A0503
Title: Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates
Authors: Li-Pang Chen - National Chengchi University (Taiwan) [presenting]
Bangxu Qiu - National Chengchi University (Taiwan)
Abstract: Analysis of length-biased and interval-censored data is an important topic in survival analysis, and many methods have been developed to address this complex data structure. However, these methods focus on low-dimensional data and assume the covariates to be precisely measured, while high-dimensional data subject to measurement error are frequently collected in applications. We explore a valid inference method for handling high-dimensional length-biased and interval-censored survival data with measurement error in covariates under the accelerated failure time model. We primarily employ the SIMEX method to correct for measurement error effects and propose the boosting procedure to do variable selection and estimation. 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.
