Development of Flexible Parametric Models for Competing Risks and Tools to Facilitate in the Understanding and Communication of Cancer Survival

In population-based cancer studies, researchers are often only interested in cancer-specific survival to determine variations in the impact of cancer in different population groups. In such cases, the net survival measure is usually reported. However, this is of little relevance for patients as it does not consider the probability of dying from other causes before dying from cancer, otherwise known as competing risks. Therefore, alternative measures that take this into account are required for a better representation of cancer survival in the real-world. Measures estimated from within this framework provide a more meaningful interpretation for patients which can be communicated to facilitate treatment-related decisions. Differences in interpretation between various cancer survival measures, and when they are appropriate, has led to some confusion amongst non-statisticians. This motivates the development of publicly available tools to improve understanding and communication. Thus, an aim of this thesis is to develop an interactive web-tool to aid interpretation of various important cancer survival measures that are commonly reported. Although not a new concept, many often fail to account for competing risks when it is necessary for a study. Even when accounted for, many apply the theory, or report analyses incorrectly. Recently, efforts have been made to make competing risks methods more accessible for researchers from within the increasingly popular flexible parametric modelling framework. However, much work is yet to be done, especially as cancer registry datasets are becoming larger with more detailed covariate information. This means that models are increasing in complexity and more computationally efficient methods are required. With this in mind, the primary aim of this PhD is to further develop competing risks methods from within the flexible parametric modelling framework. Particular focus is on obtaining predictions with less computational effort that facilitate communication of risk when interest is in prognosis.