Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data
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© The Author(s). 2020 Published in BMC Public Health. This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/. Please refer to the licence to obtain terms for any further reuse or distribution of this work.
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BACKGROUND: Relative survival is the most common method used for measuring survival from population-based registries. However, the relative survival concept of 'survival as far as the cancer is concerned' can be biased due to differing non-cancer risk of death in the population with cancer (competing risks). Furthermore, while relative survival can be stratified or standardised, for example by sex or age, adjustment for a broad range of sociodemographic variables potentially influencing survival is not possible. In this paper we propose Fine and Gray competing risks multivariable regression as a method that can assess the probability of death from cancer, incorporating competing risks and adjusting for sociodemographic confounders.
METHODS: We used whole of population, person-level routinely linked Western Australian cancer registry and mortality data for individuals diagnosed from 1983 to 2011 for major cancer types combined, female breast, colorectal, prostate, lung and pancreatic cancers, and grade IV glioma. The probability of death from the index cancer (cancer death) was evaluated using Fine and Gray competing risks regression, adjusting for age, sex, Indigenous status, socio-economic status, accessibility to services, time sub-period and (for all cancers combined) cancer type. RESULTS: When comparing diagnoses in 2008-2011 to 1983-1987, we observed substantial decreases in the rate of cancer death for major cancer types combined (N = 192,641, - 31%), female breast (- 37%), prostate (- 76%) and colorectal cancers (- 37%). In contrast, improvements in pancreatic (- 15%) and lung cancers (- 9%), and grade IV glioma (- 24%) were less and the cumulative probability of cancer death for these cancer types remained high.
CONCLUSION: Considering the justifiable expectation for confounder adjustment in observational epidemiological studies, standard methods for tracking population-level changes in cancer survival are simplistic. This study demonstrates how competing risks and sociodemographic covariates can be incorporated using readily available software. While cancer has been focused on here, this technique has potential utility in survival analysis for other disease states.
