| Full text | |
| Author(s): |
Hashimoto, Elizabeth M.
[1]
;
Ortega, Edwin M. M.
[1]
;
Cordeiro, Gauss M.
[2]
;
Barreto, Mauricio L.
[3]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, Dept Ciencias Exatas, BR-13418900 Sao Paulo - Brazil
[2] Univ Fed Pernambuco, Dept Estat, Recife, PE - Brazil
[3] Univ Fed Bahia, Inst Saude Colet, BR-41170290 Salvador, BA - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | Journal of Biopharmaceutical Statistics; v. 22, n. 1, p. 141-159, 2012. |
| Web of Science Citations: | 7 |
| Abstract | |
The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data. (AU) | |
| FAPESP's process: | 10/04496-2 - Gamma-G regression model in survival analysis |
| Grantee: | Elizabeth Mie Hashimoto |
| Support Opportunities: | Scholarships in Brazil - Doctorate |