| dc.contributor | 統計系 | |
| dc.creator (作者) | Hwang, Y.T. | |
| dc.creator (作者) | 黃佳慧 | |
| dc.creator (作者) | Huang, C.-H. | |
| dc.creator (作者) | Yeh, W.L. | |
| dc.creator (作者) | Shen, Y.D. | |
| dc.date (日期) | 2016-04 | |
| dc.date.accessioned | 13-Aug-2019 09:18:21 (UTC+8) | - |
| dc.date.available | 13-Aug-2019 09:18:21 (UTC+8) | - |
| dc.date.issued (上傳時間) | 13-Aug-2019 09:18:21 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/125122 | - |
| dc.description.abstract (摘要) | Identifying cost-effective decisions that can take into account of medical cost and health outcome is an important issue under very limited resources. Analyzing medical costs has been challenged owing to skewness of cost distributions, heterogeneity across samples and censoring. When censoring is due to administrative reasons, the total cost might be related to the survival time since longer survivals are likely to be censored and the corresponding total cost will be censored as well. This paper uses the general linear model for the longitudinal data to model the repeated medical cost data and the weighted estimating equation is used to find more accurate estimates for the parameter. Furthermore, the asymptotic properties for the proposed model are discussed. Simulations are used to evaluate the performance of estimators under various scenarios. Finally, the proposed model is implemented on the data extracted from National Health Insurance database for patients with the colorectal cancer. | |
| dc.format.extent | 1917071 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Journal of Applied Statistics, Vol.44, pp.288-307 | |
| dc.subject (關鍵詞) | General linear model ; inverse probability weighted method ; medical cost ; longitudinaldata ; proportional hazards model | |
| dc.title (題名) | The weighted general linear model for longitudinal medical cost data-an application in colorectal cancer | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/02664763.2016.1169255 | |
| dc.doi.uri (DOI) | https://doi.org/10.1080/02664763.2016.1169255 | |
