| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳立榜 | |
| dc.creator (作者) | Chen, Li-Pang;Huang, Hsiao-Ting | |
| dc.date (日期) | 2024-08 | |
| dc.date.accessioned | 28-Oct-2024 11:42:47 (UTC+8) | - |
| dc.date.available | 28-Oct-2024 11:42:47 (UTC+8) | - |
| dc.date.issued (上傳時間) | 28-Oct-2024 11:42:47 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154113 | - |
| dc.description.abstract (摘要) | Survival analysis has been used to characterize the time-to-event data. In medical studies, a typical application is to analyze the survival time of specific cancers by using high-dimensional gene expressions. The main challenges include the involvement of non-informaive gene expressions and possibly nonlinear relationship between survival time and gene expressions. Moreover, due to possibly imprecise data collection or wrong record, measurement error might be ubiquitous in the survival time and its censoring status. Ignoring measurement error effects may incur biased estimator and wrong conclusion. | |
| dc.format.extent | 106 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | BMC Bioinformatics, Vol.25, Article number: 265, pp.1-20 | |
| dc.subject (關鍵詞) | Boosting; Gene expression; Measurement error; Survival analysis | |
| dc.title (題名) | AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1186/s12859-024-05831-5 | |
| dc.doi.uri (DOI) | https://doi.org/10.1186/s12859-024-05831-5 | |
