Estimation of net survival for cancer patients: Relative survival setting more robust to some assumption violations than cause-specific setting, a sensitivity analysis on empirical data

Abstract

Net survival is the survival that would be observed if the only possible underlying cause of death was the disease under study. It can be estimated with either cause-specific or relative survival data settings, if the informative censoring is properly considered. However, net survival estimators are prone to specific biases related to the data setting itself. We examined which data setting was the most robust against violation of key assumptions (erroneous cause of death and inappropriate life tables). We identified 4285 women in the Geneva Cancer Registry, diagnosed with breast, colorectal, lung cancer and melanoma between 1981 and 1991 and estimated net survival up to 20 years using cause-specific and relative survival settings. We used weights to tackle informative censoring in both settings and performed sensitivity analyses to evaluate the impact of misclassification of cause of death in the cause-specific setting or of using inappropriate life tables on net survival estimates in the relative survival setting. For all the four cancers, net survival was highest when using the cause-specific setting and the absolute difference between the two estimators increased with time since diagnosis. The sensitivity analysis showed that (i) the use of different life tables did not compromise net survival estimation in the relative survival setting, whereas (ii) a small level of misclassification for the cause of death led to a large change in the net survival estimate in the cause-specific setting. The relative survival setting was more robust to the above assumptions violations and is therefore recommended for estimation of net survival.

Determinants of long‐term survival in a population‐based cohort study of patients with head and neck cancer from Scotland

“…Overall survival considers the risk of death by all causes, whereas disease‐specific survival methods only consider deaths by the disease of interest and ignores other causes of deaths that may have been related to or occurred as a secondary effect to the disease of interest . Net survival is defined as the excess mortality between the observed mortality of the patients under study and the expected mortality of the population—this provides a more accurate representation of the mortality from a particular disease of interest by disentangling other causes of death, and therefore only measuring causes of death that are related to the disease of interest via the use of excess mortality …”

“…17 Net survival is defined as the excess mortality between the observed mortality of the patients under study and the expected mortality of the population-this provides a more accurate representation of the mortality from a particular disease of interest by disentangling other causes of death, and therefore only measuring causes of death that are related to the disease of interest via the use of excess mortality. 18,19 The Scottish Audit of Head and Neck Cancer (SAHNC) is a population-based cohort which provides a unique opportunity to explore a wide range of factors in association with the long-term survival of HNC patients. This study aims to investigate HNC survival via the use of overall survival, disease-specific survival, and net survival analyses together to provide an in-depth and comprehensive picture of the survival of HNC patients.…”

Changes in conditional net survival and dynamic prognostic factors in patients with newly diagnosed metastatic prostate cancer initially treated with androgen deprivation therapy

“…Conditional survival analysis has been applied to assess the prognosis for a number of cancers, including metastatic cancers; however, only a small number of studies have investigated conditional survival for prostate cancer, especially metastatic prostate cancer . Net survival, which measures the survival that would be observed if the only possible cause of death was the disease of interest, provides the most appropriate method of estimating survival from cancer . The newly developed Pohar Perme estimator has been shown to provide unbiased net survival estimates that are more accurate than classical relative survival estimates .…”

“…15,16 Net survival, which measures the survival that would be observed if the only possible cause of death was the disease of interest, provides the most appropriate method of estimating survival from cancer. 17,18 The newly developed Pohar Perme estimator has been shown to provide unbiased net survival estimates that are more accurate than classical relative survival estimates. 19 However, there is little evidence regarding estimates of conditional survival using an unbiased Pohar Perme estimator in cancer populations.…”

Spatial barriers impact upon appropriate delivery of radiotherapy in breast cancer patients

“…Overall survival was calculated in the propensity score‐matched groups using the Kaplan–Meier method. As recommended for Register data analysis , Pohar Perme's estimator calculated net relative survival which provides a survival estimate that is analogous to disease‐specific survival.…”