Modeling the multi-state natural history of rare diseases with heterogeneous individual patient data: A simulation study.

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.

Evaluation of Flexible Parametric Relative Survival Approaches for Enforcing Long-Term Constraints When Extrapolating All-Cause Survival.

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.

Perils of Randomized Controlled Trial Survival Extrapolation Assuming Treatment Effect Waning: Why the Distinction Between Marginal and Conditional Estimates Matters.

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.

Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time.

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.

Flexible parametric methods for calculating life expectancy in small populations.

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.

International variation in oesophageal and gastric cancer survival 2012-2014: differences by histological subtype and stage at diagnosis (an ICBP SURVMARK-2 population-based study).

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.

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

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

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

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

Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome.

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.

Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility.

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.

Immortal time bias for life-long conditions in retrospective observational studies using electronic health records.

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.

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv.

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.

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

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

Comparing the number and length of primary care consultations in people with and without intellectual disabilities and health needs: observational cohort study using electronic health records.

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.

International trends in oesophageal cancer survival by histological subtype between 1995 and 2014.

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 31st 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.

Colon and rectal cancer survival in seven high-income countries 2010-2014: variation by age and stage at diagnosis (the ICBP SURVMARK-2 project).

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.

Comparison of liver cancer incidence and survival by subtypes across seven high-income countries.

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.

The impact of reclassifying cancers of unspecified histology on international differences in survival for small cell and non-small cell lung cancer (ICBP SurvMark-2 project).

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.

Understanding the impact of sex and stage differences on melanoma cancer patient survival: a SEER-based study.

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.

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

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