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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects: QA, R1
This thesis is focused on the topic of competing risks survival analysis. The first\ud chapter provides an introduction and motivation with a brief literature review. Chapter 2\ud considers the fundamental functional of all competing risks data: the crude incidence\ud function. This function is considered in the light of the counting process framework\ud which provides powerful mathematics to calculate confidence bands in an analytical\ud form, rather than bootstrapping or simulation.\ud Chapter 3 takes the Peterson bounds and considers what happens in the event\ud of covariate information. Fortunately, these bounds do become tighter in some cases.\ud Chapter 4 considers what can be inferred about the effect of covariates in the case\ud of competing risks. The conclusion is that there exist bounds on any covariate-time\ud transformation. These two preceding chapters are illustrated with a data set in chapter 5.\ud Chapter 6 considers the result of Heckman and Honore (1989) and investigates\ud the question of their generalisation. It reaches the conclusion that the simple assumption\ud of a univariate covariate-time transformation is not enough to provide identifiability.\ud More practical questions of modeling dependent competing risks data through\ud the use of frailty models to induce dependence is considered in chapter 7. A practical\ud and implementable model is illustrated.\ud A diversion is taken into more abstract probability theory in chapter 8 which\ud considers the Bayesian non-parametric tool: P61ya trees. The novel framework of this\ud tool is explained and some results are obtained concerning the limiting random density\ud function and the issues which arise when trying to integrate with a realised P61ya\ud distribution as the integrating measure.\ud Chapter 9 applies the theory of chapters 7 and 8 to a competing risks data set\ud of a prostate cancer clinical trial. This has several continuous baseline covariates and\ud gives the opportunity to use a frailty model discussed in chapter 7 where the unknown\ud frailty distribution is modeled using a P61ya tree which is considered in chapter 8.\ud An overview of the thesis is provided in chapter 10 and directions for future\ud research are considered here.
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