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Abstract: Background: It is vital that unbiased estimates of relative survival are estimated and reported by cancer registries. A single figure of relative survival is often required to make reporting simpler. This can be obtained by pooling all ages or, more commonly, by using age-standardisation. The various methods for providing a single figure estimate of relative survival can give very different estimates. Methods: The problem is illustrated through an example using Finnish thyroid cancer data. The differences are further explored through a simulation study that investigates the effect of age on the estimates of relative survival. Results: The example highlights that in practice the all-age estimates from the various methods can be substantially different (up to 6 percentage units at 15 years of follow-up). The simulation study confirms the finding that differing estimates for the all-age estimates of relative survival are obtained. Performing age-standardisation makes the methods more comparable and results in better estimation of the true net survival. Conclusions: The all-age estimates of relative survival rarely give an appropriate estimate of net survival. We feel that modelling or stratifying by age when calculating relative survival is vitally important as the lack of homogeneity in the cohort of patients leads to potentially biased estimates. We feel that the methods using modelling provide a greater flexibility than life-table based approaches. The flexible parametric approach does not require an arbitrary splitting of the time-scale, which makes it more computationally efficient. It also has the advantage of easily being extended to incorporate time-dependent effects. [Copyright &y& Elsevier]

Parental Bonding Inventories (PBI) were given to 173 males, mean age 22, and analyzed based on the participant's familial risk for alcoholism. Differences in PBI scores were examined based on the recency and severity of paternal drinking problems, and on whether both the mother and father had alcohol problems or only the father. Differences in drinking patterns of sons were also examined based on PBI ratings. Fathers of the high familial risk participants were rated by sons as being significantly less Caring than the fathers of low familial risk participants. Participants who rated their fathers as low in Protection had significantly more days of heavy drinking in the past year compared to those who rated their fathers as high in Protection. [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s).)

Background: In the United Kingdom, 15-min appointments with the general practitioner (GP) are recommended for people with complex health conditions, including intellectual disabilities and health needs, but we do not know whether this happens., Aims: We compared number and length of primary care consultations (GP, nurse, other allied health, other) for people with and without intellectual disabilities and health needs., Methods: Linked primary care data from the Clinical Practice Research Datalink (CPRD) in England were used to investigate face-to-face and telephone primary care consultations in 2017-2019. Health needs investigated were: epilepsy; incontinence; severe visual/hearing impairments; severe mobility difficulties; cerebral palsy; and percutaneous endoscopic gastrostomy feeding. Age and gender-standardized consultation rates per year (Poisson), duration of consultations, and the proportion of "long consultations" (≥15 min) were reported., Results: People with intellectual disabilities (n = 7,794) had 1.9 times as many GP consultations per year as those without (n = 176,807; consultation rate ratio = 1.87 [95% confidence interval 1.86-1.89]). Consultation rates with nurses and allied healthcare professionals were also twice as high. Mean GP consultation time was 9-10 min regardless of intellectual disability/health need status. Long GP consultations were less common in people with intellectual disabilities (18.2% [17.8-18.7] vs. 20.9% [20.8-21.0]). Long consultations with practice nurses were more common in people with health needs, particularly severe visual loss., Conclusions: People with intellectual disabilities and/or health needs tend to have more, rather than longer, GP consultations compared with the rest of the population. We recommend further investigation into the role of practice nurses to support people with intellectual disabilities and health needs., (© The Author(s) 2022. Published by Oxford University Press.)

Competing Interests: I declare no competing interests.

Objectives: A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up; hence, sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a hazard ratio (HR) to 1 does not necessarily estimate loss of individual-level treatment effect accurately because of HR selection bias. A simulation study was designed to explore the behavior of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is "survival difference with individual-level waning"., Methods: Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time-varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. Restricted mean survival time differences, estimated having constrained the marginal HR to 1, were compared with true values to assess bias induced by marginal constraints., Results: Under loss of conditional treatment effect, the marginal HR took a value >1 because of covariate imbalances. Constraining this value to 1 lead to restricted mean survival time difference bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect., Conclusions: Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be overestimated, and incremental cost-effectiveness ratios will be underestimated., Competing Interests: Author Disclosures Links to the disclosure forms provided by the authors are available here., (Copyright © 2024. Published by Elsevier Inc.)

Multi-state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one-stage and two-stage approaches to meta-analyzing individual patient data (IPD) in a multi-state survival setting when the number and size of studies being meta-analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi-state IPD were simulated from study- and transition-specific hazard functions. One-stage frailty and two-stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population-level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real-world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out-performed by two-stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta-analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Objectives: Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation., Methods: We describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists., Results: The compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility., Conclusions: The various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment., Competing Interests: Author Disclosures Links to the individual disclosure forms provided by the authors are available here., (Copyright © 2023. Published by Elsevier Inc.)

We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re-estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause-specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995-2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10-year calibration plots demonstrated that using the standard approach over-estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: In population-based cancer studies it is common to try to isolate the impact of cancer by estimating net survival. Net survival is defined as the probability of surviving cancer in the absence of any other-causes of death. Net survival can be estimated either in the cause-specific or relative survival framework. Cause-specific survival considers deaths from the cancer as the event of interest. Relative survival incorporates general population expected mortality rates to represent the other-cause mortality rate. Estimation approaches in both frameworks are impacted by the systematic removal of patients from the risk-set, commonly referred to as informative censoring in the cause-specific framework. In the relative survival framework, the Pohar Perme estimator combats the effect of this systematic removal of patients through weighting. When the two frameworks have been compared, informative censoring is rarely accounted for in the cause-specific framework., Methods: We investigate the use of weighted cause-specific Kaplan-Meier estimates to overcome the impact of informative censoring and compared approaches to defining weights. Individuals remaining in the risk-set are upweighted using their predicted other-cause survival obtained through various model-based approaches. We also compare weights derived from expected mortality rates. We applied the approaches to US cancer registry data and conducted a simulation study., Results: Using weighted cause-specific estimates provides a better estimate of marginal net survival. The unweighted Kaplan-Meier estimates have a similar bias to the Ederer II method for relative survival. Weighted Kaplan-Meier estimates are unbiased and similar to the Pohar Perme estimator. There was little variation between the several weighting approaches., Conclusion: In comparisons of cause-specific and relative survival, it is important to compare "like-with-like", therefore, a weighted approach should be considered for both frameworks. If researchers are interested in obtaining net measures in a cause-specific framework, then weighting is needed to account for informative censoring., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Background: Life expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source, but this has not formally been investigated., Methods: Using data on 451,222 individuals from the Clinical Practice Research Datalink on the presence/absence of intellectual disability and type 2 diabetes mellitus, we compared stratified and combined flexible parametric models, and Chiang's methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang's adjusted life table approach and bootstrapping., Results: The flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided less bias and greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed., Conclusions: Life expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision, less bias and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Different parametric survival models can lead to widely discordant extrapolations and decision uncertainty in cost-effectiveness analyses. The use of excess hazard (EH) methods, which incorporate general population mortality data, has the potential to reduce model uncertainty. This review highlights key practical considerations of EH methods for estimating long-term survival., Methods: Demonstration of methods used a case study of 686 patients from the German Breast Cancer Study Group, followed for a maximum of 7.3 y and divided into low (1/2) and high (3) grade cancers. Seven standard parametric survival models were fit to each group separately. The same 7 distributions were then used in an EH framework, which incorporated general population mortality rates, and fitted both with and without a cure parameter. Survival extrapolations, restricted mean survival time (RMST), and difference in RMST between high and low grades were compared up to 30 years along with Akaike information criterion goodness-of-fit and cure fraction estimates. The sensitivity of the EH models to lifetable misspecification was investigated., Results: In our case study, variability in survival extrapolations was extensive across the standard models, with 30-y RMST ranging from 7.5 to 14.3 y. Incorporation of general population mortality rates using EH cure methods substantially reduced model uncertainty, whereas EH models without cure had less of an effect. Long-term treatment effects approached the null for most models but at varying rates. Lifetable misspecification had minimal effect on RMST differences., Conclusions: EH methods may be useful for survival extrapolation, and in cancer, EHs may decrease over time and be easier to extrapolate than all-cause hazards. EH cure models may be helpful when cure is plausible and likely to result in less extrapolation variability., Highlights: In health economic modeling, to help anchor long-term survival extrapolation, it has been recommended that survival models incorporate background mortality rates using excess hazard (EH) methods.We present a thorough description of EH methods with and without the assumption of cure and demonstrate user-friendly software to aid researchers wishing to use these methods.EH models are applied to a case study, and we demonstrate that EHs are easier to extrapolate and that the use of the EH cure model, when cure is plausible, can reduce extrapolation variability.EH methods are relatively robust to lifetable misspecification.

Background: An increasing proportion of patients with cancer experience acute myocardial infarction (AMI). We investigated differences in quality of AMI care and survival between patients with and without previous cancer diagnoses., Methods: A retrospective cohort study using Virtual Cardio-Oncology Research Initiative data. Patients aged 40+ years hospitalized in England with AMI between January 2010 and March 2018 were assessed, ascertaining previous cancers diagnosed within 15 years. Multivariable regression was used to assess effects of cancer diagnosis, time, stage, and site on international quality indicators and mortality., Results: Of 512 388 patients with AMI (mean age, 69.3 years; 33.5% women), 42 187 (8.2%) had previous cancers. Patients with cancer had significantly lower use of ACE (angiotensin-converting enzyme) inhibitors/angiotensin receptor blockers (mean percentage point decrease [mppd], 2.6% [95% CI, 1.8-3.4]) and lower overall composite care (mppd, 1.2% [95% CI, 0.9-1.6]). Poorer quality indicator attainment was observed in patients with cancer diagnosed in the last year (mppd, 1.4% [95% CI, 1.8-1.0]), with later stage disease (mppd, 2.5% [95% CI, 3.3-1.4]), and with lung cancer (mppd, 2.2% [95% CI, 3.0-1.3]). Twelve-month all-cause survival was 90.5% in noncancer controls and 86.3% in adjusted counterfactual controls. Differences in post-AMI survival were driven by cancer-related deaths. Modeling improving quality indicator attainment to noncancer patient levels showed modest 12-month survival benefits (lung cancer, 0.6%; other cancers, 0.3%)., Conclusions: Measures of quality of AMI care are poorer in patients with cancer, with lower use of secondary prevention medications. Findings are primarily driven by differences in age and comorbidities between cancer and noncancer populations and attenuated after adjustment. The largest impact was observed in recent cancer diagnoses (<1 year) and lung cancer. Further investigation will determine whether differences reflect appropriate management according to cancer prognosis or whether opportunities to improve AMI outcomes in patients with cancer exist., Competing Interests: Disclosures Dr Teece, Dr Sweeting, B. Coles, Dr Oliver-Williams, Dr Welch, Dr Rutherford, Dr Lambert, D. Adlam, and M.D. Peake had financial support from the British Heart Foundation and Cancer Research UK for the submitted work. D. Adlam has received research funding and in kind support for unrelated research from AstraZeneca, Inc. He has received an educational grant from Abbott Vascular, Inc, to support a clinical research fellow for unrelated research. He has also conducted consultancy for General Electric Inc., to support research funds for unrelated research. B. Coles previously received funding from Novo Nordisk. J. Deanfield had financial support from the British Heart Foundation in the previous 3 years. Dr Sweeting is a full-time employee of AstraZeneca. C. Weston is the clinical lead of the Myocardial Ischaemia National Audit Project. Dr de Belder reports data safety and monitoring board membership of the UK GRIS (UK GRACE Risk Score Intervention Study) trial, is chair of the ARREST (a randomized trial of expedited transfer to a cardiac arrest center for non-ST elevation out-of-hospital cardiac arrest) trial Steering Committee, and is an executive member of the DAPA-MI (dapagliflozin effects on cardiovascular events in patients with an acute heart attack) trial. The other authors report no conflicts.

Aims: Currently, little evidence exists on survival and quality of care in cancer patients presenting with acute heart failure (HF). The aim of the study is to investigate the presentation and outcomes of hospital admission with acute HF in a national cohort of patients with prior cancer., Methods and Results: This retrospective, population-based cohort study identified 221 953 patients admitted to a hospital in England for HF during 2012-2018 (12 867 with a breast, prostate, colorectal, or lung cancer diagnosis in the previous 10 years). We examined the impact of cancer on (i) HF presentation and in-hospital mortality, (ii) place of care, (iii) HF medication prescribing, and (iv) post-discharge survival, using propensity score weighting and model-based adjustment. Heart failure presentation was similar between cancer and non-cancer patients. A lower percentage of patients with prior cancer were cared for in a cardiology ward [-2.4% age point difference (ppd) (95% CI -3.3, -1.6)] or were prescribed angiotensin-converting enzyme inhibitors or angiotensin receptor antagonists (ACEi/ARB) for heart failure with reduced ejection fraction [-2.1 ppd (-3.3, -0.9)] than non-cancer patients. Survival after HF discharge was poor with median survival of 1.6 years in prior cancer and 2.6 years in non-cancer patients. Mortality in prior cancer patients was driven primarily by non-cancer causes (68% of post-discharge deaths)., Conclusion: Survival in prior cancer patients presenting with acute HF was poor, with a significant proportion due to non-cancer causes of death. Despite this, cardiologists were less likely to manage cancer patients with HF. Cancer patients who develop HF were less likely to be prescribed guideline-based HF medications compared with non-cancer patients. This was particularly driven by patients with a poorer cancer prognosis., Competing Interests: Conflict of interest: M.S., B.C., L.T., M.R., P.C.L., D.A., and M.P. had financial support from the British Heart Foundation and Cancer Research UK for the submitted work; D.A. has received research funding and in-kind support for unrelated research from AstraZeneca Inc. He has received an educational grant from Abbott Vascular Inc. to support a clinical research fellow for unrelated research. He has also conducted consultancy for GE Inc. to support research funds for unrelated research; B.C. previously received funding from Novo Nordisk; J.D. had financial support from the British Heart Foundation in the previous 3 years; M.S. is an employee of AstraZeneca and owns shares in AstraZeneca. C.W. is Clinical Lead of the Myocardial Ischaemia National Audit Project (MINAP); M.d.B. reports DSMB membership of the UK GRIS Trial, chair of the ARREST Trial Steering Committee, and Executive Member of the DAPA-MI Trial; no other relationships or activities that could appear to have influenced the submitted work have been reported., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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., (© 2022. The Author(s).)

Background: The loss in life expectancy, LLE, is defined as the difference in life expectancy between patients with cancer and that of the general population. It is a useful measure for summarizing the impact of a cancer diagnosis on an individual's life expectancy. However, it is less useful for making comparisons of cancer survival across groups or over time, because the LLE is influenced by both mortality due to cancer and other causes and the life expectancy in the general population., Methods: We present an approach for making LLE estimates comparable across groups and over time by using reference expected mortality rates with flexible parametric relative survival models. The approach is illustrated by estimating temporal trends in LLE of patients with colon cancer in Sweden., Results: The life expectancy of Swedish patients with colon cancer has improved, but the LLE has not decreased to the same extent because the life expectancy in the general population has also increased. When using a fixed population and other-cause mortality, that is, a reference-adjusted approach, the LLE decreases over time. For example, using 2010 mortality rates as the reference, the LLE for females diagnosed at age 65 decreased from 11.3 if diagnosed in 1976 to 7.2 if diagnosed in 2010, and from 3.9 to 1.9 years for women 85 years old at diagnosis., Conclusions: The reference-adjusted LLE is useful for making comparisons across calendar time, or groups, because differences in other-cause mortality are removed., Impact: The reference-adjusted approach enhances the use of LLE as a comparative measure., (©2022 The Authors; Published by the American Association for Cancer Research.)

Background: When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways., Methods: We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model., Results: With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest., Conclusions: Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated., (© 2022. The Author(s).)

Objective: To provide the first international comparison of oesophageal and gastric cancer survival by stage at diagnosis and histological subtype across high-income countries with similar access to healthcare., Methods: As part of the ICBP SURVMARK-2 project, data from 28 923 patients with oesophageal cancer and 25 946 patients with gastric cancer diagnosed during 2012-2014 from 14 cancer registries in seven countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK) were included. 1-year and 3-year age-standardised net survival were estimated by stage at diagnosis, histological subtype (oesophageal adenocarcinoma (OAC) and oesophageal squamous cell carcinoma (OSCC)) and country., Results: Oesophageal cancer survival was highest in Ireland and lowest in Canada at 1 (50.3% vs 41.3%, respectively) and 3 years (27.0% vs 19.2%) postdiagnosis. Survival from gastric cancer was highest in Australia and lowest in the UK, for both 1-year (55.2% vs 44.8%, respectively) and 3-year survival (33.7% vs 22.3%). Most patients with oesophageal and gastric cancer had regional or distant disease, with proportions ranging between 56% and 90% across countries. Stage-specific analyses showed that variation between countries was greatest for localised disease, where survival ranged between 66.6% in Australia and 83.2% in the UK for oesophageal cancer and between 75.5% in Australia and 94.3% in New Zealand for gastric cancer at 1-year postdiagnosis. While survival for OAC was generally higher than that for OSCC, disparities across countries were similar for both histological subtypes., Conclusion: Survival from oesophageal and gastric cancer varies across high-income countries including within stage groups, particularly for localised disease. Disparities can partly be explained by earlier diagnosis resulting in more favourable stage distributions, and distributions of histological subtypes of oesophageal cancer across countries. Yet, differences in treatment, and also in cancer registration practice and the use of different staging methods and systems, across countries may have impacted the comparisons. While primary prevention remains key, advancements in early detection research are promising and will likely allow for additional risk stratification and survival improvements in the future., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on., Methods: We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented., Results: We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research., Conclusions: We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research., (© 2022. The Author(s).)

Health needs are common in people living with intellectual disabilities, but we do not know how they contribute to life expectancy. We used the Clinical Practice Research Datalink (CPRD) linked with hospital/mortality data in England (2017-2019) to explore life expectancy among people with or without intellectual disabilities, indicated by the presence or absence, respectively, of: epilepsy; incontinence; severe visual loss; severe visual impairment; severe mobility difficulties; cerebral palsy and PEG feeding. Life expectancy and 95% confidence intervals were compared using flexible parametric methods. At baseline, 46.4% (total n = 7794) of individuals with intellectual disabilities compared with 9.7% (total n = 176,807) in the comparison group had ≥1 health need. Epilepsy was the most common health need (18.7% vs. 1.1%). All health needs except hearing impairment were associated with shorter life expectancy: PEG feeding and mobility difficulties were associated with the greatest loss in life years (65-68% and 41-44%, respectively). Differential life expectancy attenuated but remained (≈12% life years lost) even after restricting the population to those without health needs (additional years expected to live at 10 years: 65.5 [60.3, 71.1] vs. 74.3 [73.8, 74.7]). We conclude that health needs play a significant role but do not explain all of the differential life expectancy experienced by people with intellectual disabilities.

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., (© 2022. The Author(s).)

Previous articles in Statistics in Medicine describe how to calculate the sample size required for external validation of prediction models with continuous and binary outcomes. The minimum sample size criteria aim to ensure precise estimation of key measures of a model's predictive performance, including measures of calibration, discrimination, and net benefit. Here, we extend the sample size guidance to prediction models with a time-to-event (survival) outcome, to cover external validation in datasets containing censoring. A simulation-based framework is proposed, which calculates the sample size required to target a particular confidence interval width for the calibration slope measuring the agreement between predicted risks (from the model) and observed risks (derived using pseudo-observations to account for censoring) on the log cumulative hazard scale. Precise estimation of calibration curves, discrimination, and net-benefit can also be checked in this framework. The process requires assumptions about the validation population in terms of the (i) distribution of the model's linear predictor and (ii) event and censoring distributions. Existing information can inform this; in particular, the linear predictor distribution can be approximated using the C-index or Royston's D statistic from the model development article, together with the overall event risk. We demonstrate how the approach can be used to calculate the sample size required to validate a prediction model for recurrent venous thromboembolism. Ideally the sample size should ensure precise calibration across the entire range of predicted risks, but must at least ensure adequate precision in regions important for clinical decision-making. Stata and R code are provided., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Immortal time bias is common in observational studies but is typically described for pharmacoepidemiology studies where there is a delay between cohort entry and treatment initiation., Methods: This study used the Clinical Practice Research Datalink (CPRD) and linked national mortality data in England from 2000 to 2019 to investigate immortal time bias for a specific life-long condition, intellectual disability. Life expectancy (Chiang's abridged life table approach) was compared for 33,867 exposed and 980,586 unexposed individuals aged 10+ years using five methods: (1) treating immortal time as observation time; (2) excluding time before date of first exposure diagnosis; (3) matching cohort entry to first exposure diagnosis; (4) excluding time before proxy date of inputting first exposure diagnosis (by the physician); and (5) treating exposure as a time-dependent measure., Results: When not considered in the design or analysis (Method 1), immortal time bias led to disproportionately high life expectancy for the exposed population during the first calendar period (additional years expected to live: 2000-2004: 65.6 [95% CI: 63.6,67.6]) compared to the later calendar periods (2005-2009: 59.9 [58.8,60.9]; 2010-2014: 58.0 [57.1,58.9]; 2015-2019: 58.2 [56.8,59.7]). Date of entry of diagnosis (Method 4) was unreliable in this CPRD cohort. The final methods (Method 2, 3 and 5) appeared to solve the main theoretical problem but residual bias may have remained., Conclusions: We conclude that immortal time bias is a significant issue for studies of life-long conditions that use electronic health record data and requires careful consideration of how clinical diagnoses are entered onto electronic health record systems., (© 2022. The Author(s).)

Background: The effect of policy initiatives and deprivation on mortality disparities in people with intellectual disabilities is not clear., Methods: An electronic health record observational study of linked primary care data in England from the Clinical Practice Research Datalink and the Office for National Statistics deaths data from 2000 to 2019 was undertaken. All-cause and cause-specific mortality for people with intellectual disabilities were calculated by gender and deprivation status (index of multiple deprivation quintile) using direct age-standardised mortality rates (all years) and ratios (SMR; 2000-2009 vs 2010-2019)., Results: Among 1.0 million patients (n=33 844 with intellectual disability; n=980 586 general population without intellectual disability), differential mortality was consistently higher in people with intellectual disabilities and there was no evidence of attenuation over time. There was a dose-response relationship between all-cause mortality and lower deprivation quintile in the general population which was not observed in people with intellectual disabilities. Cause-specific SMR were consistent in both the 2000-2009 and 2010-2019 calendar periods, with a threefold increased risk of death in both males and females with intellectual disabilities (SMR ranges: 2.91-3.51). Mortality was highest from epilepsy (SMR ranges: 22.90-52.74) and aspiration pneumonia (SMR ranges: 19.31-35.44). SMRs were disproportionately high for people with intellectual disabilities living in the least deprived areas., Conclusions: People with intellectual disabilities in England continue to experience significant mortality disparities and there is no evidence that the situation is improving. Deprivation indicators may not be effective for targeting vulnerable individuals., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Accurately recorded vital status of individuals is essential when estimating cancer patient survival. When deaths are ascertained by linkage with vital statistics registers, some may be missed, and such individuals will wrongly appear to be long-term survivors, and survival will be overestimated. Interval-specific relative survival that levels off above one indicates that the survival among the cancer patients is better than expected, which could be due to the presence of immortals., Methods: We included colon cancer cases diagnosed in 1995-1999 within the 19 jurisdictions in seven countries participating in ICBP SURVMARK-2, with follow-up information available until end-2015. Interval-specific relative survival was estimated for each year following diagnosis, by country and age group at diagnosis., Results: The interval-specific relative survival levels off at 1 for all countries and age groups, with two exceptions: for the age group diagnosed at age 75 years and above in Ireland, and, to a lesser extent, in New Zealand., Conclusion: Overall, a subset of immortals are not apparent in the early years within the ICBP SURVMARK-2 study, except for possibly in Ireland. We suggest this approach as one strategy of exploring the existence of immortals, and to be part of routine checks of cancer registry data., (Crown Copyright © 2021. Published by Elsevier Ltd. All rights reserved.)

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., (© 2022. The Author(s).)

Aims: To assess the recording and accuracy of acute myocardial infarction (AMI) hospital admissions between two electronic health record databases within an English cancer population over time and understand the factors that affect case-ascertainment., Methods and Results: We identified 112 502 hospital admissions for AMI in England 2010-2017 from the Myocardial Ischaemia National Audit Project (MINAP) disease registry and hospital episode statistics (HES) for 95 509 patients with a previous cancer diagnosis up to 15 years prior to admission. Cancer diagnoses were identified from the National Cancer Registration Dataset (NCRD). We calculated the percentage of AMI admissions captured by each source and examined patient characteristics associated with source of ascertainment. Survival analysis assessed whether differences in survival between case-ascertainment sources could be explained by patient characteristics. A total of 57 265 (50.9%) AMI admissions in patients with a prior diagnosis of cancer were captured in both MINAP and HES. Patients captured in both sources were younger, more likely to have ST-segment elevation myocardial infarction and had better prognosis, with lower mortality rates up to 9 years after AMI admission compared with patients captured in only one source. The percentage of admissions captured in both data sources improved over time. Cancer characteristics (site, stage, and grade) had little effect on how AMI was captured., Conclusion: MINAP and HES define different populations of patients with AMI. However, cancer characteristics do not substantially impact on case-ascertainment. These findings support a strategy of using multiple linked data sources for observational cardio-oncological research into AMI., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

International comparison of liver cancer survival has been hampered due to varying standards and degrees for morphological verification and differences in coding practices. This article aims to compare liver cancer survival across the International Cancer Benchmarking Partnership's (ICBP) jurisdictions whilst trying to ensure that the estimates are comparable through a range of sensitivity analyses. Liver cancer incidence data from 21 jurisdictions in 7 countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the United Kingdom) were obtained from population-based registries for 1995-2014. Cases were categorised based on histological classification, age-groups, basis of diagnosis and calendar period. Age-standardised incidence rate (ASR) per 100 000 and net survival at 1 and 3 years after diagnosis were estimated. Liver cancer incidence rates increased over time across all ICBP jurisdictions, particularly for hepatocellular carcinoma (HCC) with the largest relative increase in the United Kingdom, increasing from 1.3 to 4.4 per 100 000 person-years between 1995 and 2014. Australia had the highest age-standardised 1-year and 3-year net survival for all liver cancers combined (48.7% and 28.1%, respectively) in the most recent calendar period, which was still true for morphologically verified tumours when making restrictions to ensure consistent coding and classification. Survival from liver cancers is poor in all countries. The incidence of HCC is increasing alongside the proportion of nonmicroscopically verified cases over time. Survival estimates for all liver tumours combined should be interpreted in this context. Care is needed to ensure that international comparisons are performed on appropriately comparable patients, with careful consideration of coding practice variations., (© 2021 UICC.)

A commonly reported measure when interested in the survival of cancer patients is relative survival. Relative survival circumvents issues with inaccurate cause of death information by incorporating the expected mortality rates of cancer individuals from population lifetables of the general population. A summary of the cancer population prognosis can be obtained using the marginal relative survival. To explore differences between exposure groups, such as socioeconomic groups, the difference in marginal relative survival between exposed and unexposed can be obtained and under assumptions is interpreted as the average causal effect of exposure to survival. In a modeling context, this is usually estimated by applying regression standardization as the average of the individual-specific estimates after fitting a relative survival model. Regression standardization yields an estimator that consistently estimates the causal effect under standard causal inference assumptions and if the relative survival model is correctly specified. We extend inverse probability weighting (IPW) and doubly robust standardization methods in the relative survival framework as additional valuable tools for obtaining average causal effects when correct model specification might not hold for the relative survival model. IPW yields an unbiased estimate of the average causal effect if a correctly specified model has been fitted for the exposure (propensity score) whereas doubly robust standardization requires that at least one of the propensity score model or the relative survival model is correctly specified. An example using data on melanoma is provided and a simulation study is conducted to investigate how sensitive are the methods to model misspecification, including different ways for obtaining standard errors., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Survival from lung cancer remains low, yet is the most common cancer diagnosed worldwide. With survival contrasting between the main histological groupings, small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), it is important to assess the extent that geographical differences could be from varying proportions of cancers with unspecified histology across countries. Lung cancer cases diagnosed 2010-2014, followed until 31 December 2015 were provided by cancer registries from seven countries for the ICBP SURVMARK-2 project. Multiple imputation was used to reassign cases with unspecified histology into SCLC, NSCLC and other. One-year and three-year age-standardised net survival were estimated by histology, sex, age group and country. In all, 404 617 lung cancer cases were included, of which 47 533 (11.7%) and 262 040 (64.8%) were SCLC and NSCLC. The proportion of unspecified cases varied, from 11.2% (Denmark) to 29.0% (The United Kingdom). After imputation with unspecified histology, survival variations remained: 1-year SCLC survival ranged from 28.0% (New Zealand) to 35.6% (Australia) NSCLC survival from 39.4% (The United Kingdom) to 49.5% (Australia). The largest survival change after imputation was for 1-year NSCLC (4.9 percentage point decrease). Similar variations were observed for 3-year survival. The oldest age group had lowest survival and largest decline after imputation. International variations in SCLC and NSCLC survival are only partially attributable to differences in the distribution of unspecified histology. While it is important that registries and clinicians aim to improve completeness in classifying cancers, it is likely that other factors play a larger role, including underlying risk factors, stage, comorbidity and care management which warrants investigation., (© 2021 UICC.)

Background: Population-based cancer registries strive to cover all cancer cases diagnosed within the population, but some cases will always be missed and no register is 100 % complete. Many cancer registries use death certificates to identify additional cases not captured through other routine sources, to hopefully add a large proportion of the missed cases. Cases notified through this route, who would not have been captured without death certificate information, are referred to as Death Certificate Initiated (DCI) cases. Inclusion of DCI cases in cancer registries increases completeness and is important for estimating cancer incidence. However, inclusion of DCI cases will generally lead to biased estimates of cancer survival, but the same is often also true if excluding DCI cases. Missed cases are probably not a random sample of all cancer cases, but rather cases with poor prognosis. Further, DCI cases have poorer prognosis than missed cases in general, since they have all died with cancer mentioned on the death certificates., Methods: We performed a simulation study to estimate the impact of including or excluding DCI cases on cancer survival estimates, under different scenarios., Results: We demonstrated that including DCI cases underestimates survival. The exclusion of DCI cases gives unbiased survival estimates if missed cases are a random sample of all cancer cases, while survival is overestimated if these have poorer prognosis., Conclusion: In our most extreme scenarios, with 25 % of cases initially missed, the usual practice of including DCI cases underestimated 5-year survival by at most 3 percentage points., (Crown Copyright © 2021. Published by Elsevier Ltd. All rights reserved.)

Background: Royston-Parmar flexible parametric survival models (FPMs) can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. Restricted mean survival time (RMST), or restricted mean failure time (RMFT) on the mortality scale, is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. Compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure as introduced by Andersen., Methods: In the presence of competing risks, prediction of RMFT and the expected life-years lost due to a cause of death are presented using Royston-Parmar FPMs. These can be predicted for a specific covariate pattern to facilitate interpretation in observational studies at the individual level, or at the population-level using standardisation to obtain marginal measures. Predictions are illustrated using English colorectal data and are obtained using the Stata post-estimation command, standsurv., Results: Reporting such measures facilitate interpretation of a competing risks analysis, particularly when the proportional hazards assumption is not appropriate. Standardisation provides a useful way to obtain marginal estimates to make absolute comparisons between two covariate groups. Predictions can be made at various time-points and presented visually for each cause of death to better understand the overall impact of different covariate groups., Conclusions: We describe estimation of RMFT, and expected life-years lost partitioned by each competing cause of death after fitting a single FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. These can be used to facilitate interpretation of a competing risks analysis when the proportionality assumption is in doubt.

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.

Background: This paper investigates the difference in survival of melanoma patients across stage and sex by utilising net survival measures. Metrics are presented at both the individual and population level., Methods: Flexible parametric models were fitted to estimate life-expectancy metrics to be applied to a group of 104,938 subjects with a melanoma skin cancer diagnosis from 2000 to 2017. Period analysis was used for better predictions for newly diagnosed patients, and missing-stage information was imputed for 9918 patients. Female relative survival was assigned to male subjects to demonstrate the survival discrepancies experienced between sexes., Results: At the age of 60, males diagnosed at the regional stage lose an average of 4.99 years of life compared to the general population, and females lose 4.79 years, demonstrating the sex variation in expected mortality. In 2017, males contributed 3545 more life years lost than females, and a potential 1931 life years could be preserved if sex differences in survival were eliminated., Conclusions: This study demonstrates the survival differences across population subgroups as a result of a melanoma cancer diagnosis. Females experience better prognosis across age and stage at diagnosis; however, further investigation is necessary to better understand the mechanisms behind this difference.

Mediation analysis can be applied to investigate the effect of a third variable on the pathway between an exposure and the outcome. Such applications include investigating the determinants that drive differences in cancer survival across subgroups. However, cancer disparities may be the result of complex mechanisms that involve both cancer-related and other-cause mortality differences making it difficult to identify the causing factors. Relative survival, a commonly used measure in cancer epidemiology, can be used to focus on cancer-related differences. We extended mediation analysis to the relative survival framework for exploring cancer inequalities. The marginal effects were obtained using regression standardization, after fitting a relative survival model. Contrasts of interests included both marginal relative survival and marginal all-cause survival differences between exposure groups. Such contrasts include the indirect effect due to a mediator that is identifiable under certain assumptions. A separate model was fitted for the mediator and uncertainty was estimated using parametric bootstrapping. The avoidable deaths under interventions can also be estimated to quantify the impact of eliminating differences. The methods are illustrated using data for individuals diagnosed with colon cancer. Mediation analysis within relative survival allows focus on factors that account for cancer-related differences instead of all-cause differences and helps improve our understanding on cancer inequalities., (© 2020 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Introduction: Survival from oesophageal cancer remains poor, even across high-income countries. Ongoing changes in the epidemiology of the disease highlight the need for survival assessments by its two main histological subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC)., Methods: The ICBP SURVMARK-2 project, a platform for international comparisons of cancer survival, collected cases of oesophageal cancer diagnosed 1995 to 2014, followed until 31 st December 2015, from cancer registries covering seven participating countries with similar access to healthcare (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK). 1-year and 3-year age-standardised net survival alongside incidence rates were calculated by country, subtype, sex, age group and period of diagnosis., Results: 111 894 cases of AC and 73 408 cases of SCC were included in the analysis. Marked improvements in survival were observed over the 20-year period in each country, particularly for AC, younger age groups and 1 year after diagnosis. Survival was consistently higher for both subtypes in Australia and Ireland followed by Norway, Denmark, New Zealand, the UK and Canada. During 2010 to 2014, survival was higher for AC compared with SCC, with 1-year survival ranging from 46.9% (Canada) to 54.4% (Ireland) for AC and 39.6% (Denmark) to 53.1% (Australia) for SCC., Conclusion: Marked improvements in both oesophageal AC and SCC survival suggest advances in treatment. Less marked improvements 3 years after diagnosis, among older age groups and patients with SCC, highlight the need for further advances in early detection and treatment of oesophageal cancer alongside primary prevention to reduce the overall burden from the disease., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: As part of the International Cancer Benchmarking Partnership (ICBP) SURVMARK-2 project, we provide the most recent estimates of colon and rectal cancer survival in seven high-income countries by age and stage at diagnosis., Methods: Data from 386 870 patients diagnosed during 2010-2014 from 19 cancer registries in seven countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK) were analysed. 1-year and 5-year net survival from colon and rectal cancer were estimated by stage at diagnosis, age and country, RESULTS: (One1-year) and 5-year net survival varied between (77.1% and 87.5%) 59.1% and 70.9% and (84.8% and 90.0%) 61.6% and 70.9% for colon and rectal cancer, respectively. Survival was consistently higher in Australia, Canada and Norway, with smaller proportions of patients with metastatic disease in Canada and Australia. International differences in (1-year) and 5-year survival were most pronounced for regional and distant colon cancer ranging between (86.0% and 94.1%) 62.5% and 77.5% and (40.7% and 56.4%) 8.0% and 17.3%, respectively. Similar patterns were observed for rectal cancer. Stage distribution of colon and rectal cancers by age varied across countries with marked survival differences for patients with metastatic disease and diagnosed at older ages (irrespective of stage)., Conclusions: Survival disparities for colon and rectal cancer across high-income countries are likely explained by earlier diagnosis in some countries and differences in treatment for regional and distant disease, as well as older age at diagnosis. Differences in cancer registration practice and different staging systems across countries may have impacted the comparisons., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: In population-based cancer survival studies, the most common measure to compare population groups is age-standardized marginal relative survival, which under assumptions can be interpreted as marginal net survival; the probability of surviving if it was not possible to die of causes other than the cancer under study (if the age distribution was that of a common reference population). The hypothetical nature of this definition has led to confusion and incorrect interpretation. For any measure to be fair in terms of comparing cancer survival, then differences between population groups should depend only on differences in excess mortality rates due to the cancer and not differences in other-cause mortality rates or differences in the age distribution., Methods: We propose using crude probabilities of death and all-cause survival which incorporate reference expected mortality rates. This makes it possible to obtain marginal crude probabilities and all-cause probability of death that only differ between population groups due to excess mortality rate differences. Choices have to be made regarding what reference mortality rates to use and what age distribution to standardize to., Results: We illustrate the method and some potential choices using data from England for men diagnosed with melanoma. Various marginal measures are presented and compared., Conclusions: The new measures help enhance understanding of cancer survival and are a complement to the more commonly used measures., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Age-standardization is vital in international comparison studies of cancer patient survival, but standard approaches can fail to produce estimates in the case of sparsity., Methods: The purpose of this paper is to demonstrate that using a standardization pre-weighting approach is a viable alternative approach for external age-standardization in population-based cancer data and performs well in cases of sparsity. We further de;1;scribe how the pre-weighting approach to age-standardization can be coupled with the Pohar Perme estimator in both a cohort and period analysis setting. For period analysis, we compare approaches for defining the internal age distribution. We use SEER public use data to illustrate our approach and estimate survival for Connecticut and by race to create a scenario with sufficient sparsity., Results: The pre-weighting approach gives comparable estimates to traditional age-standardization in cases with sufficient data and produces estimates throughout follow-up in cases of sparsity when a traditional approach would fail., Conclusion: International comparison studies and other national population-based survival studies that need to age-standardize estimates for comparability purposes should adopt the Pohar Perme estimator with pre-weighting. This approach avoids issues of non-estimation in the case of sparsity and will allow more consistent comparisons across the produced estimates., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest, (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Prognostic models are typically developed in studies covering long time periods. However, if more recent years have seen improvements in survival, then using the full dataset may lead to out-of-date survival predictions. Period analysis addresses this by developing the model in a subset of the data from a recent time window, but results in a reduction of sample size., Methods: We propose a new approach, called temporal recalibration, to combine the advantages of period analysis and full cohort analysis. This approach develops a model in the entire dataset and then recalibrates the baseline survival using a period analysis sample. The approaches are demonstrated utilizing a prognostic model in colon cancer built using both Cox proportional hazards and flexible parametric survival models with data from 1996-2005 from the Surveillance, Epidemiology, and End Results (SEER) Program database. Comparison of model predictions with observed survival estimates were made for new patients subsequently diagnosed in 2006 and followed-up until 2015., Results: Period analysis and temporal recalibration provided more up-to-date survival predictions that more closely matched observed survival in subsequent data than the standard full cohort models. In addition, temporal recalibration provided more precise estimates of predictor effects., Conclusion: Prognostic models are typically developed using a full cohort analysis that can result in out-of-date long-term survival estimates when survival has improved in recent years. Temporal recalibration is a simple method to address this, which can be used when developing and updating prognostic models to ensure survival predictions are more closely calibrated with the observed survival of individuals diagnosed subsequently., (© The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: The impact of cardiovascular disease (CVD) comorbidity on resection rates and survival for patients with early-stage non-small-cell lung cancer (NSCLC) is unclear. We explored if CVD comorbidity explained surgical resection rate variation and the impact on survival if resection rates increased., Methods: Cancer registry data consisted of English patients diagnosed with NSCLC from 2012 to 2016. Linked hospital records identified CVD comorbidities. We investigated resection rate variation by geographical region using funnel plots; resection and death rates using time-to-event analysis. We modelled an increased propensity for resection in regions with the lowest resection rates and estimated survival change., Results: Among 57,373 patients with Stage 1-3A NSCLC, resection rates varied considerably between regions. Patients with CVD comorbidity had lower resection rates and higher mortality rates. CVD comorbidity explained only 1.9% of the variation in resection rates. For every 100 CVD comorbid patients, increasing resection in regions with the lowest rates from 24 to 44% would result in 16 more patients resected and alive after 1 year and two fewer deaths overall., Conclusions: Variation in regional resection rate is not explained by CVD comorbidities. Increasing resection in patients with CVD comorbidity to the levels of the highest resecting region would increase 1-year survival.

Background: In population-based cancer survival studies, the event of interest is usually death due to cancer. However, other competing events may be present. Relative survival is a commonly used measure in cancer studies that circumvents problems caused by the inaccuracy of the cause of death information. A summary of the prognosis of the cancer population and potential differences between subgroups can be obtained using marginal estimates of relative survival., Methods: We utilize regression standardization to obtain marginal estimates of interest in a relative survival framework. Such measures include the standardized relative survival, standardized all-cause survival and standardized crude probabilities of death. Contrasts of these can be formed to explore differences between exposure groups and under certain assumptions are interpreted as causal effects. The difference in standardized all-cause survival can also provide an estimate for the impact of eliminating cancer-related differences between exposure groups. The potential avoidable deaths after such hypothetical scenarios can also be estimated. To illustrate the methods we use the example of survival differences across socio-economic groups for colon cancer., Results: Using relative survival, a range of marginal measures and contrasts were estimated. For these measures we either focused on cancer-related differences only or chose to incorporate both cancer and other cause differences. The impact of eliminating differences between groups was also estimated. Another useful way for quantifying that impact is the avoidable deaths under hypothetical scenarios., Conclusions: Marginal estimates within the relative survival framework provide useful summary measures and can be applied to better understand differences across exposure groups., (© The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.)