dc.contributor.author | Wanyonyi, Kennedy S | |
dc.date.accessioned | 2020-01-09T08:35:04Z | |
dc.date.available | 2020-01-09T08:35:04Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/107432 | |
dc.description.abstract | Background
Survival trees and Random Survival Forests are extensions of classi cation and regression
trees and random forests in analyzing time to event data. These methods are alternatives
to Cox Proportional hazards models when Proportional Hazard assumption is violated.
Survival tree methods are exible and can handle high dimensional covariate data as they
are fully non-parametric. Random survival forests use the Brieman’s approach, rst, by
employing a random selection of a bootstrap sample used for growing a tree then growing
tree learners by splitting the nodes on the randomly selected predictors. The performance
of the survival trees highly depended on the splitting method that is applied while growing
the tree.
0.2 Methods
In our analysis, we compare the performance of random survival forests in variable selecting
based on the following split rules; Log-rank splitting, Log-rank score splitting, and and
Conditioned Inference Forests. Our outcome variable is the under- ve child mortality in
Kenya using 2014 DHS data. Covariates that were included in the models were chosen
based on the existing literature.
0.3 Results
Findings from this study show that Log-rank split rule outperforms Log-rank score split
rule. Both split rules analyze time to event data based on the bootstrap cross-validated
estimates for integrated Brier scores.
0.4 Conclusion
As much as it is evident that Log-rank is the best, there is need to investigate other split
rules and the nature of data that that best suit each split rule to be able to identify the best
slitting method. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Survival Forests Split rules | en_US |
dc.title | Comparison of Random Survival Forests Split rules in selecting the determinants of under-five mortality in Kenya using 2014 DHS data. | en_US |
dc.type | Thesis | en_US |