Cancer survival statistics for patients and healthcare professionals - a tutorial of real-world data analysis.

Monitoring survival of cancer patients using data collected by population-based cancer registries is an important component of cancer control. In this setting, patient survival is often summarized using net survival, that is survival from cancer if there were no other possible causes of death. Although net survival is the gold standard for comparing survival between groups or over time, it is less relevant for understanding the anticipated real-world prognosis of patients. In this review, we explain statistical concepts targeted towards patients, clinicians and healthcare professionals that summarize cancer patient survival under the assumption that other causes of death exist. Specifically, we explain the appropriate use, interpretation and assumptions behind statistical methods for competing risks, loss in life expectancy due to cancer and conditional survival. These concepts are relevant when producing statistics for risk communication between physicians and patients, planning for use of healthcare resources, or other applications when consideration of both cancer outcomes and the competing risks of death is required. To reinforce the concepts, we use Swedish population-based data of patients diagnosed with cancer of the breast, prostate, colon and chronic myeloid leukaemia. We conclude that when choosing between summary measures of survival it is critical to characterize the purpose of the study and to determine the nature of the hypothesis under investigation. The choice of terminology and style of reporting should be carefully adapted to the target audience and may range from summaries for specialist readers of scientific publications to interactive online tools aimed towards lay persons.

Incidence of myeloproliferative neoplasms - trends by subgroup and age in a population-based study in Sweden.

BACKGROUND: The reported incidence of Philadelphia-negative myeloproliferative neoplasms (MPNs) differs substantially between previous reports, likely due to true regional differences in incidence and/or variations in the quality and coverage of the cancer registers. OBJECTIVE: We therefore assessed MPN incidence in Sweden during recent years using prospectively collected information captured in Swedish health registers. METHODS: Patients with MPNs were identified through the Swedish Cancer Register and Swedish Blood Cancer Register between 2000 and 2014. Information on the Swedish population was obtained from the Human Mortality Database. Crude and age-standardized incidence rates of MPNs with 95% confidence intervals (CIs) were calculated. RESULTS: A total of 6281 MPN cases were reported to the Swedish Cancer Register and Swedish Blood Cancer Register during 2000-2014. The age-standardized, to the Swedish population in 2000, incidence for all MPNs was 4.45 (95% confidence interval [CI] 4.34-4.56)/100 000 person-years. The age-standardized incidence for polycythemia vera was 1.48 (1.42-1.54), for essential thrombocythemia 1.60 (1.53-1.66) and for primary myelofibrosis 0.52 (0.48-0.56)/100 000 person-years, respectively. The incidence rate of MPNs was substantially higher in the older compared to the younger age groups. The incidence increased during the study period, likely to do better reporting and increasing age of the general population. CONCLUSION: The reported MPN incidences in our study, which were in the higher interval of previously published studies, are likely more accurate compared to previous reports due to the population-based setting and high level of coverage in the Swedish Cancer and Blood Cancer Registers.

Loss in life expectancy and gain in life years as measures of cancer impact.

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.

Continued improvement in survival of acute myeloid leukemia patients: an application of the loss in expectation of life.

We evaluated temporal trends in survival of Swedish acute myeloid leukemia (AML) patients diagnosed between 1973 and 2011 using relative survival ratios (RSRs) and a measure called the loss in expectation of life (LEL). RSRs increased most for patients <60 years at diagnosis during the first calendar periods, but between 1997-2005 and 2006-2011 the most pronounced increase was for those aged 61-70 years at diagnosis; RSR changed from 0.16 (95% confidence interval (CI): 0.13-0.19) to 0.28 (95% CI: 0.23-0.33), respectively. The LEL for males aged 35 years at diagnosis was 41.0 (95% CI: 40.1-41.8) years in 1975 and 19.5 (95% CI: 16.4-22.5) years in 2011. For males aged 65 years, the corresponding figures were 13.8 (95% CI: 13.7-14.0) and 12.0 (95% CI: 11.3-12.8). Conditional LEL estimates suggested that patients who survive 5 years postdiagnosis have shorter remaining lifespan than the general population. The proportion of expected life lost (PELL) suggested that male 65-year-old patients lost 75% of their life expectancy in 2005 and 66% if they were diagnosed in 2011. Survival continued to increase to 2011, with larger improvements in those aged 61-70 years at diagnosis. The LEL and PELL are intuitive measures that may be useful in communicating survival statistics to patients, clinicians and health-care providers.

Understanding the impact of socioeconomic differences in breast cancer survival in England and Wales: avoidable deaths and potential gain in expectation of life.

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.

Estimating the proportion cured of cancer: some practical advice for users.

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.

Does socioeconomic status influence the prospect of cure from colon cancer--a population-based study in Sweden 1965-2000.

AIM OF STUDY: Differences in the survival of colon cancer patients by socioeconomic status have been demonstrated in several populations, but the underlying reasons for the differences are not well understood. By simultaneously estimating the proportion of patients cured from colon cancer and the survival times of the 'uncured' we hope to increase understanding of how socioeconomic status affects survival following a diagnosis of colon cancer. METHODS: We conducted a population-based cohort study of 58,873 patients diagnosed with colon cancer in Sweden 1965-2000. Socioeconomic status was classified based on occupation. We fitted mixture cure models and Poisson regression models adjusted for age, sex and calendar period. RESULTS: We observed higher excess mortality, lower proportion cured and shorter survival times among the uncured in patients from lower socioeconomic groups compared to the highest socioeconomic group. There was no evidence that the gap between the socioeconomic groups reduced over time. Farmers had the lowest odds of cure (odds ratio (OR) 0.85, 95% confidence interval (CI) 0.75-0.95) compared to higher non-manual workers followed by self-employed (0.91, 0.81-1.03), manual workers (0.93, 0.85-1.03) and lower non-manual workers (0.98, 0.89-1.08). CONCLUSION: Patients from lower socioeconomic groups in Sweden experience worse survival following a diagnosis of colon cancer. Differences exist in both the cure proportion and the survival time of the uncured, suggesting that socioeconomic differences cannot be attributed solely to lead time bias.Although this study has furthered our understanding of socioeconomic differences in survival, more detailed studies are required in order to identify, and subsequently remove, the underlying reasons for the differences.

Post-transfusion mortality among recipients of ABO-compatible but non-identical plasma.

BACKGROUND AND OBJECTIVES: The consequences of ABO-compatible non-identical plasma for patient outcome have not been studied in randomized clinical trials or large cohort studies and use varies widely in the absence of evidence-based policies. We investigated if transfusion with compatible instead of identical plasma confers any short-term survival disadvantage on the recipients. MATERIALS AND METHODS: The cohort of all 86 082 Swedish patients who received their first plasma transfusion between 1990 and 2002 was followed for 14 days and the risk of death in patients exposed to compatible non-identical plasma compared to recipients of only identical plasma. RESULTS: After adjustment for potential confounding factors, there was an increased mortality associated with exposure to ABO-compatible non-identical plasma, with the excess risk mostly confined to those receiving 5 or more units (relative risk, 1.15; 95% confidence interval, 1.02-1.29). Stratification by blood group indicated higher risks in group O recipients, especially when the compatible plasma was from a group AB donor. CONCLUSIONS: This study suggests that ABO-compatible non-identical plasma is less safe than identical plasma. Subanalyses by blood group suggest a role for circulating immune complexes. Our findings may have policy implications for improving transfusion safety.