Contrast weighted learning for robust optimal treatment rule estimation.

ABSTRACT: Personalized medicine aims to tailor medical decisions based on patient-specific characteristics. Advances in data capturing techniques such as electronic health records dramatically increase the availability of comprehensive patient profiles, promoting the rapid development of optimal treatment rule (OTR) estimation methods. An archetypal OTR estimation approach is the outcome weighted learning, where OTR is determined under a weighted classification framework with clinical outcomes as the weights. Although outcome weighted learning has been extensively studied and extended, existing methods are susceptible to irregularities of outcome distributions such as outliers and heavy tails. Methods that involve modeling of the outcome are also sensitive to model misspecification. We propose a con

SUBMITTER: Guo X 

PROVIDER: S-EPMC9826186 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature