Approaches for Handling Time-Varying Covariates in Survival Models
| dc.contributor.advisor | Little, Francesca | |
| dc.contributor.author | Nwoko, Onyekachi Esther | |
| dc.date.accessioned | 2020-02-20T09:48:31Z | |
| dc.date.available | 2020-02-20T09:48:31Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-14T08:17:02Z | |
| dc.description.abstract | Survival models are used in analysing time-to-event data. This type of data is very common in medical research. The Cox proportional hazard model is commonly used in analysing time-to-event data. However, this model is based on the proportional hazard (PH) assumption. Violation of this assumption often leads to biased results and inferences. Once non-proportionality is established, there is a need to consider time-varying effects of the covariates. Several models have been developed that relax the proportionality assumption making it possible to analyse data with time-varying effects of both baseline and time-updated covariates. I present various approaches for handling time-varying covariates and time-varying effects in time-to-event models. They include the extended Cox model which handles exogenous time-dependent covariates using the counting process formulation introduced by cite{andersen1982cox}. Andersen and Gill accounts for time varying covariates by each individual having multiple observations with the total-at-risk follow up for each individual being further divided into smaller time intervals. The joint models for the longitudinal and time-to-event processes and its extensions (parametrization and multivariate joint models) were used as it handles endogenous time-varying covariates appropriately. Another is the Aalen model, an additive model which accounts for time-varying effects. However, there are situations where all the covariates of interest do not have time-varying effects. Hence, the semi-parametric additive model can be used. In conclusion, comparisons are made on the results of all the fitted models and it shows that choice of a particular model to fit is influenced by the aim and objectives of fitting the model. In 2002, an AntiRetroviral Treatment (ART) service was established in the Cape Town township of Gugulethu, South Africa. These models will be applied to an HIV/AIDS observational dataset obtained from all patients who initiated ART within the programme between September 2002 and June 2007. | |
| dc.identifier.apacitation | Nwoko, O. E. (2019). <i>Approaches for Handling Time-Varying Covariates in Survival Models</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/31187 | en_ZA |
| dc.identifier.chicagocitation | Nwoko, Onyekachi Esther. <i>"Approaches for Handling Time-Varying Covariates in Survival Models."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2019. http://hdl.handle.net/11427/31187 | en_ZA |
| dc.identifier.citation | Nwoko, O. 2019. Approaches for Handling Time-Varying Covariates in Survival Models. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Nwoko, Onyekachi Esther AB - Survival models are used in analysing time-to-event data. This type of data is very common in medical research. The Cox proportional hazard model is commonly used in analysing time-to-event data. However, this model is based on the proportional hazard (PH) assumption. Violation of this assumption often leads to biased results and inferences. Once non-proportionality is established, there is a need to consider time-varying effects of the covariates. Several models have been developed that relax the proportionality assumption making it possible to analyse data with time-varying effects of both baseline and time-updated covariates. I present various approaches for handling time-varying covariates and time-varying effects in time-to-event models. They include the extended Cox model which handles exogenous time-dependent covariates using the counting process formulation introduced by cite{andersen1982cox}. Andersen and Gill accounts for time varying covariates by each individual having multiple observations with the total-at-risk follow up for each individual being further divided into smaller time intervals. The joint models for the longitudinal and time-to-event processes and its extensions (parametrization and multivariate joint models) were used as it handles endogenous time-varying covariates appropriately. Another is the Aalen model, an additive model which accounts for time-varying effects. However, there are situations where all the covariates of interest do not have time-varying effects. Hence, the semi-parametric additive model can be used. In conclusion, comparisons are made on the results of all the fitted models and it shows that choice of a particular model to fit is influenced by the aim and objectives of fitting the model. In 2002, an AntiRetroviral Treatment (ART) service was established in the Cape Town township of Gugulethu, South Africa. These models will be applied to an HIV/AIDS observational dataset obtained from all patients who initiated ART within the programme between September 2002 and June 2007. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Survival models KW - longitudinal models KW - time-dependent effects KW - time-varying covariates LK - https://open.uct.ac.za PY - 2019 T1 - Approaches for Handling Time-Varying Covariates in Survival Models TI - Approaches for Handling Time-Varying Covariates in Survival Models UR - http://hdl.handle.net/11427/31187 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31187 | |
| dc.identifier.vancouvercitation | Nwoko OE. Approaches for Handling Time-Varying Covariates in Survival Models. []. ,Faculty of Science ,Department of Statistical Sciences, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31187 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Survival models | |
| dc.subject | longitudinal models | |
| dc.subject | time-dependent effects | |
| dc.subject | time-varying covariates | |
| dc.title | Approaches for Handling Time-Varying Covariates in Survival Models | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |