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Title: Long-term net survival among women diagnosed with cancer : accuracy of its estimation and evaluation of its determinants
Author: Schaffar, R.
ISNI:       0000 0004 7229 1636
Awarding Body: London School of Hygiene & Tropical Medicine
Current Institution: London School of Hygiene and Tropical Medicine (University of London)
Date of Award: 2018
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Breast cancer is a major public health challenge. It affects very large numbers of women across the globe. Although improvements in its management have dramatically transformed its prognosis at diagnosis, breast cancer remains associated with an increased long-term risk of death, persisting even decades after diagnosis. A comprehensive understanding of this underlying pattern of death from breast cancer in the long-term is currently lacking but increasingly important as the number of long-term survivors rises. The reliability of the cause of death is of particular interest in this context. In this thesis, I use data from the Geneva Cancer Registry to first, determine the best methodology for examining long-term net survival, and second, to evaluate its determinants. Two data settings are available for the estimation of net survival: the cause-specific setting, where the cause of death is required, and the relative-survival setting, where it is not. I first evaluated the accuracy of routinely collected cause of death information and the impact of inaccuracies upon survival estimates. I observed small but non-negligible advantages in using a reviewed cause of death when estimating survival. I then compared the cause-specific to the relative survival setting for the estimation of long-term net survival and demonstrated that the relative-survival setting was less sensitive to violations of the assumptions both for breast cancer patients as well as for patients diagnosed with cancer at three other localisations. I further investigated the long-term effects of key prognostic factors and treatment for women with breast cancer in the relative survival setting using an appropriate strategy for model selection. Although I demonstrated insightful non-linear and time-dependent effects for some prognostic variables, the analyses were limited by issues of convergence and misspecification of the model. High quality population-based data and additional statistical tools are required to understand with greater certainty the determinants of breast cancer long-term excess mortality.
Supervisor: Woods, L. ; Rachet, B. Sponsor: Swiss Cancer League ; Geneva Cancer Registry
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral