Quantifying differences in breast cancer survival between England and Norway

Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, 2nd Floor Adrian Building, University of Leicester, University Road, Leicester LE1 7RH, UK.
Cancer epidemiology 05/2011; 35(6):526-33. DOI: 10.1016/j.canep.2011.04.003
Source: PubMed


Survival from breast cancer is lower in the UK than in some other European countries. We compared survival in England and Norway by age and time from diagnosis.
We included 303,648 English and 24,919 Norwegian cases of breast cancer diagnosed 1996-2004 using flexible parametric relative survival models, enabling improved quantification of differences in survival. Crude probabilities were estimated to partition the probability of death due to all causes into that due to cancer and other causes and to estimate the number of "avoidable" deaths.
England had lower relative survival for all ages with the difference increasing with age. Much of the difference was due to higher excess mortality in England in the first few months after diagnosis. Older patients had a higher proportion of deaths due to other causes. At 5 years post diagnosis, a woman aged 85 in England had probabilities of 0.35 of dying of cancer and 0.32 of dying of other causes, whilst in Norway they were 0.26 and 0.35. By eight years the number of "avoidable" all-cause deaths in England was 1020 with the number of "avoidable" breast cancer related deaths 1488.
Lower breast cancer survival in England is mainly due to higher mortality in the first year after diagnosis. Crude probabilities aid our understanding of the impact of disease on individual patients and help assess different treatment options.

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    • "In this paper we have grouped age into four categories for simplicity whilst illustrating the method. However, it may be preferable to model age continuously using regression splines as has been done in previous papers [37,38]. "
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    ABSTRACT: Background Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. Methods There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. Results An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. Conclusion A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies.
    BMC Medical Research Methodology 02/2013; 13(1):13. DOI:10.1186/1471-2288-13-13 · 2.27 Impact Factor
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    • "Hence, they were able to estimate reductions in breast cancer mortality [5,6]. Lambert et al. [7] estimated and partitioned the crude probability of all-cause mortality to the probabilities due to cancer and other causes. Crude probabilities can be used to understand the impact of disease on individual patients and help assess different treatment options. "
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    British Journal of Cancer 04/2012; 106(11):1846-9. DOI:10.1038/bjc.2012.169 · 4.84 Impact Factor
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