Limits...
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.

Show MeSH

Related in: MedlinePlus

Partitioning excess mortality. Predicted total and component-specific excess mortality rates (per 1,000 person-years) estimated from a proportional excess hazards model among women diagnosed with breast cancer in Sweden at ages 70-79 years and between the years 1978-1982.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3526518&req=5

Figure 1: Partitioning excess mortality. Predicted total and component-specific excess mortality rates (per 1,000 person-years) estimated from a proportional excess hazards model among women diagnosed with breast cancer in Sweden at ages 70-79 years and between the years 1978-1982.

Mentions: The left part of figure1 shows the overall excess mortality rate per 1,000 person-years for patients diagnosed at ages 70-79 years between the years 1978-1982 as a function of years since diagnosis whereas the right part of the graph shows the mortality rates for excess CVD deaths and the remaining excess deaths separately. In cancer patient survival the overall cancer mortality rate is typically highest within the first few years after diagnosis. This is also observed in figure1 although the pattern of the excess CVD mortality rate is somewhat different. In general, we expect to observe an increasing excess CVD mortality rate with increasing time since diagnosis. However, elderly women are more likely to be cardiologically fragile at the time of diagnosis which could potentially explain the high excess CVD mortality immediately following diagnosis. Because the main objective of this application is to study late adverse health effects of cardio-toxic therapies such as anthracyclines and radiotherapy we have chosen not to show any results for the first three months following the diagnosis of breast cancer.


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)

Partitioning excess mortality. Predicted total and component-specific excess mortality rates (per 1,000 person-years) estimated from a proportional excess hazards model among women diagnosed with breast cancer in Sweden at ages 70-79 years and between the years 1978-1982.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Partitioning excess mortality. Predicted total and component-specific excess mortality rates (per 1,000 person-years) estimated from a proportional excess hazards model among women diagnosed with breast cancer in Sweden at ages 70-79 years and between the years 1978-1982.
Mentions: The left part of figure1 shows the overall excess mortality rate per 1,000 person-years for patients diagnosed at ages 70-79 years between the years 1978-1982 as a function of years since diagnosis whereas the right part of the graph shows the mortality rates for excess CVD deaths and the remaining excess deaths separately. In cancer patient survival the overall cancer mortality rate is typically highest within the first few years after diagnosis. This is also observed in figure1 although the pattern of the excess CVD mortality rate is somewhat different. In general, we expect to observe an increasing excess CVD mortality rate with increasing time since diagnosis. However, elderly women are more likely to be cardiologically fragile at the time of diagnosis which could potentially explain the high excess CVD mortality immediately following diagnosis. Because the main objective of this application is to study late adverse health effects of cardio-toxic therapies such as anthracyclines and radiotherapy we have chosen not to show any results for the first three months following the diagnosis of breast cancer.

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