Schaffar, R; (2018) Long-term net survival among women diagnosed with cancer: accuracy of its estimation and evaluation of its determinants. PhD (research paper style) thesis, London School of Hygiene & Tropical Medicine. DOI: https://doi.org/10.17037/PUBS.04648206
Permanent Identifier
Use this Digital Object Identifier when citing or linking to this resource.
Abstract
Breast cancer is a major public health challenge. It affects a 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.
Item Type | Thesis |
---|---|
Thesis Type | Doctoral |
Thesis Name | PhD (research paper style) |
Contributors | Woods, Laura and Rachet, B |
Faculty and Department | Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Funder Name | Swiss Cancer League, Geneva Cancer Registry |
Grant number | BIL KFS-3274-08-2013 |
Copyright Holders | Robin Schaffar |
Download
Filename: 2018_EPH_PhD_Schaffar_R.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0
Download