Have the recent advancements in cancer therapy and survival benefitted patients of all age groups across the Nordic countries? NORDCAN survival analyses 2002-2021.

BACKGROUND: Since the early 2000s, overall and site-specific cancer survival have improved substantially in the Nordic countries. We evaluated whether the improvements have been similar across countries, major cancer types, and age groups. MATERIAL AND METHODS: Using population-based data from the five Nordic cancer registries recorded in the NORDCAN database, we included a cohort of 1,525,854 men and 1,378,470 women diagnosed with cancer (except non-melanoma skin cancer) during 2002-2021, and followed for death until 2021. We estimated 5-year relative survival (RS) in 5-year calendar periods, and percentage points (pp) differences in 5-year RS from 2002-2006 until 2017-2021. Separate analyses were performed for eight cancer sites (i.e. colorectum, pancreas, lung, breast, cervix uteri, kidney, prostate, and melanoma of skin). RESULTS: Five-year RS improved across nearly all cancer sites in all countries (except Iceland), with absolute differences across age groups ranging from 1 to 21 pp (all cancer sites), 2 to 20 pp (colorectum), -1 to 36 pp (pancreas), 2 to 28 pp (lung), 0 to 9 pp (breast), -11 to 26 pp (cervix uteri), 2 to 44 pp (kidney), -2 to 23 pp (prostate) and -3 to 30 pp (skin melanoma). The oldest patients (80-89 years) exhibited lower survival across all countries and sites, although with varying improvements over time. INTERPRETATION: Nordic cancer patients have generally experienced substantial improvements in cancer survival during the last two decades, including major cancer sites and age groups. Although survival has improved over time, older patients remain at a lower cancer survival compared to younger patients.

Loss in life expectancy in patients with stage II-III cutaneous melanoma in Sweden: A population-based cohort study.

BACKGROUND: Survival in cutaneous melanoma (CM) is heterogeneous. Loss in life expectancy (LLE) measures impact of CM on remaining lifespan compared to general population. OBJECTIVES: Investigating LLE in operated stage II-III CM patients. METHODS: Data from 8061 patients (aged 40-80 years) with stage II-III CM in Sweden, diagnosed between 2005 and 2018, were analyzed (Swedish Melanoma Registry). A flexible parametric survival model estimated life expectancy and LLE. RESULTS: Based on 2018 diagnoses, stage II and III CM patients lost 2209 and 1902 life years, respectively. LLE was higher in stage III: 5.2 versus 10.9 years (stage II vs III 60-year-old females). Younger patients had higher LLE: 10.7 versus 3.9 years (stage II CM in 40 vs 70-year-old males). In stage II, females had lower LLE than males; 50-year-old females and males stage II CM had LLE equal to 7.3 and 8.3 years, respectively. LLE increased with higher substages, stage IIB resembling IIIB and IIC resembling IIIC-D. LIMITATIONS: Extrapolation was used to estimate LLE. Varying stage group sizes require caution. CONCLUSIONS: Our results are both clinically relevant and easy-to-interpret measures of the impact of CM on survival, but the results also summarize the prognosis over the lifetime of a CM patient.

Improving communication of cancer survival statistics-feasibility of implementing model-based algorithms in routine publications.

BACKGROUND: Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics. METHODS: We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients. RESULTS: For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites. DISCUSSION: It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients.

Association of metformin use and survival in patients with cutaneous melanoma and diabetes.

BACKGROUND: Metformin use has been associated with improved survival in patients with different types of cancer, but research regarding the effect of metformin on cutaneous melanoma (CM) survival is sparse and inconclusive. OBJECTIVES: To investigate the association between metformin use and survival among patients with CM and diabetes. METHODS: All adult patients with a primary invasive CM between 2007 and 2014 were identified in the Swedish Melanoma Registry and followed until death, or end of follow-up on 31 December 2017 in this population-based cohort study. Patients with both CM and type 2 diabetes mellitus were assessed further. Overall survival (OS) and melanoma-specific survival (MSS) were the primary endpoints. Cox proportional hazard models estimating crude and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were used comparing peridiagnostic use vs. nonuse of metformin. Dose response was evaluated based on defined daily doses. RESULTS: Among a total of 23 507 patients, 1162 patients with CM and type 2 diabetes mellitus were included in the final cohort, with a median follow-up time of 4.1 years (interquartile range 2.4-6.1). Peridiagnostic metformin use was associated with a significantly decreased risk of death by any cause (HR 0.68, 95% CI 0.57-0.81). Cumulative pre- and postdiagnostic metformin use was also associated with improved OS: the HR for prediagnostic use was 0.90 (95% CI 0.86-0.95) for every 6 months of use and the HR for postdiagnostic use ranged from 0.98 (95% CI 0.97-0.98) for 0-6 months to 0.59 (0.49-0.70) for 24-30 months of use. No association was found for metformin use and MSS. CONCLUSIONS: Metformin use was associated with improved OS in patients with CM and diabetes regardless of timing (pre-, post- or peridiagnostic use) and followed a dose-response pattern. However, further research regarding the underlying mechanisms is warranted.

Including uncertainty of the expected mortality rates in the prediction of loss in life expectancy.

PURPOSE: This study introduces a novel method for estimating the variance of life expectancy since diagnosis (LEC) and loss in life expectancy (LLE) for cancer patients within a relative survival framework in situations where life tables based on the entire general population are not accessible. LEC and LLE are useful summary measures of survival in population-based cancer studies, but require information on the mortality in the general population. Our method addresses the challenge of incorporating the uncertainty of expected mortality rates when using a sample from the general population. METHODS: To illustrate the approach, we estimated LEC and LLE for patients diagnosed with colon and breast cancer in Sweden. General population mortality rates were based on a random sample drawn from comparators of a matched cohort. Flexible parametric survival models were used to model the mortality among cancer patients and the mortality in the random sample from the general population. Based on the models, LEC and LLE together with their variances were estimated. The results were compared with those obtained using fixed expected mortality rates. RESULTS: By accounting for the uncertainty of expected mortality rates, the proposed method ensures more accurate estimates of variances and, therefore, confidence intervals of LEC and LLE for cancer patients. This is particularly valuable for older patients and some cancer types, where underestimation of the variance can be substantial when the entire general population data are not accessible. CONCLUSION: The method can be implemented using existing software, making it accessible for use in various cancer studies. The provided example of Stata code further facilitates its adoption.

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group.

BACKGROUND: Multi-state models are used to study several clinically meaningful research questions. Depending on the research question of interest and the information contained in the data, different multi-state structures and modelling choices can be applied. We aim to explore different research questions using a series of multi-state models of increasing complexity when studying repeated prescriptions data, while also evaluating different modelling choices. METHODS: We develop a series of research questions regarding the probability of being under antidepressant medication across time using multi-state models, among Swedish women diagnosed with breast cancer (n = 18,313) and an age-matched population comparison group of cancer-free women (n = 92,454) using a register-based database (Breast Cancer Data Base Sweden 2.0). Research questions were formulated ranging from simple to more composite ones. Depending on the research question, multi-state models were built with structures ranging from simpler ones, like single-event survival analysis and competing risks, up to complex bidirectional and recurrent multi-state structures that take into account the recurring start and stop of medication. We also investigate modelling choices, such as choosing a time-scale for the transition rates and borrowing information across transitions. RESULTS: Each structure has its own utility and answers a specific research question. However, the more complex structures (bidirectional, recurrent) enable accounting for the intermittent nature of prescribed medication data. These structures deliver estimates of the probability of being under medication and total time spent under medication over the follow-up period. Sensitivity analyses over different definitions of the medication cycle and different choices of timescale when modelling the transition intensity rates show that the estimates of total probabilities of being in a medication cycle over follow-up derived from the complex structures are quite stable. CONCLUSIONS: Each research question requires the definition of an appropriate multi-state structure, with more composite ones requiring such an increase in the complexity of the multi-state structure. When a research question is related with an outcome of interest that repeatedly changes over time, such as the medication status based on prescribed medication, the use of novel multi-state models of adequate complexity coupled with sensible modelling choices can successfully address composite, more realistic research questions.

Seasonal effects on cancer incidence and prognosis.

BACKGROUND: It is unknown if the reduction in the expected number of cancer cases diagnosed during Swedish holidays are due to diagnostic delays, how different cancers are affected, and if the season of diagnosis influences long-term cancer survival. We aimed to quantify seasonal trends in incidence and excess mortality for a wide range of malignancies, requiring more or less urgent clinical management. MATERIAL AND METHODS: This nationwide cohort study included all Swedish residents aged 20-84 in 1990-2019. Incidence and relative survival in pancreatic, colorectal, lung, urothelial, breast, and prostate cancer, together with malignant melanoma, non-Hodgkin lymphoma, and acute leukemia diagnosed during holiday and post-holiday were compared to working (reference) season. Incidence rate ratios (IRR) were estimated using Poisson regression and excess (cancer) mortality rate ratios using flexible parametric models. RESULTS: We identified 882,980 cancer cases. Incidence declined during holiday season for all malignancies and the IRR ranged from 0.58 (95% CI 0.57-0.59 in breast to 0.92 (95% CI 0.89-0.94) in pancreatic cancer. A post-holiday increase was noted for acute leukemia, pancreatic, and lung cancer. For all malignancies except lung cancer, non-Hodgkin lymphoma, and acute leukemia, the excess mortality at 2 years from diagnosis was higher among those diagnosed during the holiday season. A tendency toward elevated short-term (0.5 years) excess mortality was noted in the post-holiday group, but long-term effects only persisted in breast cancer. CONCLUSION: This study demonstrates lower holiday detection rates and higher mortality rates in various cancer types diagnosed during holiday season. Healthcare systems should offer a uniform level of cancer care independent of calendar season.

Assessing lead time bias due to mammography screening on estimates of loss in life expectancy.

BACKGROUND: An increasingly popular measure for summarising cancer prognosis is the loss in life expectancy (LLE), i.e. the reduction in life expectancy following a cancer diagnosis. The proportion of life lost (PLL) can also be derived, improving comparability across age groups as LLE is highly age-dependent. LLE and PLL are often used to assess the impact of cancer over the remaining lifespan and across groups (e.g. socioeconomic groups). However, in the presence of screening, it is unclear whether part of the differences across population groups could be attributed to lead time bias. Lead time is the extra time added due to early diagnosis, that is, the time from tumour detection through screening to the time that cancer would have been diagnosed symptomatically. It leads to artificially inflated survival estimates even when there are no real survival improvements. METHODS: In this paper, we used a simulation-based approach to assess the impact of lead time due to mammography screening on the estimation of LLE and PLL in breast cancer patients. A natural history model developed in a Swedish setting was used to simulate the growth of breast cancer tumours and age at symptomatic detection. Then, a screening programme similar to current guidelines in Sweden was imposed, with individuals aged 40-74 invited to participate every second year; different scenarios were considered for screening sensitivity and attendance. To isolate the lead time bias of screening, we assumed that screening does not affect the actual time of death. Finally, estimates of LLE and PLL were obtained in the absence and presence of screening, and their difference was used to derive the lead time bias. RESULTS: The largest absolute bias for LLE was 0.61 years for a high screening sensitivity scenario and assuming perfect screening attendance. The absolute bias was reduced to 0.46 years when the perfect attendance assumption was relaxed to allow for imperfect attendance across screening visits. Bias was also present for the PLL estimates. CONCLUSIONS: The results of the analysis suggested that lead time bias influences LLE and PLL metrics, thus requiring special consideration when interpreting comparisons across calendar time or population groups.

Obtaining long-term stage-specific relative survival estimates in the presence of incomplete historical stage information.

BACKGROUND: Completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries, making it challenging to provide long-term stage-specific survival estimates. Stage-specific survival differences are driven by differences in short-term prognosis, meaning estimated survival metrics using period analysis are unlikely to be sensitive to imputed historical stage data. METHODS: We used data from the Surveillance, Epidemiology, and End Results (SEER) Program for lung, colon and breast cancer. To represent missing data patterns in less complete registry data, we artificially inflated the proportion of missing stage information conditional on stage at diagnosis and calendar year of diagnosis. Period analysis was applied and missing stage at diagnosis information was imputed under four different conditions to emulate extreme imputed stage distributions. RESULTS: We fit a flexible parametric model for each cancer stage on the excess hazard scale and the differences in stage-specific marginal relative survival were assessed. Estimates were also obtained from non-parametric approaches for validation. There was little difference between the 10-year stage-specific marginal relative survival estimates, regardless of the assumed historical stage distribution. CONCLUSIONS: When conducting a period analysis, multiple imputation can be used to obtain stage-specific long-term estimates of relative survival, even when the historical stage information is largely incomplete.

Standardised survival probabilities: a useful and informative tool for reporting regression models for survival data.

BACKGROUND: When interested in studying the effect of a treatment (or other exposure) on a time-to-event outcome, the most popular approach is to estimate survival probabilities using the Kaplan-Meier estimator. In the presence of confounding, regression models are fitted, and results are often summarised as hazard ratios. However, despite their broad use, hazard ratios are frequently misinterpreted as relative risks instead of relative rates. METHODS: We discuss measures for summarising the analysis from a regression model that overcome some of the limitations associated with hazard ratios. Such measures are the standardised survival probabilities for treated and untreated: survival probabilities if everyone in the population received treatment and if everyone did not. The difference between treatment arms can be calculated to provide a measure for the treatment effect. RESULTS: Using publicly available data on breast cancer, we demonstrated the usefulness of standardised survival probabilities for comparing the experience between treated and untreated after adjusting for confounding. We also showed that additional important research questions can be addressed by standardising among subgroups of the total population. DISCUSSION: Standardised survival probabilities are a useful way to report the treatment effect while adjusting for confounding and have an informative interpretation in terms of risk.

Five ways to improve international comparisons of cancer survival: lessons learned from ICBP SURVMARK-2.

BACKGROUND: Comparisons of population-based cancer survival between countries are important to benchmark the overall effectiveness of cancer management. The International Cancer Benchmarking Partnership (ICBP) Survmark-2 study aims to compare survival in seven high-income countries across eight cancer sites and explore reasons for the observed differences. A critical aspect in ensuring comparability in the reported survival estimates are similarities in practice across cancer registries. While ICBP Survmark-2 has shown these differences are unlikely to explain the observed differences in cancer-specific survival between countries, it is important to keep in mind potential biases linked to registry practice and understand their likely impact. METHODS: Based on experiences gained within ICBP Survmark-2, we have developed a set of recommendations that seek to optimally harmonise cancer registry datasets to improve future benchmarking exercises. RESULTS: Our recommendations stem from considering the impact on cancer survival estimates in five key areas: (1) the completeness of the registry and the availability of registration sources; (2) the inclusion of death certification as a source of identifying cases; (3) the specification of the date of incidence; (4) the approach to handling multiple primary tumours and (5) the quality of linkage of cases to the deaths register. CONCLUSION: These recommendations seek to improve comparability whilst maintaining the opportunity to understand and act upon international variations in outcomes among cancer patients.

Assessing the impact of including variation in general population mortality on standard errors of relative survival and loss in life expectancy.

BACKGROUND: A relative survival approach is often used in population-based cancer studies, where other cause (or expected) mortality is assumed to be the same as the mortality in the general population, given a specific covariate pattern. The population mortality is assumed to be known (fixed), i.e. measured without uncertainty. This could have implications for the estimated standard errors (SE) of any measures obtained within a relative survival framework, such as relative survival (RS) ratios and the loss in life expectancy (LLE). We evaluated the existing approach to estimate SE of RS and the LLE in comparison to if uncertainty in the population mortality was taken into account. METHODS: The uncertainty from the population mortality was incorporated using parametric bootstrap approach. The analysis was performed with different levels of stratification and sizes of the general population used for creating expected mortality rates. Using these expected mortality rates, SEs of 5-year RS and the LLE for colon cancer patients in Sweden were estimated. RESULTS: Ignoring uncertainty in the general population mortality rates had negligible (less than 1%) impact on the SEs of 5-year RS and LLE, when the expected mortality rates were based on the whole general population, i.e. all people living in a country or region. However, the smaller population used for creating the expected mortality rates, the larger impact. For a general population reduced to 0.05% of the original size and stratified by age, sex, year and region, the relative precision for 5-year RS was 41% for males diagnosed at age 85. For the LLE the impact was more substantial with a relative precision of 1286%. The relative precision for marginal estimates of 5-year RS was 3% and 30% and for the LLE 22% and 313% when the general population was reduced to 0.5% and 0.05% of the original size, respectively. CONCLUSIONS: When the general population mortality rates are based on the whole population, the uncertainty in the estimates of the expected measures can be ignored. However, when based on a smaller population, this uncertainty should be taken into account, otherwise SEs may be too small, particularly for marginal values, and, therefore, confidence intervals too narrow.

Non-parametric estimation of reference adjusted, standardised probabilities of all-cause death and death due to cancer for population group comparisons.

BACKGROUND: Ensuring fair comparisons of cancer survival statistics across population groups requires careful consideration of differential competing mortality due to other causes, and adjusting for imbalances over groups in other prognostic covariates (e.g. age). This has typically been achieved using comparisons of age-standardised net survival, with age standardisation addressing covariate imbalance, and the net estimates removing differences in competing mortality from other causes. However, these estimates lack ease of interpretability. In this paper, we motivate an alternative non-parametric approach that uses a common rate of other cause mortality across groups to give reference-adjusted estimates of the all-cause and cause-specific crude probability of death in contrast to solely reporting net survival estimates. METHODS: We develop the methodology for a non-parametric equivalent of standardised and reference adjusted crude probabilities of death, building on the estimation of non-parametric crude probabilities of death. We illustrate the approach using regional comparisons of survival following a diagnosis of rectal cancer for men in England. We standardise to the covariate distribution and other cause mortality of England as a whole to offer comparability, but with close approximation to the observed all-cause region-specific mortality. RESULTS: The approach gives comparable estimates to observed crude probabilities of death, but allows direct comparison across population groups with different covariate profiles and competing mortality patterns. In our illustrative example, we show that regional variations in survival following a diagnosis of rectal cancer persist even after accounting for the variation in deprivation, age at diagnosis and other cause mortality. CONCLUSIONS: The methodological approach of using standardised and reference adjusted metrics offers an appealing approach for future cancer survival comparison studies and routinely published cancer statistics. Our non-parametric estimation approach through the use of weighting offers the ability to estimate comparable survival estimates without the need for statistical modelling.

Modelling multiple time-scales with flexible parametric survival models.

BACKGROUND: There are situations when we need to model multiple time-scales in survival analysis. A usual approach in this setting would involve fitting Cox or Poisson models to a time-split dataset. However, this leads to large datasets and can be computationally intensive when model fitting, especially if interest lies in displaying how the estimated hazard rate or survival change along multiple time-scales continuously. METHODS: We propose to use flexible parametric survival models on the log hazard scale as an alternative method when modelling data with multiple time-scales. By choosing one of the time-scales as reference, and rewriting other time-scales as a function of this reference time-scale, users can avoid time-splitting of the data. RESULT: Through case-studies we demonstrate the usefulness of this method and provide examples of graphical representations of estimated hazard rates and survival proportions. The model gives nearly identical results to using a Poisson model, without requiring time-splitting. CONCLUSION: Flexible parametric survival models are a powerful tool for modelling multiple time-scales. This method does not require splitting the data into small time-intervals, and therefore saves time, helps avoid technological limitations and reduces room for error.

Cardiovascular, bone, and metabolic health in men with castrate-resistant prostate cancer treated with androgen deprivation: a matched cohort study.

BACKGROUND: Descriptive data on late effects associated with castrate-resistant prostate cancer (CRPC) are sparse. We aimed to define the timing and incidence of cardiovascular disease (CVD), fractures, and diabetes in a patient population with CRPC. METHODS: In the population-based STHLM0 cohort 1464 men with CRPC were identified and matched with three men free from prostate cancer (PC) in the Stockholm region of Sweden. Kaplan-Meier estimates of net survival were used to describe time to CVD, fracture, and diabetes. Cox regression was used to compare incidence rates (IRRs) for the respective late effects. Cumulative incidence analyses of late effects in the presence of the competing risk of death were performed to estimate absolute risks. RESULTS: The Kaplan Meier estimates demonstrated a higher net probability for CVD, fracture, and diabetes among men diagnosed with CRPC compared to the matched comparators. The IRRs were 1.94 (95% CI: 1.79-2.12) for CVD, 2.08 (95% CI: 1.70-2.53) for fracture, and 2.00 (95% CI: 1.31-3.05) for diabetes, respectively, comparing men diagnosed with CRPC to men free from PC. The cumulative incidence of CVD at 12 months of follow-up was higher in men diagnosed with CRPC compared to healthy controls regardless of age with a difference in cumulative incidence being 0.20 for men aged <65 and 0.11 for men aged >84. CONCLUSIONS: In this cohort, the incidence of CVD was significantly higher among men with CRPC compared to healthy controls. Despite having this end-stage disease this finding proves that clinicians must recognize this late effect in men diagnosed with CRPC to improve preventive actions. These men did not have a higher absolute risk of fractures and diabetes after accounting for deaths due to any cause compared to healthy controls.

Age-specific survival trends and life-years lost in women with breast cancer 1990-2016: the NORDCAN survival studies.

BACKGROUND: A recent overview of cancer survival trends 1990-2016 in the Nordic countries reported continued improvements in age-standardized breast cancer survival among women. The aim was to estimate age-specific survival trends over calendar time, including life-years lost, to evaluate if improvements have benefited patients across all ages in the Nordic countries. METHODS: Data on breast cancers diagnosed 1990-2016 in Denmark, Finland, Iceland, Norway, and Sweden were obtained from the NORDCAN database. Age-standardized and age-specific relative survival (RS) was estimated using flexible parametric models, as was reference-adjusted crude probabilities of death and life-years lost. RESULTS: Age-standardized period estimates of 5-year RS in women diagnosed with breast cancer ranged from 87% to 90% and 10-year RS from 74% to 85%. Ten-year RS increased with 15-18 percentage points from 1990 to 2016, except in Sweden (+9 percentage points) which had the highest survival in 1990. The largest improvements were observed in Denmark, where a previous survival disadvantage diminished. Most recent 5-year crude probabilities of cancer death ranged from 9% (Finland, Sweden) to 12% (Denmark, Iceland), and life-years lost from 3.3 years (Finland) to 4.6 years (Denmark). Although survival improvements were consistent across different ages, women aged ≥70 years had the lowest RS in all countries. Period estimates of 5-year RS were 94-95% in age 55 years and 84-89% in age 75 years, while 10-year RS were 88-91% in age 55 years and 69-84% in age 75 years. Women aged 40 years lost on average 11.0-13.8 years, while women lost 3.8-6.0 years if aged 55 and 1.9-3.5 years if aged 75 years. CONCLUSIONS: Survival for Nordic women with breast cancer improved from 1990 to 2016 in all age groups, albeit with larger country variation among older women where survival was also lower. Women over 70 years of age have not had the same survival improvement as women of younger age.

On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models.

In competing risks settings where the events are death due to cancer and death due to other causes, it is common practice to use time since diagnosis as the timescale for all competing events. However, attained age has been proposed as a more natural choice of timescale for modeling other cause mortality. We examine the choice of using time since diagnosis versus attained age as the timescale when modeling other cause mortality, assuming that the hazard rate is a function of attained age, and how this choice can influence the cumulative incidence functions ( C I F $CIF$ s) derived using flexible parametric survival models. An initial analysis on the colon cancer data from the population-based Swedish Cancer Register indicates such an influence. A simulation study is conducted in order to assess the impact of the choice of timescale for other cause mortality on the bias of the estimated C I F s $CIFs$ and how different factors may influence the bias. We also use regression standardization methods in order to obtain marginal C I F $CIF$ estimates. Using time since diagnosis as the timescale for all competing events leads to a low degree of bias in C I F $CIF$ for cancer mortality ( C I F 1 $CIF_{1}$ ) under all approaches. It also leads to a low degree of bias in C I F $CIF$ for other cause mortality ( C I F 2 $CIF_{2}$ ), provided that the effect of age at diagnosis is included in the model with sufficient flexibility, with higher bias under scenarios where a covariate has a time-varying effect on the hazard rate for other cause mortality on the attained age scale.

Self-reported arm and shoulder problems in breast cancer survivors in Sub-Saharan Africa: the African Breast Cancer-Disparities in Outcomes cohort study.

BACKGROUND: Arm and shoulder problems (ASP), including lymphedema, were common among women with breast cancer in high-income countries before sentinel lymph node biopsy became the standard of care. Although ASP impair quality of life, as they affect daily life activities, their frequency and determinants in Sub-Saharan Africa remain unclear. METHODS: All women newly diagnosed with breast cancer at the Namibian, Ugandan, Nigerian, and Zambian sites of the African Breast Cancer-Disparities in Outcomes (ABC-DO) cohort study were included. At each 3-month follow-up interview, women answered the EORTC-QLQ-Br23 questionnaire, including three ASP items: shoulder/arm pain, arm stiffness, and arm/hand swelling. We estimated the cumulative incidence of first self-reported ASP, overall and stratified by study and treatment status, with deaths treated as competing events. To identify determinants of ASP, we estimated cause-specific hazard ratios using Cox models stratified by study site. RESULTS: Among 1476 women, up to 4 years after diagnosis, 43% (95% CI 40-46), 36% (33-38) and 23% (20-25), respectively, self-reported having experienced arm/shoulder pain, stiffness and arm/hand swelling at least once. Although risks of self-reported ASP differed between sites, a more advanced breast cancer stage at diagnosis, having a lower socioeconomic position and receiving treatment increased the risk of reporting an ASP. CONCLUSION: ASP are very common in breast cancer survivors in Sub-Saharan Africa. They are influenced by different factors than those observed in high-income countries. There is a need to raise awareness and improve management of ASP within the African setting.

Exploring the impact of cancer registry completeness on international cancer survival differences: a simulation study.

BACKGROUND: Data from population-based cancer registries are often used to compare cancer survival between countries or regions. The ICBP SURVMARK-2 study is an international partnership aiming to quantify and explore the reasons behind survival differences across high-income countries. However, the magnitude and relevance of differences in cancer survival between countries have been questioned, as it is argued that observed survival variations may be explained, at least in part, by differences in cancer registration practice, completeness and the availability and quality of the respective data sources. METHODS: As part of the ICBP SURVMARK-2 study, we used a simulation approach to better understand how differences in completeness, the characteristics of those missed and inclusion of cases found from death certificates can impact on cancer survival estimates. RESULTS: Bias in 1- and 5-year net survival estimates for 216 simulated scenarios is presented. Out of the investigated factors, the proportion of cases not registered through sources other than death certificates, had the largest impact on survival estimates. CONCLUSION: Our results show that the differences in registration practice between participating countries could in our most extreme scenarios explain only a part of the largest observed differences in cancer survival.

A multistate model incorporating estimation of excess hazards and multiple time scales.

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code.