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The application and development of relative survival methods in coronary heart disease

By Christopher Paul Nelson


Relative survival is an estimate of net-survival without the need for cause-of-death information. This is achieved by matching the study cohort to the general population by various covariates, including age, sex and year of hospitalisation, in order to obtain an expected mortality rate. In this thesis relative survival methodology will be applied in heart disease where the form of the excess hazard rate is known to be very different from cancer, where this methodology originates.\ud The dataset presented is from the Leicester Royal Infirmary coronary care unit where all admissions to the unit were recorded between 1993 and 2006, which includes all patients in Leicestershire. Only patients who present with an ST-elevated acute myocardial infarction will be studied.\ud Relative survival is a new methodology in heart disease and this thesis will describe some of the problems that are encountered including the increased prevalence of the disease in the population and the very high early excess mortality rate that is not present in most cancers. Also investigated are period analysis models, which are also new to heart disease and allow the estimation of up-to-date information. An analysis of admission blood glucose levels and diabetic status is performed to examine the potential impact on patient prognosis in the short and long term, which involves the use of relative survival.\ud A new methodology is developed in this thesis for relative survival that fits spline based flexible parametric models on the log cumulative excess hazard scale. This methodology holds many advantages over current relative survival techniques due to the use of non-split- time data. This thesis demonstrates these advantages.\ud This thesis details how current relative survival methods have been extended to heart disease. A new model is developed, which is suitable in heart disease and cancer that fits flexible parametric spline based models

Publisher: University of Leicester
Year: 2009
OAI identifier: oai:lra.le.ac.uk:2381/7514

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