A0697
Title: Length-biased and partly interval-censored survival data analysis with measurement error in covariates
Authors: Li-Pang Chen - National Chengchi University (Taiwan) [presenting]
Abstract: Length-biased and partly interval-censored data are considered, whose challenges primarily come from biased sampling and interfere with interval censoring. Unlike existing methods that focus on low-dimensional data and assume the covariates to be precisely measured, researchers are able to encounter high-dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, a valid inference method is explored for handling high-dimensional length-biased and interval-censored survival data with measurement error in covariates under the accelerated failure time model. The SIMEX method is primarily employed to correct the measurement error effects, and the boosting procedure for variable selection and estimation is proposed. The proposed method is able to handle the case that the dimension of covariates is larger than the sample size and enjoys appealing features that the distributions of the covariates are left unspecified.
