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There are a broad range of survival-based metrics that are available to report from cancer survival studies, with varying advantages and disadvantages. A combination of metrics should be considered to improve comprehensibility and give a fuller understanding of the impact of cancer. In this article, we discuss the utility of loss in life expectancy and gain in life years as measures of cancer impact, and to quantify differences across population groups. These measures are simple to interpret, have a real-world meaning, and evaluate impact over a life-time horizon. We illustrate the use of the loss in life expectancy measures through a range of examples using data on women diagnosed with cancer in England. We use four different examples across a number of tumour types to illustrate different uses of the metrics, and highlight how they can be interpreted and used in practice in population-based oncology studies. Extensions of the measures conditional on survival to specific times after diagnosis can be used to give updated prognosis for cancer patients. Furthermore, we show how the measures can be used to understand the impact of population differences seen across patient groups. We believe that these under-used, and relatively easy to calculate, measures of overall impact can supplement reporting of cancer survival metrics and improve the comprehensibility compared to the metrics typically reported., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Socioeconomic differences in cancer patient survival are known to exist for women diagnosed with breast cancer. Standard metrics tend not to place great emphasis on evaluating the actual impact of these differences., Methods: We used two alternative, but related, methods of reporting the impact of socioeconomic differences for breast cancer patients in England and Wales. We calculated the average gain in life years for each patient should socioeconomic differences in relative survival be removed and show how this is related to the number of all-cause deaths that could be postponed by removing socioeconomic differences in cancer patient survival., Results: Our results indicate that deprivation differences for women with breast cancer exist and result in women from more deprived areas losing a larger proportion of their life due to a diagnosis of cancer. We also estimate that on average 1.1 years could be gained for a 60 year old breast cancer patient in the most deprived group by improving their relative survival to match the least deprived group. However, our results also show that deprivation differences in general survival have a large impact on life expectancy; showing that over two-thirds of the gap in differential life expectancy is explained by differences in other-cause survival., Conclusion: Socioeconomic differences in relative survival have an impact on life expectancy for patients and result in higher early mortality for more deprived patients. However, differences in general survival across socioeconomic groups explain a larger proportion of the deprivation gap in life expectancy for breast cancer patients., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Background: Cure models can provide improved possibilities for inference if used appropriately, but there is potential for misleading results if care is not taken. In this study, we compared five commonly used approaches for modelling cure in a relative survival framework and provide some practical advice on the use of these approaches., Patients and Methods: Data for colon, female breast, and ovarian cancers were used to illustrate these approaches. The proportion cured was estimated for each of these three cancers within each of three age groups. We then graphically assessed the assumption of cure and the model fit, by comparing the predicted relative survival from the cure models to empirical life table estimates., Results: Where both cure and distributional assumptions are appropriate (e.g., for colon or ovarian cancer patients aged <75 years), all five approaches led to similar estimates of the proportion cured. The estimates varied slightly when cure was a reasonable assumption but the distributional assumption was not (e.g., for colon cancer patients ≥75 years). Greater variability in the estimates was observed when the cure assumption was not supported by the data (breast cancer)., Conclusions: If the data suggest cure is not a reasonable assumption then we advise against fitting cure models. In the scenarios where cure was reasonable, we found that flexible parametric cure models performed at least as well, or better, than the other modelling approaches. We recommend that, regardless of the model used, the underlying assumptions for cure and model fit should always be graphically assessed., (Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.)