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

Eloranta S, Lambert PC, Andersson TM, Czene K, Hall P, Björkholm M, Dickman PW - BMC Med Res Methodol (2012)

Bottom Line: 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.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, Sweden. sandra.eloranta@ki.se

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.

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Crude probabilities of death. Predicted crude probabilities of death and the relative contribution of the component parts to total mortality among women diagnosed with breast cancer in Sweden in 1992 at ages 55, 65 and 75 respectively.
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Figure 5: Crude probabilities of death. Predicted crude probabilities of death and the relative contribution of the component parts to total mortality among women diagnosed with breast cancer in Sweden in 1992 at ages 55, 65 and 75 respectively.

Mentions: The crude probabilities of death due to cancer were partitioned by applying equation 11 to each component part after having partitioned excess mortality into component parts. The predicted relative survival estimates and excess mortality rates from the second non-proportional hazards model, described last in the previous section, were used in the numerical integration. The top three graphs in figure5 show how the crude probabilities of death due to treatment-related CVD, breast cancer death (excluding the CVD deaths) and other causes respectively vary as a function of time since diagnosis for patients diagnosed in 1992 at ages 55, 65 and 75. The graphs clearly show how the risk of dying from any breast cancer related cause decreases as the patients are diagnosed at an older age whereas the risk of dying from causes other than the cancer increase with increasing age at diagnosis. For the two mutually exclusive sources of excess cancer mortality we see that the probability of death from excess CVD increased with age at diagnosis as opposed to the probability of death from the remaining cancer causes. This is due to treatment-related side effects being less common in younger ages when the patients are typically of better cardiac health.


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

Eloranta S, Lambert PC, Andersson TM, Czene K, Hall P, Björkholm M, Dickman PW - BMC Med Res Methodol (2012)

Crude probabilities of death. Predicted crude probabilities of death and the relative contribution of the component parts to total mortality among women diagnosed with breast cancer in Sweden in 1992 at ages 55, 65 and 75 respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3526518&req=5

Figure 5: Crude probabilities of death. Predicted crude probabilities of death and the relative contribution of the component parts to total mortality among women diagnosed with breast cancer in Sweden in 1992 at ages 55, 65 and 75 respectively.
Mentions: The crude probabilities of death due to cancer were partitioned by applying equation 11 to each component part after having partitioned excess mortality into component parts. The predicted relative survival estimates and excess mortality rates from the second non-proportional hazards model, described last in the previous section, were used in the numerical integration. The top three graphs in figure5 show how the crude probabilities of death due to treatment-related CVD, breast cancer death (excluding the CVD deaths) and other causes respectively vary as a function of time since diagnosis for patients diagnosed in 1992 at ages 55, 65 and 75. The graphs clearly show how the risk of dying from any breast cancer related cause decreases as the patients are diagnosed at an older age whereas the risk of dying from causes other than the cancer increase with increasing age at diagnosis. For the two mutually exclusive sources of excess cancer mortality we see that the probability of death from excess CVD increased with age at diagnosis as opposed to the probability of death from the remaining cancer causes. This is due to treatment-related side effects being less common in younger ages when the patients are typically of better cardiac health.

Bottom Line: 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.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, Sweden. sandra.eloranta@ki.se

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.

Show MeSH
Related in: MedlinePlus