Penalized weighted proportional hazards model for robust variable selection and outlier detection.

ABSTRACT: Identifying exceptional responders or nonresponders is an area of increased research interest in precision medicine as these patients may have different biological or molecular features and therefore may respond differently to therapies. Our motivation stems from a real example from a clinical trial where we are interested in characterizing exceptional prostate cancer responders. We investigate the outlier detection and robust regression problem in the sparse proportional hazards model for censored survival outcomes. The main idea is to model the irregularity of each observation by assigning an individual weight to the hazard function. By applying a LASSO-type penalty on both the model parameters and the log transformation of the weight vector, our proposed method is able to perform variab

SUBMITTER: Luo B 

PROVIDER: S-EPMC9283382 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature