| dc.contributor.author | Chepkech, W. K. | |
| dc.contributor.author | Chelule, J. C. | |
| dc.contributor.author | Ayubu, Anapapa O. | |
| dc.contributor.author | Imboga, H. | |
| dc.date.accessioned | 2021-04-29T11:19:58Z | |
| dc.date.available | 2021-04-29T11:19:58Z | |
| dc.date.issued | 2019-05 | |
| dc.identifier.citation | International Journal of Science and Research (IJSR), Volume 8 Issue 5, May 2019 | en_US |
| dc.identifier.issn | 2319-7064 | |
| dc.identifier.uri | https://www.ijsr.net/get_abstract.php?paper_id=ART20197461 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/4617 | |
| dc.description.abstract | Cancer incidence and mortality are rapidly growing worldwide. The reasons are complex but reflect both aging and growth of the population, as well as changes in the prevalence and distribution of the main risk factors for cancer, several of which are associated with socioeconomic development. This study was analysed using Weibull distribution model on the survival of cancer patients. This study is inspired by Siegel R. L, et al (2017) .The main aim of this study was to analyse the survival of cancer patients using Weibull distribution model. The general objective of this study was to analyse the survival of cancer patients using Weibull parametric model. Analysis was performed using R and other mathematical modules were discussed. From the analysed data, age of diagnosis, Histologic grade, Cancer type, BMI, stages of infections and Gender were significant factors in the survival of Cancer patients. Family history and treatment administered did not show much significant effect. In future studies, using other parametric distributions such as gamma Weibull distributions which leads to cover different types of hazard functions were suggested. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Weibull distribution model, Survival Time, Cancer disease | en_US |
| dc.title | Survival Analysis of Cancer Patients Using Weibull Parametric Model | en_US |
| dc.type | Article | en_US |
