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Approaches for Handling Time-Varying Covariates in Survival Models
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 of...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867613166080360448 |
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| access_status_str | Open Access |
| author | Nwoko, Onyekachi Esther |
| author2 | Little, Francesca |
| author_browse | Little, Francesca Nwoko, Onyekachi Esther |
| author_facet | Little, Francesca Nwoko, Onyekachi Esther |
| author_sort | Nwoko, Onyekachi Esther |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31187 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:48.735Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31187 Approaches for Handling Time-Varying Covariates in Survival Models Nwoko, Onyekachi Esther Little, Francesca Survival models longitudinal models time-dependent effects time-varying covariates 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. 2020-02-20T09:48:31Z 2020-02-20T09:48:31Z 2019 2020-02-14T08:17:02Z Master Thesis Masters MSc http://hdl.handle.net/11427/31187 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Survival models longitudinal models time-dependent effects time-varying covariates Nwoko, Onyekachi Esther Approaches for Handling Time-Varying Covariates in Survival Models |
| thesis_degree_str | Master's |
| title | Approaches for Handling Time-Varying Covariates in Survival Models |
| title_full | Approaches for Handling Time-Varying Covariates in Survival Models |
| title_fullStr | Approaches for Handling Time-Varying Covariates in Survival Models |
| title_full_unstemmed | Approaches for Handling Time-Varying Covariates in Survival Models |
| title_short | Approaches for Handling Time-Varying Covariates in Survival Models |
| title_sort | approaches for handling time varying covariates in survival models |
| topic | Survival models longitudinal models time-dependent effects time-varying covariates |
| url | http://hdl.handle.net/11427/31187 |
| work_keys_str_mv | AT nwokoonyekachiesther approachesforhandlingtimevaryingcovariatesinsurvivalmodels |