AbstractThis thesis focusses on the cumulative hazard function as a tool for modelling time-to-event data, as opposed to the more common hazard or survival functions. By focussing on and providing a detailed discussion of the properties of these functions a new framework is explored for building complex models from, the relatively simple, cumulative hazards.Parametric families are thoroughly explored in this thesis by detailing types of parameters for time-to-event models. The discussion leads to the proposal of combination parametric families, which aim to provide flexible behaviour of the cumulative hazard function.A common issue in the analysis of time-to-event data is the presence of informative censoring. This thesis explores new models which are useful for dealing with this issue.
|Date of Award||22 Nov 2018|
|Supervisor||Karim Anaya-Izquierdo (Supervisor)|
Modelling techniques for time-to-event data analysis
Davis, A. (Author). 22 Nov 2018
Student thesis: Doctoral Thesis › PhD