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

BMC Medical Research Methodology

Paul W Dickman³  Magnus Björkholm²  Per Hall³  Kamila Czene³  Therese ML Andersson³  Paul C Lambert¹  Sandra Eloranta³ 

[1] Center for Biostatistics and Epidemiology, Department of Health Sciences, University of Leicester, UK;Department of Medicine, Division of Hematology, Karolinska University Hospital Solna, Sweden;Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, , Box 281, Sweden

关键词: Competing risks;    Regression models;    Relative survival;    Cancer;    Survival analysis;   

Others  :  1136601
DOI  :  10.1186/1471-2288-12-86

 received in 2011-08-18, accepted in 2012-06-24,  发布年份 2012

【 摘 要 】

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.

【 授权许可】

   
2012 Eloranta et al.; licensee BioMed Central Ltd.

【 图 表 】

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