Variable selection and estimation for the additive hazards model subject to left-truncation, right-censoring and measurement error in covariates. Issue 18 (11th December 2020) - Chen, Li-Pang
Abstract : Variable selection with censored survival data is of great practical importance, and several methods have been proposed for variable selection based on different models. However, the impacts of biased samples caused by left-truncation and covariate measurement error to variable selection are not fully explored. In this paper, we mainly focus on the additive hazards model and analyze variable selection and estimation for survival data subject to left-truncation and measurement error in covariates. We develop the three-stage procedure to correct for error effects, select informative variables, and estimate the parameters of interest simultaneously. Numerical studies are reported to assess the performance of the proposed methods.