  High-dimensional sparse modeling with censored survival data is of great
practical importance, and several methods have been proposed for variable
selection based on different models. However, the impact of biased sample
caused by left-truncation and covariates measurement error to variable
selection is not fully explored. In this paper, we mainly focus on the additive
hazards model and analyze the high-dimensional survival data subject to
left-truncation and measurement error in covariates. We develop the three-stage
procedure to correct the error effect, select variables, and estimate the
parameters of interest simultaneously. Numerical studies are reported to assess
the performance of the proposed methods.
