| dc.contributor.author | Ikiao, Josphat Mutwiri | |
| dc.contributor.author | Kennedy, Nyongesa | |
| dc.contributor.author | Gitunga, Robert Muriungi | |
| dc.date.accessioned | 2018-11-13T08:46:35Z | |
| dc.date.accessioned | 2020-02-06T14:01:23Z | |
| dc.date.available | 2018-11-13T08:46:35Z | |
| dc.date.available | 2020-02-06T14:01:23Z | |
| dc.date.issued | 2018-04 | |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/975 | |
| dc.description.abstract | Survival analysis majors mainly on estimation of time taken before an event of interest takes place. Time taken
before an event of interest takes place is a random process that takes shape overtime. Stochastic processes theory is therefore very crucial in analysis of survival data. The study employed markov chain theory in developing a simple stochastic stomach cancer model. The model is depicted with a state diagram and a stochastic matrix. The model was applied to stomach cancer data obtained from Meru Hospice. Transition probability theory was used in determining transition probabilities. The entries of the stochastic matrix T were estimated using the Aalen-Johansen estimators. The time taken for all the people under the study to transit to death was estimated using the limiting matrix. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Science Publishing Group | en_US |
| dc.subject | Stochastic Stomach Cancer Model, State Diagram, Stochastic Matrix, Transition Probabilities, Limiting Matrix | en_US |
| dc.title | A Simple Stochastic Stomach Cancer Model with Application | en_US |
| dc.type | Article | en_US |
