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.
"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
[Show abstract][Hide abstract] 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.
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.
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.
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
"Hence, they were able to estimate reductions in breast cancer mortality
[5,6]. Lambert et al.
 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. "
[Show abstract][Hide abstract] ABSTRACT: Background
Evaluating the cost-effectiveness of breast cancer screening requires estimates of the absolute risk of breast cancer, which is modified by various risk factors. Breast cancer incidence, and thus mortality, is altered by the occurrence of competing events. More accurate estimates of competing risks should improve the estimation of absolute risk of breast cancer and benefit from breast cancer screening, leading to more effective preventive, diagnostic, and treatment policies. We have previously described the effect of breast cancer risk factors on breast cancer incidence in the presence of competing risks. In this study, we investigate the association of the same risk factors with mortality as a competing event with breast cancer incidence.
We use data from the Canadian National Breast Screening Study, consisting of two randomized controlled trials, which included data on 39 risk factors for breast cancer. The participants were followed up for the incidence of breast cancer and mortality due to breast cancer and other causes. We stratified all-cause mortality into death from other types of cancer and death from non-cancer causes. We conducted separate analyses for cause-specific mortalities.
We found that “age at entry” is a significant factor for all-cause mortality, and cancer-specific and non-cancer mortality. “Menstruation length” and “number of live births” are significant factors for all-cause mortality, and cancer-specific mortality. “Ever noted lumps in right/left breasts” is a factor associated with all-cause mortality, and non-cancer mortality.
For proper estimation of absolute risk of the main event of interest common risk factors associated with competing events should be identified and considered.
Breast Cancer Research and Treatment 06/2012; 134(2):839-51. DOI:10.1007/s10549-012-2113-6 · 3.94 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Random error in the numbers of avoidable deaths among cancer patients has not been considered in earlier studies.
Methods to obtain valid confidence intervals (CIs) for numbers of avoidable deaths were developed. The excess mortality rates were estimated for patients diagnosed with colon cancer in five cancer control regions in Finland during 2000-2007 using a relative survival regression model. Numbers of avoidable deaths due to colon cancer and other causes, respectively, were estimated in different scenarios.
Altogether, 4139 and 1335 out of 10 772 patients under 90 years at diagnosis were estimated to have died due to colon cancer and other causes, respectively, during the first 5 years after diagnosis. If all the patients had shared the relative survival of the largest cancer control region to which the country capital belongs, the estimated number of avoidable deaths would have been 146 (95% CI 3-290).
Random error in numbers of avoidable deaths, often substantial, can be quantified by realistic error margins, based on appropriate statistical methods.
British Journal of Cancer 04/2012; 106(11):1846-9. DOI:10.1038/bjc.2012.169 · 4.84 Impact Factor
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