Outlier effects on robust joint modelling of longitudinal and survival data

Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their detrimental impact being down weighed and thus providing more efficient and reliable estimates. The robust joint modelling technique and its major benefits are showcased through the analysis of Northern Irish end stage renal disease patients. With an ageing population and growing prevalence of chronic kidney disease within the United Kingdom, there is a pressing demand to investigate the detrimental relationship between the changing haemoglobin levels of haemodialysis patients and their survival. As outliers within the NI renal data were found to have significantly worse survival, identification of outlying individuals through robust joint modelling may aid nephrologists to improve patient's survival. A simulation study was also undertaken to explore the difference between robust and standard joint models in the presence of increasing proportions and extremity of longitudinal outliers. More efficient and reliable estimates were obtained by robust joint models with increasing contrast between the robust and standard joint models when a greater proportion of more extreme outliers are present. Through illustration of the gains in efficiency and reliability of parameters when outliers exist, the potential of robust joint modelling is evident. The research presented in this thesis highlights the benefits and stresses the need to utilise a more robust approach to joint modelling in the presence of longitudinal outliers.

History Matters: How International Regimes Become Entrenched—and Why We Suffer for It

International regimes are composite historical constructions. They are built-up through bricolage, as resource-strapped officials combine operational capacities, frequently turning to outside assistance. Who wins and loses—and why—when organisations are added or subtracted? What happens when inter-organisational relations are recalibrated? Why do regimes cohere as they do? By comparing the development of financial-regulatory regimes and probing other illustrative cases, I offer an explanatory framework that emphasizes the importance of timing and sequencing in determining outcomes. Thinking beyond interstate network effects and switching costs, I distil new data and theoretical insights into how and why temporality matters in global politics. I find that time structures the strategic bargaining contexts that mediate the intense distributional struggles between organisations driving key institutional reforms. The explanatory power of this framework upsets conventional wisdom whereby the distribution of state power, and the dynamics of interstate bargaining, are assumed the critical sources of institutional reform.