| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳立榜 | |
| dc.creator (作者) | Chen, Li-Pang | |
| dc.date (日期) | 2024-09 | |
| dc.date.accessioned | 2024-12-12 | - |
| dc.date.available | 2024-12-12 | - |
| dc.date.issued (上傳時間) | 2024-12-12 | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154708 | - |
| dc.description.abstract (摘要) | As a specific application of survival analysis, one of main interests in medical studies aims to analyze the patients’ survival time of a specific cancer. Typically, gene expressions are treated as covariates to characterize the survival time. In the framework of survival analysis, the accelerated failure time model in the parametric form is perhaps a common approach. However, gene expressions are possibly nonlinear and the survival time as well as censoring status are subject to measurement error. In this paper, we aim to tackle those complex features simultaneously. We first correct for measurement error in survival time and censoring status, and use them to develop a corrected Buckley–James estimator. After that, we use the boosting algorithm with the cubic spline estimation method to iteratively recover nonlinear relationship between covariates and survival time. Theoretically, we justify the validity of measurement error correction and estimation procedure. Numerical studies show that the proposed method improves the performance of estimation and is able to capture informative covariates. The methodology is primarily used to analyze the breast cancer data provided by the Netherlands Cancer Institute for research. | |
| dc.format.extent | 106 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Statistics and Computing, Vol.34, article number 183, pp.1-19 | |
| dc.subject (關鍵詞) | Boosting; Cubic spline; Measurement error; Misclassification; Regression calibration; Variable selection | |
| dc.title (題名) | Accelerated failure time models with error-prone response and nonlinear covariates | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1007/s11222-024-10491-9 | |
| dc.doi.uri (DOI) | https://doi.org/10.1007/s11222-024-10491-9 | |
