Relative Survival Analysis in Cancer Epidemiology
Summary
Relative survival analysis quantifies the survival of cancer patients by comparing observed survival in a patient cohort with the expected survival of a comparable segment of the general population. This approach isolates the excess mortality attributable to cancer without requiring cause-of-death information. Net survival, a closely related concept, estimates the probability of survival if cancer were the only possible cause of death. Central to relative survival analysis is the construction of life tables to represent background mortality by age, sex and other demographic factors. Modern methodology has evolved to include non-parametric estimators, such as Pohar-Perme, model-based approaches employing flexible parametric survival models, and cure models that estimate the proportion of patients who experience no excess mortality beyond that of the general population. By standardising for age and other covariates, relative survival enables fair comparisons across different regions, time periods and patient subgroups. Its applications range from monitoring long-term trends in cancer outcomes to informing policy on resource allocation and evaluating the impact of screening, treatment advances and public health interventions on cancer prognosis.
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Relative Survival Analysis in Cancer Epidemiology publication trend
The graph below shows the total number of articles in relative survival analysis in cancer epidemiology across all publications each year (not limited to Nature Index journals).
Technical terms
Relative survival ratio: The ratio of observed survival in a patient group to expected survival in a matched general population, reflecting excess mortality due to disease.
Net survival: The probability of survival from cancer in the hypothetical situation where other causes of death are eliminated; estimated using relative survival methods.
Life tables: Age-, sex- and cohort-specific tables of background mortality rates used to calculate expected survival for relative survival analysis.
Flexible parametric survival models: Regression models that employ spline functions to model baseline hazard or log-cumulative hazard, allowing for smooth, data-driven hazard shapes and time-dependent effects.
Loss of life expectancy (LOLE): The difference in expected remaining years of life between cancer patients and the general population, quantifying the overall impact of cancer on life span.
References
- Number of life-years lost at the time of diagnosis and several years post-diagnosis in patients with solid malignancies: a population-based study in the Netherlands, 1989–2019. EClinicalMedicine (2023).
- Geographical, racial and socio-economic variation in life expectancy in the US and their impact on cancer relative survival. PLOS ONE (2018).
- Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Medical Research Methodology (2011).
- Nonparametric Relative Survival Analysis with the R Package relsurv. Journal of Statistical Software (2018).
- Errors in determination of net survival: cause-specific and relative survival settings. British Journal of Cancer (2020).
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