Department of Statistics, National Chengchi University, Taipei, Taiwan.
Biometrics. 2023 Dec;79(4):3929-3940. doi: 10.1111/biom.13898. Epub 2023 Jul 17.
In this paper, we analyze the length-biased and partly interval-censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low-dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high-dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, 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.
在本文中，我们分析了长度有偏和部分区间删失数据，其主要挑战来自于有偏抽样和区间删失引起的干扰。与现有方法主要关注低维数据并假设协变量被精确测量不同，有时研究人员可能会遇到受到测量误差影响的高维数据，这在应用中很常见，并且会导致估计不可靠。为了解决这些挑战，我们探索了一种有效的推断方法，用于在加速失效时间模型下处理具有协变量测量误差的高维长度有偏和区间删失生存数据。我们主要采用 SIMEX 方法来校正测量误差的影响，并提出了提升过程来进行变量选择和估计。所提出的方法能够处理协变量维度大于样本量的情况，并具有吸引人的特征，即协变量的分布是未指定的。
