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Open Access Research article

Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models

Sandra Eloranta1*, Paul C Lambert12, Therese ML Andersson1, Kamila Czene1, Per Hall1, Magnus Björkholm3 and Paul W Dickman1

Author Affiliations

1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, , Box 281, Sweden

2 Center for Biostatistics and Epidemiology, Department of Health Sciences, University of Leicester, UK

3 Department of Medicine, Division of Hematology, Karolinska University Hospital Solna, Sweden

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BMC Medical Research Methodology 2012, 12:86  doi:10.1186/1471-2288-12-86

Published: 24 June 2012

Abstract

Background

Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality) on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects.

Methods

In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death.

Results

The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality.

Conclusions

The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is through adapting treatment strategies and follow-up of patients towards reducing the excess mortality caused by side effects of the treatment.

Keywords:
Survival analysis; Cancer; Relative survival; Regression models; Competing risks