome and treatment assignments adjusting for the patient and treatment covariates, yet correctly specifying such relationships is challenging. Thus, a robust method against misspecified models is desirable in practice. Parsimonious models are also desired to pursue a concise interpretation and to avoid including spurious predictors of the outcome or treatment benefits. These issues have not been comprehensively addressed in the presence of competing risks. Recognizing that competing risks and group variables are f

SUBMITTER: He Y 

PROVIDER: S-EPMC8117077 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature