Background: Incremental improvements in early detection, diagnosis and treatment of cancer have led to an increasing number of cancer survivors worldwide. Existing cancer prevalence statistics however have seldom focused on specific phase-of-care pathways essential to inform cancer survivors’ and healthcare needs. Methods: We estimated 5-year cancer prevalence in 186 countries in 2024 using available estimates of cancer incidence and survival by cancer type, time since diagnosis, sex for ages at diagnosis > 15 years. We defined three clinically distinct phases of care: (a) the initial phase of treatment; (b) the phase from initial treatment to end-of-life care where patients are followed-up; and (c) the “end-of-life” phase. These were calculated by first partitioning the 1-year survival to either treatment (if the patient was alive), or end-of-life (if the patient died). For the remaining four years, we then assigned cases to the follow-up phase if they were alive, or end-of-life, if they died from cancer or other causes. Results: The 5-year prevalence was estimated to be approximately 52.4 million, indicating 1% of the global adult population were living within five years of a diagnosis in 2024. Around 22.9% (12/52 million) of prevalent cases were in diagnosis and treatment, 65.5% (34/52 million) in follow-up and 11.6% (6/52 million) were at the end-of-life phases. Female breast, colorectal and prostate cancers were the most prevalent cancer types contributing 43% (23/52 million) of the total prevalent cases, with almost three-quarters of the prevalent cases in the follow-up phase. On the other hand, lung cancer, the fourth most prevalent cancer, had a larger proportion of prevalent cases (4.1 million) in the end-of-life phase (32%, 1.3/4.1 million). Cancer prevalence as a proportion was higher in very high versus low Human Development Index (HDI) countries (2,608 vs. 417 survivors per 100,000 respectively), yet the proportion in the end-of-life phase was higher among low HDI countries (8% vs. 12% of all prevalent cases in very high vs low HDI, respectively). Interpretation: Among the 52 million cancer survivors estimated in 2024, there are substantial variations in cancer survivorship at different phases of care, driven by distinct cancer profiles coupled with survival disparities. This study highlights the need for equitable access to early detection, timely diagnosis and comprehensive care to improve survival and quality of life cancer survivors worldwide.

Introduction. Cost-effectiveness analyses are vital in guiding decisions on treatment reimbursement. Natural history models are central to these, enabling the estimation of long-term costs and quality-adjusted life-years (QALYs) in the absence of lifetime trial data. Rare disease data are often scarce, resulting in disease progression being estimated through clinical assumptions. This study aims to evaluate how different modeling approaches influence cost-effectiveness estimates in rare disease health technology assessments (HTAs), using Duchenne muscular dystrophy (DMD) as a case study. Methods. A published economic model was used to compare 2 approaches for estimating disease progression: an assumption-based method relying on clinical plausibility and data-driven methods using data from 1,005 patients with DMD across 8 studies. Transition probabilities were estimated assuming increasing flexibility of study heterogeneity and compared with a simulated treatment cohort. Models were evaluated by comparing incremental cost-effectiveness ratios (ICERs) across approaches. No gold standard exists, so the plausibility of predictions was evaluated by comparing survival and disease progression estimates to published milestones. Results. Results showed that although the assumption-based model was clinically plausible, it predicted higher QALY gains (0.77) and lower ICERs (£1.96M per QALY) than data-driven methods did, which estimated QALY gains of 0.25, 0.26, 0.27, and 0.28 and ICERs of £6.2M, £6.2M, £5.8M, and £5.7M per QALY for the least to most flexible models, respectively. Limitations. No covariate effects or updated cost and utility data were incorporated, as the study purpose was a methodological comparison between approaches. Analyses were deterministic not probabilistic. Conclusions and Implications. This study emphasizes the critical role of model selection for HTA in rare diseases, showing that cost-effectiveness estimates from robust data-driven approaches can differ from clinically plausible assumption-based models. Highlights The choice of a natural history modeling method can drastically alter the cost-effectiveness results in rare disease evaluations. A case study in Duchenne muscular dystrophy demonstrates how different modeling approaches yield divergent cost-effectiveness outcomes. Assumption-based models, even when clinically plausible, may underestimate measures of cost-effectiveness and result in less reliable guidance for decision makers. Data-driven models using real-world patient data provide more reliable estimates for health technology assessment (HTA). This study offers practical guidance for analysts and HTA bodies on selecting robust modeling approaches in rare disease contexts.

BACKGROUND:Global disparities exist in cancer incidence, mortality, and survival. We aimed to provide estimates of avoidable deaths among people diagnosed with cancer to inform the prioritisation of interventions and narrow cancer inequalities. METHODS:National incidence estimates for 35 cancer sites in 2022 for 185 countries were extracted from the GLOBOCAN database. We estimated numbers of avoidable deaths within 5 years of diagnosis for patients diagnosed with cancer in 2022, consisting of those deaths avoidable through primary prevention (preventable deaths) and those avoidable through early detection and improved access to treatment (treatable deaths) by cancer site, country, region, and human development index (HDI) group. Preventable deaths were estimated using population attributable fractions for tobacco use, alcohol consumption, excess body weight, infectious agents, and ultraviolet radiation obtained from the literature. Treatable deaths were estimated by eliminating survival differences using 5-year net survival from the SURVCAN-3 project and additional sources. Preventable, treatable, and overall avoidable deaths as proportions of the total expected deaths within 5 years of cancer diagnosis were also calculated. FINDINGS:5 years after cancer diagnosis, 4·5 million (47·6% [95% uncertainty interval 47·5-47·8]) of the 9·4 million expected deaths were avoidable. Of these avoidable deaths, 3·1 million (3·1-3·1; 33·2% [33·1-33·3] of total expected deaths) were preventable and 1·4 million (1·4-1·4; 14·4% [14·4-14·5]) were treatable. Lung, liver, stomach, colorectal, and cervical cancers contributed the greatest burden, collectively accounting for 59·1% of all avoidable deaths. Lung cancer was responsible for the most preventable deaths (1·1 million; 34·6% of all preventable deaths), while female breast cancer was responsible for the most treatable deaths (0·2 million; 14·8% of all treatable deaths). Disproportionately large proportions of avoidable deaths from cervical and breast cancer were observed in countries with a low or medium HDI. INTERPRETATION:Nearly half of deaths among people diagnosed with cancer globally could be avoided through primary prevention and improvements in early detection and curative cancer treatment. Global efforts are needed to tailor prevention, early diagnosis, and treatment of cancer to address inequities in avoidable deaths, especially in low and medium HDI countries. FUNDING:Erasmus Mundus Exchange Programme and French National Cancer Institute (INCa).

BACKGROUND:The COVID-19 pandemic had an impact on cancer services globally. There is a crucial need to understand how the incidence and stage of major cancer types were affected internationally. We aimed to assess these metrics in seven countries within the International Cancer Benchmarking Partnership. METHODS:This population-based study used data on 2·6 million patients diagnosed with primary cancers of the colon, rectum, lung, prostate, female breast, ovary, and melanoma of the skin, between Jan 1, 2015, and Dec 31, 2020. Data were collected from cancer registries in 18 jurisdictions from seven countries participating in the International Cancer Benchmarking Partnership; Australia, Canada, Denmark, Ireland, New Zealand, Norway, and the UK. The main outcomes of monthly cases and age-standardised incidence rates by site during the pandemic (April 1 to Dec 31, 2020) were compared with predictions for the same year based on pre-pandemic trends (Jan 1, 2015, to Dec 31, 2019). FINDINGS:Between April 1, and Dec 31, 2020, 55 713 (16%) of 347 666 expected cases were predicted to be missing, with the largest deficits seen for prostate cancer (24%), female breast cancer (18%), and melanoma (18%), and the smallest deficits seen for ovarian (4%) and lung cancer (8%). The largest difference between observed and predicted incidence rates for prostate cancer was seen in the UK (164·9 per 100 000 person-years predicted vs 101·4 per 100 000 person-years observed) and the smallest difference seen in Norway (164·1 vs 168·4). Reductions in incidence were greatest from April 1, to July 31, versus from Aug 1, to Dec 31, in 2020. The percentage deficits between observed and predicted cases were 54% (UK) and 36% (Ireland) for prostate cancer, 40% (UK) and 34% (Ireland) for breast cancer, and 40% (UK) and 35% (Canada) for melanoma. INTERPRETATION:The pandemic's greatest impact was during the first few months of societal lockdowns in 2020 when barriers in access to health care were greatest. Further research is needed to understand whether patients with a missed diagnosis were diagnosed at a later date and if they presented at a later stage. FUNDING:Canadian Partnership Against Cancer; Cancer Council Victoria; Cancer Institute New South Wales; Cancer Research UK; Danish Cancer Society; National Cancer Registry Ireland; The Higher Education Authority North South Research Programme, Health Data Research UK; Te Aho o Te Kahu, Cancer Control Agency New Zealand; New Zealand Cancer Society; National Health Service England; Norwegian Cancer Society; Public Health Agency Northern Ireland, on behalf of the Northern Ireland Cancer Registry; The Scottish Government; Western Australia Department of Health; and Wales Cancer Network.

Quantifying cancer survival is a crucial component of cancer surveillance and control. Survival for all cancers combined is an overall summary to explore differences between population groups and over time. Net survival is the usual measure for reporting survival for all cancers combined. Differences in the cancer site distribution between groups can be adjusted for using standardization. We propose using individual weights incorporated into the Pohar Perme estimator of net survival for standardized all cancers combined survival estimates, rather than a weighted average of stratum specific estimates. This removes sparse data problems, where estimates are unobtainable for some strata. Extending to reference adjusted all-cause survival gives an alternative, interpretable measure, enabling partitioning of all-cause survival differences into those due to cancer site/age/sex distribution differences, other cause mortality differences and cancer mortality differences. We illustrate the methods using data on 749 889 individuals diagnosed with cancer in Norway 1986-2021. Using individual weights gives very similar estimates to traditional and model-based standardization and avoids using ad-hoc sparse data methods. Reference adjusted all-cause survival provides measures with simpler interpretation. For example, between 1986 and 1990 and 2016-2021 there was a 25.9 %age point improvement in 5-year all-cause survival. This improvement was partitioned into changes in the site/age/sex distribution (2.0), changes in other cause mortality rates (4.4) with the majority (19.6) due to improvements in cancer survival. Survival of all cancers combined is easily analyzed non-parametrically using individual weights. Reference adjusted all-cause survival gives a more interpretable measure improving understanding of differences over time/between groups.

Objective:Unlike survival measures, life expectancy readily illustrates the burden of cancer on society and the average impact on individuals diagnosed with cancer. Cancer stage at diagnosis is a key prognostic factor and hence it is important to obtain stage-specific life expectancy estimates. However, completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries. Therefore, it can be challenging to obtain the long-term stage-specific survival estimates required for estimating stage-specific life expectancy. We provide the first stage-specific life expectancy estimates using whole population data in England. Methods and analysis:Multiple imputation was used to impute values of cancer stage at diagnosis for patients with missing stage at diagnosis information. The simultaneous application of period analysis to obtain up-to-date estimates restricts the contribution of patients with historical diagnoses and hence improves the overall completeness of stage at diagnosis information. For each of the 10 cancer sites in this study, we fit a flexible parametric excess hazard model for each cancer stage on the cumulative excess hazard scale and estimated stage-specific life expectancy using the relative survival framework. Results:The differences in stage-specific and sex-specific life expectancy were evaluated from 40 to 90 years of age. Colorectal cancer, prostate cancer and bladder cancer yield much lower estimates of life expectancy for patients diagnosed with stage IV cancer compared with stages I-III. For example, female patients diagnosed with stage IV colorectal cancer at 70 years of age have a life expectancy of 72.3 years, while those with stages I-III can expect to live beyond 82.0 years. The remaining cancer sites yield approximately equal reductions in life expectancy with each increase in stage at diagnosis from I to IV. Conclusion:We offer the first stage-specific life expectancy estimates for a range of cancer sites in England using data from the National Cancer Registration and Analysis Service, with follow-up until February 2020. Estimates of stage-specific life expectancy provide a real-world, intuitive metric to evaluate the impact of cancer stage at diagnosis on prognosis up to a lifetime horizon. Stage-specific life expectancy estimates also provide key information regarding the potential benefits of early diagnosis initiatives in terms of gains in life years.

As the burden of global cancer diagnoses rise, there is growing importance to provide accessible survival statistics to patients. Although life expectancy (LE) is often used to assess economic benefit of new treatments, it is rarely provided to help patients comprehend their diagnosis as this metric often requires extrapolation. We sought to determine the best modelling framework for providing this extrapolation, the minimum follow-up required, and circumstances where reliable estimates can be obtained. We analysed United States cancer registration data collected via the Surveillance, Epidemiology and End Results (SEER) Program. 122,703 patients aged 18-89 diagnosed between 1988 and 1991 with breast, colorectal, lung, or stomach cancer at localised, regional, or distant stages were included (follow-up until 2021). Mortality risk factors included age, sex, and stage at diagnosis. All-cause mortality was modelled and extrapolated using all-cause, cause-specific, and relative survival frameworks across 2-, 3-, 5-, 10-, and 20-year follow-up. Flexible parametric models were built to assess the importance of model complexity (Simple Model(s): Main-effects, Complex Model(s): Main-effects, interactions and time-dependent-effects). Timescales for other-cause mortality within the cause-specific framework were also compared (Time-since-diagnosis, or Attained-age). For evaluation, patients were categorised into 30 risk groups (5 age-groups, 3 stages, and 2 sexes; females only for breast cancer). Using at least 10-years of follow-up, LE/30-year restricted mean survival time (RMST) can be predicted to within 1-year/10% of observed values, with at least 80% of covariate groups predicted to within this relatively small difference for breast, colorectal, and lung cancer patients of varied risks. The relative and cause-specific survival frameworks produced reasonable extrapolated estimates with attained age the recommended timescale for other-cause mortality in the cause-specific setting. Increasing model complexity improves accuracy, particularly among lower-risk patients, typically younger, localised individuals. While the study demonstrated reasonable estimates for 50% of stomach cancer patients primarily those at high risk, further research is required to calculate LE/30-year RMST for lower-risk stomach cancer patients.

For the analysis of survival data obtained from cancer registries, it is common to use the relative survival framework, which incorporates expected mortality rates rather than relying on cause-of-death information. The relative survival framework enables comparisons between population groups where the effect of mortality due to the cancer is isolated to enable fair comparisons when there is differential other-cause mortality between the groups being compared. The stpp command provides nonparametric estimates of marginal relative survival and a range of other nonparametric estimates, including all-cause survival and crude probabilities of death and also recently developed reference-adjusted measures. In addition, it enables (age) standardization to be performed using both traditional standardization and the individual weighting approach. The genindweights command simplifies the process of calculating individual weights.

Marginal life expectancy (LE) and loss in life expectancy (LLE) for patients aged 50 to 99 with colon cancer in Sweden during the years 1976 to 2010, using different reference-adjusted approaches and different years as reference.

BACKGROUND:Fluoropyrimidine chemotherapy is administered first-line for many gastrointestinal cancers. However, patients with cardiovascular disease commonly receive alternative treatment due to cardiotoxicity concerns. OBJECTIVES:This study sought to assess the risks of all-cause mortality and acute cardiovascular events with fluoropyrimidine treatment. METHODS:We conducted an observational cohort study applying a target trial emulation framework to linked national cancer, cardiac, and hospitalization registry data from the Virtual Cardio-Oncology Research Initiative. Adults diagnosed with tumors eligible for fluoropyrimidine-based chemotherapy as first-line therapy were included. All-cause mortality and a composite of hospitalization for acute cardiovascular events (acute coronary syndrome, heart failure, cardiac arrhythmia, cardiac intervention, cardiac arrest, and cardiac death) were compared in patients treated with fluoropyrimidine-based chemotherapy vs alternative management. Adjusted, weighted pooled logistic regression models were used to estimate the 1-year risk difference (RD). RESULTS:Among 103,110 patients (mean age 69.7 years, 59% male), the absolute risk of death at 1 year was significantly lower in fluoropyrimidine-treated patients (RD: -7.7%; 95% CI: -8.7% to -6.7%) with a small increased risk of acute cardiovascular events (RD: 0.9%; 95% CI: 0.0% to 1.9%). This was primarily due to arrhythmias (RD: 0.8%; 95% CI: 0.1% to 1.6%) and cardiac arrest (RD: 0.3%; 95% CI: 0.1% to 0.5%), with no increased risk of acute coronary syndromes including in the subgroup of patients with pre-existing coronary artery disease. CONCLUSIONS:The markedly improved overall survival with fluoropyrimidines in patients with gastrointestinal cancer significantly outweighs the small risk of cardiac arrhythmia and arrest. Oncologists should take this into consideration for decision making to avoid undue clinical conservatism, particularly in patients with cardiovascular disease.

BACKGROUND:Peripheral artery disease and diabetes are the main primary risk factors for non-traumatic major lower limb amputation. Regional variation in incidence of major lower limb amputation has yet to be fully described in terms of these risk factors and explained. The aim of this study was to estimate yearly incidence of major lower limb amputation over a 10-year interval (2010-2019) across England, by related condition and by region and, additionally, to investigate reasons for regional variation. METHODS:This observational study utilized primary care (Clinical Practice Research Datalink Aurum), secondary care (Hospital Episode Statistics), death and demographic data in England. Adults registered with a practice using Clinical Practice Research Datalink Aurum and with Hospital Episode Statistics linkage were included. Patients with a record of major lower limb amputation during the interval 1 January 2010 to 31 December 2019 were identified and yearly incidence rates of major lower limb amputation were calculated. Co-morbidities analysed were cardiovascular disease (including coronary artery disease, peripheral artery disease and cerebrovascular disease), diabetes (of any type) and cancer. Demographic and socioeconomic covariates analysed were age, sex, ethnicity, deprivation level, region and urban/rural categorization. RESULTS:The study included 18 397 483 individuals, 8584 of which had a record of major lower limb amputation. The age-standardized yearly incidence rate of major lower limb amputation in England decreased by 30% from 11.2 per 100 000 person-years in 2010 to 7.8 in 2019. The incidence rate in those with diabetes fell by 30% over the 10-year interval, rose by 20% for those with both diabetes and cardiovascular disease, and changed little in those with cardiovascular disease. In 2019, the age-standardized incidence rate was highest in the North East (14.8 per 100 000 person-years) and lowest in the East of England (4.5 per 100 000 person-years). Between 2010 and 1019, incidence rates decreased across all regions, the largest decrease of 56% in the East Midlands and the smallest of 8% in the North East. Statistically significant regional variation remained after full adjustment for demographic, socioeconomic data and related conditions. CONCLUSION:Whilst the incidence of major lower limb amputation is decreasing overall, significant regional variation in major lower limb amputation exists and is unexplained by demographic, socioeconomic and health data. Regional differences in service provision and accessibility should be investigated to provide further explanation.

Background: As the survival proportions for rare cancers are on average worse than for common cancers, assessing the expected remaining life years in good health becomes highly relevant. This study aimed to estimate the healthy life expectancy (HLE) of a subset of rare and common cancer survivors, and to assess the determinants of poor perceived health in rare cancer survivors. Methods: To calculate HLE, survival data from the population-based Netherlands Cancer Registry of survivors of a rare cancer (i.e., ovarian cancer, thyroid cancer, Hodgkin lymphoma, non-Hodgkin lymphoma) (n=21,376) and a common cancer (i.e., colorectal cancer (CRC)) (n=76,949) were combined with quality of life (QoL) data from the PROFILES registry on a random sample of the rare (n=1025) and common cancer (n=2400) survivors. A flexible parametric relative survival model was used to estimate life expectancy (LE) and years of life lost, and multivariate logistic regression was applied to determine factors related to reported poor perceived health. Results: Patients previously diagnosed with a rare cancer had an average LE of 8-36 years and were expected to spend >= 67 % of their remaining life in good health. CRC survivors had an average LE of 10 years with approximately 65 % of their remaining life expected to spend in good health. For all cancer types, those aged >= 65 years or with stage IV had the lowest HLE. Low socioeconomic status, advanced stage, and having received radiotherapy only were important predictors of poor perceived health among rare cancer survivors. Conclusion: HLE can provide meaningful perspective for patients and practitioners for all cancer types, including rare cancers. Yet, data on QoL for rare cancers should be routinely collected, as such will serve as an indicator for monitoring and improving cancer care, and for enabling HLE measurements in cancer survivors.

The aim of equality, equity, diversity, and inclusion (EDI) is to ensure fair treatment, equal opportunities, equitable outcomes, and representation. The NIHR Research Design Service (RDS) EDI toolkit ( https://www.rssleicesterresources.org.uk/edi-toolkit ) helps researchers embed EDI throughout their work. This study evaluated the applicability of the RDS EDI toolkit for statistical methodology research and proposed adaptations to enable statistical methodologists to embed EDI in their research. A full-day meeting was held to consider how the RDS EDI toolkit could inform the inclusion of EDI principles in statistical methodology research. Twelve individuals attended from the University of Leicester and the NIHR Research Support Service (RSS) Hub delivered by the University of Leicester and Partners. At the meeting, definitions of statistical methodology research and EDI were agreed. The RDS EDI Toolkit was interrogated to identify relevant aspects and additional considerations for statistical methodology research. Overall, the RDS EDI toolkit was valuable for incorporating EDI in statistical methodology research. Five recommendations to supplement the toolkit are proposed to reflect specific EDI challenges for statistical methodology research. Statistical methodology researchers should: Embedding EDI principles throughout statistical methodology research will improve its relevance and quality, better serve the public, and build public trust. It is essential that statistical methodologists strive towards equity in all aspects of their work. This paper demonstrates the value of the NIHR RDS EDI toolkit for statistical methodology research and encourages methodologists to adopt the recommendations in this paper. Further extensions to this work are needed to seek the wider views and experiences of statistical methodologists and public contributors from diverse and under-represented groups.

OBJECTIVES:To describe and assess, via simulation, a constraint-based spline approach to implement smooth hazard ratio (HR) waning in time-to-event analyses. METHODS:A common consideration when extrapolating survival functions to evaluate the long-term performance of a novel intervention is scenarios where the beneficial effect of an intervention eventually disappears (treatment effect waning). One approach to relaxing the proportional hazards assumption for a treatment effect is to model it as a function of the timescale, with a spline function offering a flexible approach. We consider the constraint of coefficients of spline variables to 0 during estimation, leading to log-treatment effects that are constrained to 0 (HR = 1) from a given time-point: enforcing treatment efficacy waning. An example is reported. Datasets were simulated under a variety of scenarios and analyzed with treatment effect waning assumptions under various modeling choices. Bias in mean survival time difference, given fully observed waning or fully censored waning, was assessed and constrained HR estimates were visualized. RESULTS:Given full waning, biases were small unless constraints directly contradicted truths. When waning was extrapolated, akin to real-life practice, biases over observed periods were minimized through the inclusion of a knot at the 95th percentile. The rate at which the HR waned slowed as the upper boundary knot/constraint was placed later, inducing less conservative treatment effect waning assumptions. CONCLUSION:An alternative approach to modeling smooth treatment efficacy waning is demonstrated, enabling HR conditioning and marginal RMST calculation in a single framework, along with applications of the method beyond this use.

When developing/validating prognostic models, it is typical to assess calibration between predicted and observed risks — either in the development dataset or in an external sample. For competing risks data, correct specification of more than one model may be required to ensure well-calibrated predicted risks for the event of interest. Furthermore, interest may be in the predicted risks of the event of interest, competing events and all-causes. Therefore, calibration must be assessed simultaneously using various measures. We focus on the calibration of prediction models for external validation using a cause-specific hazards approach. We propose that miscalibration for cause-specific hazard models be assessed using components specific to each model through the complement of the cause-specific survival alongside the assessment of the calibration of the cause-specific absolute risks. We simulated a range of scenarios to illustrate how to identify which model(s) are mis-specified in an external validation setting. Calibration plots and calibration statistics (calibration slope, calibration-in-the-large) are presented alongside performance measures such as the Brier score and Index of Prediction Accuracy. We use pseudo-observations to calculate observed risks and generate a smooth calibration curve with restricted cubic splines. We fitted flexible parametric survival models to the simulated data to flexibly estimate baseline cause-specific hazards for the prediction of individual cause-specific absolute risks. Our simulations illustrate that miscalibration due to changes in the baseline cause-specific hazards in external validation data is better identified using components from each cause-specific model. A mis-calibrated model on one cause could lead to poor calibration of the predicted absolute risks for each cause of interest, including the all-cause absolute risk. This is because prediction of a single cause-specific absolute risk is impacted by effects of variables on the cause of interest and competing events. If accurate predictions for both all-cause and each cause-specific absolute risks are of interest, this is best achieved by developing and validating models via the cause-specific hazards approach. For each cause-specific model, researchers should evaluate calibration plots separately using the complement of the cause-specific survival function to reveal the cause of any miscalibration. However, this also requires careful consideration of dependent censoring which must be sufficiently accounted for.

BACKGROUND:Determining the most appropriate treatment in patients with cancer with an acute myocardial infarction (MI) can be challenging. Optimal management requires an understanding of bleeding risk which may be different in this population. This study aimed to investigate the bleeding risk among patients with MI with and without cancer, in the first and second year post-MI. METHODS:Patients with MI, with and without cancer were identified from a national cardio-oncology database from England between 2006 and 2019. The outcome was a presentation to hospital for a major bleeding event, with patients followed for a maximum of 24 months. Inverse probability weighting was used to compare cancer and non-cancer cohorts in time-to-event analyses. RESULTS:587 279 patients with MI were identified, 9820 (1.7%) had cancer and 577 459 (98.3%) did not. Colorectal, prostate, breast, lung and bladder cancer were the most common types of cancer. The rate of hospital presentation for bleeding in the first year post-MI was higher in patients with cancer than the non-cancer reference population (HR: 1.53, 95% CI 1.45 to 1.62, p<0.001). 506 280 patients with MI were followed up in the second year post-MI. 5666 (1.1%) had cancer and 500 614 (98.9%) did not. The bleeding rate in patients with MI with cancer remained elevated in the second year post-MI (HR: 1.42, 95% CI 1.29 to 1.56, p<0.001). There were marked differences in bleeding between cancer types. CONCLUSION:In this real-world observational study, patients with cancer had an increased bleeding risk in the first year post-MI which decreased but persisted in the second year after MI. Bleeding risk in patients with cancer must be carefully assessed post-MI.

BACKGROUND:Along with incidence and mortality, temporal trends of cancer survival are a crucial part of cancer surveillance and control. The most common reported statistic is net survival, usually age standardized to an external reference population. However, net survival has an awkward interpretation, which has led to confusion and misunderstanding. METHODS:We describe the use of reference-adjusted all-cause survival, and the crude probability of death as an alternative to net survival for the analysis of temporal trends in cancer survival. Reference-adjusted measures aim to enable fair comparisons by incorporating additional reference-expected mortality rates into the estimation process. The different approaches are illustrated using data on 95,285 women diagnosed with breast cancer in Norway from 1986 to 2021. RESULTS:We compare different age distributions for age standardization and describe how using a recent calendar period for both the reference-expected mortality rates and age distribution for standardization leads to simple interpretation. CONCLUSIONS:Reference-adjusted measures for monitoring temporal trends in cancer survival can lead to improved understanding and is of more relevance to patients and policy makers who live and make decisions in the real world. Using the most recent calendar period for both the age standard and the reference-expected mortality rates leads to simple and useful interpretation of the measures. IMPACT:Increasing the use of reference-adjusted measures in the analysis of population-based cancer studies will enhance the understanding of cancer survival trends. The freely available software increases the likelihood of uptake.

Abstract Introduction Cardiovascular disease (CVD) and cancer are common causes of morbidity and mortality. Advancements in treatment strategies for both diseases have resulted in a growing population who live with both conditions. Myocardial infarction (MI) represents approximately 20% of all CVD admissions in cancer patients and 10% of patients who present with an acute MI have cancer. Managing MI patients with cancer require careful balancing of their ischaemic and bleeding risks. While dual antiplatelet therapy (DAPT) increases a patient’s bleeding risk, cancer patients are at further increased risk due to a range of direct and indirect cancer effects. While our recent studies demonstrated an increased bleeding risk in cancer patients in the 1st year after MI (the period of intensive APT), it is currently not known if this risk is sustained beyond this period. VICORI is the world’s first whole-country cardio-oncology research platform, linking data from the National Cancer Registration and Analysis Service, National Institute for Cardiovascular Outcomes Research and Hospital Episode Statistics [1]. We investigated the risk of bleeding among MI patients in the 2nd year post-MI, where a large majority of patients would have completed a course of DAPT, stratified by the presence or absence of cancer. Methods In this retrospective observational study, we investigated the risk of bleeding following an MI between 2006-2019, in patients with and without cancer. Cancer was defined as a diagnosis of cancer in the 1-year preceding MI. Patients who lived past the 1st year after MI were followed-up from 12 to 24 months after their MI. We used inverse probability weighting based on propensity scores to produce a balanced cohort of patients with and without cancer. Cox proportional hazard and flexible parametric modelling were used to investigate cancer as a predictor of bleeding. Subgroup analyses were performed on stent status, MI and cancer type. Results Of 506280 patients presenting with MI, 5666 (1.1%) had cancer while 500614 (98.9%) did not. Prostate (n=1377), colorectal (n=1024), lung (n=490), breast (n=448) and bladder (n=334) cancers were among the most common types of cancers. The rate (hazard) of bleeding in MI was higher in patients with cancer in the 2nd year post-MI (HR:1.42, 95%CI 1.29-1.56) compared to those without. From the flexible parametric survival analysis, the risk of bleeding in cancer patients in the 2nd year post-MI was lower compared to the 1st year but remained statistically significant compared to non-cancer patients. Conclusion In this large real-world study, cancer patients have a persistent, increased bleeding risk beyond the 1-year post-MI period, compared to those without cancer, and this risk differed by type of cancer. Further work is needed to identify specific patient characteristics in each patient sub-group which alters one’s ischaemic or bleeding risk so they can be balanced favourably with personalised strategies.

Monitoring trends of cancer incidence, mortality and survival is vital for the planning and delivery of health services, and the evaluation of diagnostics and treatment at the population level. Furthermore, comparisons are often made between population subgroups to explore inequalities in outcomes. During the COVID-19 pandemic routine delivery of health services were severely disrupted. Resources were redeployed to COVID-19 services and patient risk of COVID-19 infection required serious consideration. Cancer screening services were paused, the availability of healthcare providers was reduced and, in some cases, patients faced difficulty in accessing optimal treatment in a timely manner. Given these major disruptions, much care should be taken when interpreting changes in cancer survival estimates during this period. The impact on cancer incidence and mortality statistics that have already been reported in some jurisdictions should drive further thought on the corresponding impact on cancer survival, and whether any differences observed are real, artificial or a combination of the two. We discuss the likely impact on key cancer metrics, the likely implications for the analysis of cancer registration data impacted by the pandemic and the implications for comparative analyses between population groups and other risk factor groups when using data spanning the pandemic period.

There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.