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Title: Bayesian survival analysis for prognostic index development with many covariates and missing data
Author: Zhao, Xiaohui
Awarding Body: University of Newcastle Upon Tyne
Current Institution: University of Newcastle upon Tyne
Date of Award: 2010
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Modern computational methods have made the use of complicated models in Bayesian survival analysis more feasible. In this thesis, we consider the Bayesian analysis of a large survival data set with more than 100 explanatory variables and 2025 patients, with diffuse large B-cell lymphoma, collected by the Scotland and Newcastle Lymphoma Group. The aim of the analysis is to use Bayesian survival modelling to produce a prognostic system offering advantages over existing prognostic indices. The system is intended for use in healthcare and also by the pharmaceutical industry in clinical trial design. It will make possible the use of more variables, and a more developed model, than existing indices, and thereby, it is hoped, will give improved prognostic precision, but will also allow computation of prognoses when only some of these variables are observed. We adopt an approach using Weibull mixture models. A difficulty arises when covariate values are missing. Omitting cases with missing values would seriously reduce the number of cases available and might distort our inference. We consider how to model the dependence of survival time on covariates and, in particular, how to construct a missing data model for such a large and diverse set of variables, both for the initial analysis and for the use of the system with new patients when only some covariates are observed. We compare different approaches, which involve factorising the joint probability density of survival time and the covariates in different ways. In particular we introduce a model in which the joint distribution is constructed by modelling the conditional distribution of some covariates on the survival time. The methodology developed should be applicable to Bayesian analysis of other similar large survival data sets where there are missing covariate values.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available