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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.

Summary: Kidney transplant recipients (KTRs) have an increased cancer risk compared to the general population, but absolute risks that better reflect the clinical impact of cancer are seldom estimated. All KTRs in Sweden, Norway, Denmark, and Finland, with a first transplantation between 1995 and 2011, were identified through national registries. Post‐transplantation cancer occurrence was assessed through linkage with cancer registries. We estimated standardized incidence ratios (SIR), absolute excess risks (AER), and cumulative incidence of cancer in the presence of competing risks. Overall, 12 984 KTRs developed 2215 cancers. The incidence rate of cancer overall was threefold increased (SIR 3.3, 95% confidence interval [CI]: 3.2–3.4). The AER of any cancer was 1560 cases (95% CI: 1468–1656) per 100 000 person‐years. The highest AERs were observed for nonmelanoma skin cancer (838, 95% CI: 778–901), non‐Hodgkin lymphoma (145, 95% CI: 119–174), lung cancer (126, 95% CI: 98.2–149), and kidney cancer (122, 95% CI: 98.0–149). The five‐ and ten‐year cumulative incidence of any cancer was 8.1% (95% CI: 7.6–8.6%) and 16.8% (95% CI: 16.0–17.6%), respectively. Excess cancer risks were observed among Nordic KTRs for a wide range of cancers. Overall, 1 in 6 patients developed cancer within ten years, supporting extensive post‐transplantation cancer vigilance. [ABSTRACT FROM AUTHOR]

Solid organ transplant recipients (OTRs) have an increased cancer risk but their survival once diagnosed with cancer has seldom been assessed. We therefore investigated cancer‐specific survival among OTRs with a wide range of cancer forms nationally in Sweden. The study included 2,143 OTRs with cancer, and 946,089 nontransplanted cancer patients diagnosed 1992–2013. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox regression models adjusted for age, sex and calendar year. Median follow‐up was 3.1 (range 0–22) years. Overall, OTRs diagnosed with any cancer had a 35% higher rate of cancer death compared to nontransplanted cancer patients (HR: 1.35, 95% CI: 1.24–1.47). Specifically, higher rates of cancer‐specific death were observed among OTRs diagnosed with Hodgkin lymphoma (HR: 15.0, 95% CI: 5.56–40.6), high‐grade non‐Hodgkin lymphoma (HR: 2.68, 95% CI: 1.90–3.77), malignant melanoma (HR: 2.80, 95% CI: 1.74–4.52) and urothelial (HR: 2.56, 95% CI: 1.65–3.97), breast (HR: 2.12, 95% CI: 1.38–3.25), head/neck (HR: 1.55, 95% CI: 1.02–2.36) and colorectal (HR: 1.42, 95% CI: 1.07–1.88) cancer. The worse outcomes were not explained by differences in distribution of cancer stage or histologic subtypes. For other common cancer forms such as prostate, lung and kidney cancer, the prognosis was similar to that in nontransplanted cancer patients. In conclusion, several but not all types of posttransplantation cancer diagnoses are associated with worse outcomes than in the general population. Reasons for this should be further explored to optimize posttransplantation cancer management. What's new? While cancer risk increases significantly following solid organ transplant, little is known about cancer‐specific survival in transplant recipients. Here, analyses of survival among post‐transplant and non‐transplanted cancer patients in Sweden reveal significantly increased rates of death among transplant recipients diagnosed with certain cancer types, including Hodgkin lymphoma and melanoma and cancers of the breast and colorectal tract. Differences in stage or histologic subtype did not account for worse prognosis of these cancers. Common cancers, such as prostate and kidney cancer, had similar prognosis in the two groups. The findings could have implications for post‐transplantation cancer management and screening. [ABSTRACT FROM AUTHOR]

The international prognostic index (IPI) and similar models form the cornerstone of clinical assessment in newly diagnosed diffuse large B- cell lymphoma (DLBCL). While being simple and convenient to use, their inadequate use of the available clinical data is a major weakness. In this study, we compared performance of the International Prognostic Index (IPI) and its variations (RIPI and NCCN-IPI) to a Cox proportional hazards (CPH) model using the same covariates in nondichotomized form. All models were tested in 4863 newly diagnosed DLBCL patients from population- based Nordic registers. The CPH model led to a substantial increase in predictive accuracy as compared to conventional prognostic scores when evaluated by the area under the curve and other relevant tests. Furthermore, the generation of patient- specific survival curves rather than assigning patients to one of few predefined risk groups is a relevant step toward personalized management and treatment. A test- version is available on lymphomapredictor.org. [ABSTRACT FROM AUTHOR]

Cancer incidence, survival and mortality are essential population-based indicators for public health and cancer control. Confusion and misunderstanding still surround the estimation and interpretation of these indicators. Recurring controversies over the use and misuse of population-based cancer statistics in health policy suggests the need for further clarification. In our article, we describe the concepts that underlie the measures of incidence, survival and mortality, and illustrate the synergy between these measures of the cancer burden. We demonstrate the relationships between trends in incidence, survival and mortality, using real data for cancers of the lung and breast from England and Sweden. Finally, we discuss the importance of using all three measures in combination when interpreting overall progress in cancer control, and we offer some recommendations for their use. [ABSTRACT FROM AUTHOR]

A useful summary measure for survival data is the expectation of life, which is calculated by obtaining the area under a survival curve. The loss in expectation of life due to a certain type of cancer is the difference between the expectation of life in the general population and the expectation of life among the cancer patients. This measure is used little in practice as its estimation generally requires extrapolation of both the expected and observed survival. A parametric distribution can be used for extrapolation of the observed survival, but it is difficult to find a distribution that captures the underlying shape of the survival function after the end of follow-up. In this paper, we base our extrapolation on relative survival, because it is more stable and reliable. Relative survival is defined as the observed survival divided by the expected survival, and the mortality analogue is excess mortality. Approaches have been suggested for extrapolation of relative survival within life-table data, by assuming that the excess mortality has reached zero (statistical cure) or has stabilized to a constant. We propose the use of flexible parametric survival models for relative survival, which enables estimating the loss in expectation of life on individual level data by making these assumptions or by extrapolating the estimated linear trend at the end of follow-up. We have evaluated the extrapolation from this model using data on four types of cancer, and the results agree well with observed data. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]