Modeling pseudo-observations with covariate dependent censoring: robustness of the method against misspecified censoring models
The so called pseudo-observations in survival analysis were introduced by recent studies that reviewed this method when estimating different parameters using regressions models (Andersen and Perme, Stat. Meth. Med. Res., 2010) with the condition that the censoring distribution is independent from co...
| Autor: | |
|---|---|
| Tipo de documento: | dissertação |
| Data de publicação: | 2018 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/113468 |
| Acesso em linha: | https://hdl.handle.net/2117/113468 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Survival analysis (Biometry) Survival analysis Cox Model Dependent censoring Pseudo-values Monte Carlo Simulation Cumulative Incidence Function Restricted Mean Lifetime Anàlisi de supervivència (Biometria) Classificació AMS::62 Statistics::62N Survival analysis and censored data Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Resumo: | The so called pseudo-observations in survival analysis were introduced by recent studies that reviewed this method when estimating different parameters using regressions models (Andersen and Perme, Stat. Meth. Med. Res., 2010) with the condition that the censoring distribution is independent from covariates. If censoring depends on covariates, the method based on pseudo-observations requires modeling of the censoring distribution, which leads to the construction of alternative estimators based on censoring probability weighting. This master thesis will present the proposal of Andersen and Perme and -- by means of Monte Carlo simulation -- will also study its robustness if the model for the censoring distribution is misspecified. Two alternative estimators will be explained and used for the study of robustness of the method: the Cumulative Incidence Function and the Restricted Mean Lifetime. |
|---|