Bootstrapping methods for assessing causality in survival analysis: A case study on major adverse cardiovascular events
(2023) In Master's Theses in Mathematical Sciences MASM02 20231Mathematical Statistics
- Abstract
- The combination of graphical models with Aalen's additive hazards model, resulting in a model known as dynamical path analysis, permits assessing the effects of different variables on times until an event and decomposing these total effects into direct and indirect effects. This thesis proposes novel bootstrapping methods designed for left-truncated right-censored data, conditional on covariates within the framework of Aalen's additive hazards model,
in order to obtain confidence intervals for the estimates.
To illustrate the practical application of the bootstrapping methods, we conduct a case study utilising data from the Malmö diet and cancer study. The data set consists of left-truncated right-censored data.
Our analysis aims... (More) - The combination of graphical models with Aalen's additive hazards model, resulting in a model known as dynamical path analysis, permits assessing the effects of different variables on times until an event and decomposing these total effects into direct and indirect effects. This thesis proposes novel bootstrapping methods designed for left-truncated right-censored data, conditional on covariates within the framework of Aalen's additive hazards model,
in order to obtain confidence intervals for the estimates.
To illustrate the practical application of the bootstrapping methods, we conduct a case study utilising data from the Malmö diet and cancer study. The data set consists of left-truncated right-censored data.
Our analysis aims to examine causality and estimate the direct effects of various covariates on the incidence of major adverse cardiovascular events and indirect effects between covariates.
We compute confidence intervals for these effects with the proposed bootstrapping methods. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9127200
- author
- Benthem Ciano, Paulina LU
- supervisor
- organization
- course
- MASM02 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Causality, Graphical models, Bootstrap, Survival analysis, Additive hazards model.
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUNFMS-3119-2023
- ISSN
- 1404-6342
- other publication id
- 2023:E42
- language
- English
- id
- 9127200
- date added to LUP
- 2023-06-21 10:45:58
- date last changed
- 2023-06-21 14:20:22
@misc{9127200, abstract = {{The combination of graphical models with Aalen's additive hazards model, resulting in a model known as dynamical path analysis, permits assessing the effects of different variables on times until an event and decomposing these total effects into direct and indirect effects. This thesis proposes novel bootstrapping methods designed for left-truncated right-censored data, conditional on covariates within the framework of Aalen's additive hazards model, in order to obtain confidence intervals for the estimates. To illustrate the practical application of the bootstrapping methods, we conduct a case study utilising data from the Malmö diet and cancer study. The data set consists of left-truncated right-censored data. Our analysis aims to examine causality and estimate the direct effects of various covariates on the incidence of major adverse cardiovascular events and indirect effects between covariates. We compute confidence intervals for these effects with the proposed bootstrapping methods.}}, author = {{Benthem Ciano, Paulina}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Bootstrapping methods for assessing causality in survival analysis: A case study on major adverse cardiovascular events}}, year = {{2023}}, }