Advancing and appraising competing risks methodology for better communication of survival statistics

The probability of an event occurring or the proportion of patients experiencing an event, such as death or disease, is often of interest in medical research. It is a measure that is intuitively appealing to many consumers of statistics and yet the estimation is not always clearly understood or straightforward. Many researchers will take the complement of the survival function, obtained using the Kaplan-Meier estimator. However, in situations where patients are also at risk of competing events, the interpretation of such estimates may not be meaningful. Competing risks are present in almost all areas of medical research. They occur when patients are at risk of more than one mutually exclusive event, such as death from different causes. Although methods for the analysis of survival data in the presence of competing risks have been around since the 1760s there is increasing evidence that these methods are being underused. The primary aim of this thesis is to develop and apply new and accessible methods for analysing competing risks in order to enable better communication of the estimates obtained from such analyses. These developments will primarily involve the use of the recently established exible parametric survival model. Several applications of the methods will be considered in various areas of medical research to demonstrate the necessity of competing risks theory. As there is still a great amount of misunderstanding amongst clinical researchers about when these methods should be applied, considerations are made as to how to best present results. Finally, key concepts and assumptions of the methods will be assessed through sensitivity analyses and implications of data quality will be investigated through the use of a simulation study.