Should relative survival be used with lung cancer data?

Fair comparisons of cause-specific and relative survival by accounting for the systematic removal of patients from risk-sets

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.

A new cure model that corrects for increased risk of non-cancer death: analysis of reliability and robustness, and application to real-life data

BACKGROUND

Non-cancer mortality in cancer patients may be higher than overall mortality in the general population due to a combination of factors, such as long-term adverse effects of treatments, and genetic, environmental or lifestyle-related factors. If so, conventional indicators may underestimate net survival and cure fraction. Our aim was to propose and evaluate a mixture cure survival model that takes into account the increased risk of non-cancer death for cancer patients.

METHODS

We assessed the performance of a corrected mixture cure survival model derived from a conventional mixture cure model to estimate the cure fraction, the survival of uncured patients, and the increased risk of non-cancer death in two settings of net survival estimation, grouped life-table data and individual patients' data. We measured the model's performance in terms of bias, standard deviation of the estimates and coverage rate, using an extensive simulation study. This study included reliability assessments through violation of some of the model's assumptions. We also applied the models to colon cancer data from the FRANCIM network.

RESULTS

When the assumptions were satisfied, the corrected cure model provided unbiased estimates of parameters expressing the increased risk of non-cancer death, the cure fraction, and net survival in uncured patients. No major difference was found when the model was applied to individual or grouped data. The absolute bias was < 1% for all parameters, while coverage ranged from 89 to 97%. When some of the assumptions were violated, parameter estimates appeared more robust when obtained from grouped than from individual data. As expected, the uncorrected cure model performed poorly and underestimated net survival and cure fractions in the simulation study. When applied to colon cancer real-life data, cure fractions estimated using the proposed model were higher than those in the conventional model, e.g. 5% higher in males at age 60 (57% vs. 52%).

CONCLUSIONS

The present analysis supports the use of the corrected mixture cure model, with the inclusion of increased risk of non-cancer death for cancer patients to provide better estimates of indicators based on cancer survival. These are important to public health decision-making; they improve patients' awareness and facilitate their return to normal life.

Temporal trends in lung cancer survival: a population-based study

BACKGROUND

Lung cancer is the number one cancer-related cause of death in Sweden and worldwide. In most countries, five-year survival estimates vary between 10% and 20% with evidence of improved survival over time. Over the last decades, the management of lung cancer has changed including the introduction of national guidelines, new diagnostic procedures and treatments. This study aimed to investigate temporal trends in lung cancer survival both overall and in subgroups defined by established prognostic factors (i.e., sex, stage, histopathology and smoking history).

MATERIALS AND METHODS

We estimated one-, two-, and five-year relative survival, and excess mortality, in patients diagnosed with squamous cell carcinoma or adenocarcinoma of the lung between 1995 and 2016 in Sweden. We used population-based information available in a national lung cancer research database (LCBaSe) generated by cross-linkage between the Swedish National Lung Cancer Register and several Swedish health and sociodemographic registers.

RESULTS

We included 36,935 patients diagnosed with squamous cell carcinoma or adenocarcinoma of the lung between 1995 and 2016. The overall one-, two- and five-year survival estimates increased between 1995 and 2016, from 38% to 53%, 21% to 37%, and 14% to 24%, respectively. Over the study period, we also found improved survival in subgroups, for example in patients with stages III-IV disease, patients with adenocarcinoma, and never-smokers. The excess mortality decreased over the study period, both overall and in all subgroups.

CONCLUSION

Lung cancer survival increased over time in the overall lung cancer population. Of special note was evidence of improved survival in patients with stage IV disease. Our results corroborate a previously observed global trend of improved survival in patients with lung cancer.

Mixture Cure Models in Oncology: A Tutorial and Practical Guidance

Novel cancer therapies are associated with survival patterns that differ from established therapies, which may include survival curves that plateau after a certain follow-up time point. A fraction of the patient population is then considered statistically cured and subject to the same mortality experience as the cancer-free general population. Mixture cure models have been developed to account for this characteristic. As compared to standard survival analysis, mixture cure models can often lead to profoundly different estimates of long-term survival, required for health economic evaluations. This tutorial is designed as a practical introduction to mixture cure models. Step-by-step instructions are provided for the entire implementation workflow, i.e., from gathering and combining data from different sources to fitting models using maximum likelihood estimation and model results interpretation. Two mixture cure models were developed to illustrate (1) an "uninformed" approach where the cure fraction is estimated from trial data and (2) an "informed" approach where the cure fraction is obtained from an external source (e.g., real-world data) used as an input to the model. These models were implemented in the statistical software R, with the freely available code on GitHub. The cure fraction can be estimated as an output from ("uninformed" approach) or used as an input to ("informed" approach) a mixture cure model. Mixture cure models suggest presumed estimates of long-term survival proportions, especially in instances where some fraction of patients is expected to be statistically cured. While this type of model may initially seem complex, it is straightforward to use and interpret. Mixture cure models have the potential to improve the accuracy of survival estimates for treatments associated with statistical cure, and the present tutorial outlines the interpretation and implementation of mixture cure models in R. This type of model will likely become more widely used in health economic analyses as novel cancer therapies enter the market.

Cancer outcomes research-a European challenge: measures of the cancer burden

In a mission that aims to improve cancer control throughout Europe, the European Academy of Cancer Sciences has defined two key indicators of progress: within one to two decades, overall cancer-specific 10-year survival should reach 75%, and in each country, overall cancer mortality rates should be convincingly declining. To lay the ground for assessment of progress and to promote cancer outcomes research in general, we have reviewed the most common population-based measures of the cancer burden. We emphasize the complexities and complementary approaches to measure cancer survival and the novel opportunities for improved assessment of quality of life. We propose that: incidence and mortality rates are standardized to the European population; net survival is used as the measure of prognosis but with proper adjustments for confounding when temporal trends in overall cancer survival are assessed; and cancer-specific quality of life is measured by a combination of existing questionnaires and utilizes emerging communication technologies. We conclude that all measures are important and that a meaningful interpretation also requires a deep understanding of the larger clinical and public health context.

Incorporating competing risk theory into evaluations of changes in cancer survival: making the most of cause of death and routinely linked sociodemographic data

Background

Relative survival is the most common method used for measuring survival from population-based registries. However, the relative survival concept of ‘survival as far as the cancer is concerned’ can be biased due to differing non-cancer risk of death in the population with cancer (competing risks). Furthermore, while relative survival can be stratified or standardised, for example by sex or age, adjustment for a broad range of sociodemographic variables potentially influencing survival is not possible. In this paper we propose Fine and Gray competing risks multivariable regression as a method that can assess the probability of death from cancer, incorporating competing risks and adjusting for sociodemographic confounders.

Methods

We used whole of population, person-level routinely linked Western Australian cancer registry and mortality data for individuals diagnosed from 1983 to 2011 for major cancer types combined, female breast, colorectal, prostate, lung and pancreatic cancers, and grade IV glioma. The probability of death from the index cancer (cancer death) was evaluated using Fine and Gray competing risks regression, adjusting for age, sex, Indigenous status, socio-economic status, accessibility to services, time sub-period and (for all cancers combined) cancer type.

Results

When comparing diagnoses in 2008–2011 to 1983–1987, we observed substantial decreases in the rate of cancer death for major cancer types combined (N = 192,641, − 31%), female breast (− 37%), prostate (− 76%) and colorectal cancers (− 37%). In contrast, improvements in pancreatic (− 15%) and lung cancers (− 9%), and grade IV glioma (− 24%) were less and the cumulative probability of cancer death for these cancer types remained high.

Conclusion

Considering the justifiable expectation for confounder adjustment in observational epidemiological studies, standard methods for tracking population-level changes in cancer survival are simplistic. This study demonstrates how competing risks and sociodemographic covariates can be incorporated using readily available software. While cancer has been focused on here, this technique has potential utility in survival analysis for other disease states.

Does minimum follow-up time post-diagnosis matter? An assessment of changing loss of life expectancy for people with cancer in Western Australia from 1982 to 2016

BACKGROUND

Cancer survival has improved in Western Australia (WA) over recent decades. Loss of life expectancy (LOLE) is a useful measure for assessing cancer survival at a population-level. Some previous studies estimating LOLE have required a minimum follow-up beyond diagnosis to reduce the impact of modelled extrapolation, while others have not. The first aim of this study was to assess the impact of minimum length of follow-up on LOLE estimates for people diagnosed in 2006 with female breast, colorectal, prostate, lung, cervical, combined oesophageal and stomach cancers, and melanoma. Based on these results, the second aim was to assess temporal changes in LOLE for these cancer types for diagnoses between 1982 and 2016.

METHODS

Person-level linked cancer registry and mortality data were used for invasive primary cancer diagnoses for WA residents aged 15-89 years. The analysis for aim one included cases diagnosed from 1982 to the end of 2006, followed to the end of 2006 (i.e. no minimum follow-up), 2011 (i.e. five years minimum follow-up, assuming survival) or 2016 (i.e. 10 years minimum follow-up). To achieve the second study aim, the diagnostic period was extended to the end of 2016. Life expectancy estimates were obtained after fitting flexible parametric relative survival models. Single-year age and sex-specific death rates were used as a reference to estimate LOLE and proportionate loss of life expectancy.

RESULTS

Temporal changes were not reported for prostate, cervical, oesophageal and stomach cancers or melanoma, due to differences in LOLE estimates by minimum follow-up time, or estimate imprecision. Marked reductions in LOLE were observed for female breast and colorectal cancer. There was minimal absolute reduction for lung cancer, where LOLE remained high.

CONCLUSION

This study considered the appropriateness of including recent cancer diagnoses when assessing temporal changes in LOLE, finding variation in estimates with differing minimum follow-up or high parameter uncertainty for most included cancer types. Temporal changes in LOLE in-turn reflected changes in the life expectancy of the general population, cancer detection and management. These factors must be considered when estimating and interpreting LOLE estimates.

Errors in determination of net survival: cause-specific and relative survival settings

BACKGROUND

Cause-specific and relative survival estimates differ. We aimed to examine these differences in common cancers where by possible identifying the most plausible sources of error in each estimate.

METHODS

Ten-year cause-specific and relative survival were estimated for lung, breast, prostate, ovary, oesophagus and colorectal cancers. The cause-specific survival was corrected for misclassification of cause of death. The Pohar-Perme relative survival estimator was modified by (1) correcting for differences in deaths from ischaemic heart disease (IHD) between cancers and general population; or (2) correcting the population hazard for smoking (lung cancer only).

RESULTS

For all cancers except breast and prostate, relative survival was lower than cause-specific. Correction for published error rates in cause of death gave implausible results. Correction for rates of IHD death gave slightly different relative survival estimates for lung, oesophagus and colorectal cancers. For lung cancer, when the population hazard was inflated for smoking, survival estimates were increased.

CONCLUSION

Results agreed with the consensus that relative survival is usually preferable. However, for some cancers, relative survival might be inaccurate (e.g. lung and prostate). Likely solutions include enhancing life tables to include other demographic variables than age and sex, and to stratify relative survival calculation by cause of death.

Reporting net survival in populations: a sensitivity analysis in lung cancer demonstrates the differential implications of reporting relative survival and cause-specific survival

Background

Net survival is commonly quantified as relative survival (observed survival among lung cancer patients versus expected survival among the general population) and cause-specific survival (lung cancer–specific survival among lung cancer patients). These approaches have drastically different assumptions; hence, failure to distinguish between them results in significant implications for study findings. We quantified the differences between relative and cause-specific survival when reporting net survival of patients with non-small cell lung cancer (NSCLC).

Methods

Cases of NSCLC diagnosed between 2004 and 2014 were extracted from the Surveillance, Epidemiology, and End Results database. The net survival of each stage-by-age stratum was expressed as cause-specific survival (Kaplan-Meier approach) and relative survival (Ederer II approach); percentage-point (pp) differences between the survival estimates were quantified up to 10 years postdiagnosis.

Results

Analyses included 263,894 cases. Cause-specific survival estimates were higher than relative survival estimates across all strata. Although the differences were negligible at 1 year postdiagnosis, they increased with increasing years of follow-up, up to 9.3 pp at 10 years (eg, aged 60–74 with stage I disease: 53.0% vs 43.7%). Differences in survival estimates between the methods also increased by increasing age groups (eg, at 10 years postdiagnosis: 5.1 pp for ages 18–44, 8.8 pp for ages 45–59, and 9.3 pp for ages 60–74) but decreased drastically for those aged ≥75 (3.1 pp).

Conclusion

Relative survival and cause-specific survival are not interchangeable. The type of survival estimate used in cancer studies should be specified, particularly for long-term survival.

Bladder cancer survival: Women only fare worse in the first two years after diagnosis

OBJECTIVES

It has consistently been shown that women who are diagnosed with bladder cancer have lower survival than men, but the exact mechanism remains unknown. Most studies assumed that the sex-specific mortality ratio is constant over time, possibly resulting in inaccurate estimates in various periods of follow-up. This study aimed to investigate the sex-specific excess mortality in bladder cancer patients and its variation over follow-up time.

METHODS

Observational cohort study. Using data from the population-based Netherlands Cancer Registry, we studied 24,169 patients diagnosed between 2003 and 2014 with histologically confirmed ≥T1 bladder cancer with follow-up until January 2018. We used flexible parametric relative survival models to estimate excess mortality as a function of time for each sex and to explore the effect of covariates on these functions.

RESULTS

Female patients (24%) had worse clinical tumor, node, and metastasis-stage at diagnosis and more often a nonurothelial tumor histology. The excess mortality ratio of sex was not constant over time; in the first two years after diagnosis excess mortality rates for women were higher than for men, but lower thereafter; this applied to both nonmuscle-invasive and muscle-invasive bladder cancer subgroups. Baseline differences in age, tumor, node, and metastasis-stage and histology accounted for only part of the excess mortality gap.

CONCLUSIONS

The assumption of proportional hazards over time leads to underestimation of the excess mortality ratio for women in the first two years and overestimation thereafter, when excess mortality is comparable for women and men. Clinicians should incorporate the initial sex-specific poorer outcome in their considerations regarding prognosis and treatment options for female patients, e.g., more invasive treatment and neo-adjuvant treatment. These findings also point towards a mechanism of micrometastatic disease, warranting assessment of sex-specific efficacy in randomized controlled trials on treatments in this patient population.

Differences in Cancer Survival with Relative versus Cause-Specific Approaches: An Update Using More Accurate Life Tables

BACKGROUND

We investigated differences in net cancer survival (survival observed if the only possible cause of death was the cancer under study) estimated using new approaches for relative survival (RS) and cause-specific survival (CSS).

METHODS

We used SEER data for patients diagnosed in 2000 to 2013, followed-up through December 31, 2014. For RS, we used new life tables accounting for geography and socio-economic status. For CSS, we used the SEER cause of death algorithm for attributing cancer-specific death. Estimates were compared by site, age, stage, race, and time since diagnosis.

RESULTS

Differences between 5-year RS and CSS were generally small. RS was always higher in screen-detectable cancers, for example, female breast (89.2% vs. 87.8%) and prostate (98.5% vs. 93.7%) cancers; differences increased with age or time since diagnosis. CSS was usually higher in the remaining cancer sites, particularly those related to specific risk factors, for example, cervix (70.9% vs. 68.3%) and liver (20.7% vs. 17.1%) cancers. For most cancer sites, the gap between estimates was smaller with more advanced stage.Conclusion: RS is the preferred approach to report cancer survival from registry data because cause of death may be inaccurate, particularly for older patients and long-term survivors as comorbidities increase challenges in determining cause of death. However, CSS proved to be more reliable in patients diagnosed with localized disease or cancers related to specific risk factors as general population life tables may not capture other causes of mortality.

IMPACT

Different approaches for net survival estimation should be considered depending on cancer under study.

Prognosis and cure of long-term cancer survivors: A population-based estimation

Background

Increasing evidence of cure for some neoplasms has emerged in recent years. The study aimed to estimate population‐based indicators of cancer cure.

Methods

Information on more than half a million cancer patients aged 15‐74 years collected by population‐based Italian cancer registries and mixture cure models were used to estimate the life expectancy of fatal tumors (LEFT), proportions of patients with similar death rates of the general population (cure fraction), and time to reach 5‐year conditional relative survival (CRS) >90% or 95% (time to cure).

Results

Between 1990 and 2000, the median LEFT increased >1 year for breast (from 8.1 to 9.4 years) and prostate cancers (from 5.2 to 7.4 years). Median LEFT in 1990 was >5 years for testicular cancers (5.8) and Hodgkin lymphoma (6.3) below 45 years of age. In both sexes, it was ≤0.5 years for pancreatic cancers and NHL in 1990 and in 2000. The cure fraction showed a 10% increase between 1990 and 2000. It was 95% for thyroid cancer in women, 94% for testis, 75% for prostate, 67% for breast cancers, and <20% for liver, lung, and pancreatic cancers. Time to 5‐year CRS >95% was <10 years for testis, thyroid, colon cancers, and melanoma. For breast and prostate cancers, the 5‐year CRS >90% was reached in <10 years but a small excess remained for >15 years.

Conclusions

The study findings confirmed that several cancer types are curable. Became aware of the possibility of cancer cure has relevant clinical and social impacts.

Misclassification of the actual causes of death and its impact on analysis: A case study in non-small cell lung cancer

OBJECTIVES

Cumulative incidence of lung cancer deaths (LC-CID) is an important metric to understand cancer prognosis and to determine treatment options. However, credible estimates of LC-CID rely on accurate cause-of-death coding in death certificates. Results from lung cancer screening trials estimated 15% under-reporting and 1% over-reporting of lung cancer deaths due to misclassification. This study investigated the impact of cause-of-death misclassification on the estimation of LC-CID.

MATERIALS AND METHODS

Patients with stage I/II non-small cell lung cancer (NSCLC) from the Surveillance, Epidemiology, and End Results registry were included. LC-CID was estimated using the competing-risk approach in two ways: (1) reporting observed estimates that ignore potential cause-of-death misclassification and (2) correcting for plausible misclassification rates reported in the literature (15% under-reporting and 1% over-reporting). Bias was quantified as the difference between observed and corrected 10-year LC-CIDs: positive values indicated that observed LC-CID overestimated true LC-CID, whereas negative values indicated the opposite.

RESULTS

Among 66,179 patients, the impact of over-reporting on 10-year LC-CID was negligible across all age groups. In contrast, under-reporting resulted in substantial underestimation of 10-year LC-CID. The biases increased as age increased due to higher LC-CIDs: 10-year LC-CIDs among stage I patients 18-44, 45-59, 60-74 and ≥75 years were 25%, 32%, 41%, and 50%, respectively, and the corresponding biases given the plausible misclassification rates were -4.4%, -5.6%, -7.1%, and -8.6%. Because the observed LC-CIDs among patients with stage II disease were higher than those with stage I disease, the biases were greater among stage II patients, up to -12.5% in the oldest age group.

CONCLUSIONS

In lung cancer, LC-CID may be severely underestimated due to under-reporting of lung cancer deaths, particularly among older patients or those with late-stage disease. Future studies that involve such subpopulations should present the corrected LC-CIDs based on plausible misclassification rates alongside the observed LC-CIDs.

Excess risk of dying of other causes of cured cancer patients

BACKGROUND

The proportion of patients cured of cancer is usually estimated with cure models assuming they have the same death risk as the general population. These patients, even when cured, often maintain an extra death risk compared to the overall population. Our aims were to estimate this extra risk, and to take it into account in estimating cure proportions and relative survival (RS).

METHODS

We used RS mixture model with an additional parameter expressing the extra noncancer death risk of patients, assumed constant with age. We applied the model to the SEER registries survival data (1990-1994 diagnosed patients) with colorectal, breast, and lung cancers, and followed up to 2013.

RESULTS

The estimated relative risk of death for cured patients versus the general population was 1.11 for colorectal, 1.16 for breast, and 2.17 and 2.12, respectively, for female and male lung cancers. Taking this extra risk into account leads, for all cancers, to a higher estimated proportion of cured and a lower RS of uncured patients. In addition, it leads to a higher estimated RS for all patients aged >70 years, and for lung cancer patients aged >50 years, at diagnosis.

CONCLUSIONS

Mortality of survivors not directly due to the diagnosed cancer was significantly higher than in the general population. It affected the estimates of cure proportions for all age classes and RS in the elderly.

Estimating the Net Survival of Patients with Gastric Cancer in Iran in a Relative Survival Framework

BACKGROUND

Iran is an Eastern Mediterranean region country with the highest rate of gastric cancer. The present study aimed to evaluate the 5-year net survival of patients with gastric cancer in Iran using a relative survival framework.

METHODS

In a cross-sectional study, using life-table estimation of relative survival, we reported 1- to 5-year relative survival regarding age, sex, disease stage, pathology, and adjuvant therapies via modeling excess mortality. All the analyses were done applying Stata 11.2 with a confidence level of 95%.

RESULTS

Data on 330 patients (aged 32-96 y), who were comprised of 228 (69.1%) men and 102 (30.1%) women with gastric cancer and were followed up for 10 years, were analyzed. Adenocarcinoma was the most common malignancy (281 [85.2%] patients), and 248 (75.1%) patients were at stage 3 or stage 4. The 1- and 5-year net survival rates after surgery were 67.96 (95% CI: 62.35-72.98) and 23.35 (95% CI: 17.94-29.28), respectively. Higher stages (P=0.001), older ages (P=0.007), and less use of adjuvant therapies (P<0.001) were independently associated with excess mortality.

CONCLUSION

It is recommended to use the relative survival framework to analyze the survival of cancer patients as an alternative approach not only to eliminate biases due to competing risks and their dependencies but also to estimate the cure at the population level concerning the most important individual characteristics. Our findings showed that the survival rate of gastric cancer in Iran is lower than that in most developed countries in terms of net survival.

Critical Points for Interpreting Patients' Survival Rate Using Cancer Registries: A Literature Review

Background

Survival rate is used to develop cancer control plans. However, there are limitations and biases when interpreting patient survival rate data. This study aimed to identify and account for potential biases and/or limitations on estimating survival rate to enable more effective control of cancer.

Methods

The authors searched PubMed from December 2010 to December 2015 for articles that investigated or described biases in estimating patient survival using cancer registries. Articles that only described the tendency of survival rate and investigated relationships between patient characteristics, treatment, and survival rate were excluded.

Results

In total, 50 articles met the inclusion criteria. The identified potential biases were categorized into three areas, as follows: 1) the quality of registry data (eg, the completeness of cancer patients, accuracy of data, and follow-up rates); 2) limitations related to estimated methods of survival rates (eg, misclassification of cause of death for cause-specific survival rate or a lack of comparability of background mortality for relative survival rate); and 3) the comparability of survival rates among different groups (eg, age-adjustment or patients with multiple cancers).

Conclusion

We concluded that survival rate can be suitable for answering questions related to health policy and research. Several factors should be considered when interpreting survival rates estimated using cancer registries.

Different survival analysis methods for measuring long-term outcomes of Indigenous and non-Indigenous Australian cancer patients in the presence and absence of competing risks

Background

Net survival is the most common measure of cancer prognosis and has been used to study differentials in cancer survival between ethnic or racial population subgroups. However, net survival ignores competing risks of deaths and so provides incomplete prognostic information for cancer patients, and when comparing survival between populations with different all-cause mortality. Another prognosis measure, “crude probability of death”, which takes competing risk of death into account, overcomes this limitation. Similar to net survival, it can be calculated using either life tables (using Cronin-Feuer method) or cause of death data (using Fine-Gray method). The aim of this study is two-fold: (1) to compare the multivariable results produced by different survival analysis methods; and (2) to compare the Cronin-Feuer with the Fine-Gray methods, in estimating the cancer and non-cancer death probability of both Indigenous and non-Indigenous cancer patients and the Indigenous cancer disparities.

Methods

Cancer survival was investigated for 9,595 people (18.5% Indigenous) diagnosed with cancer in the Northern Territory of Australia between 1991 and 2009. The Cox proportional hazard model along with Poisson and Fine-Gray regression were used in the multivariable analysis. The crude probabilities of cancer and non-cancer methods were estimated in two ways: first, using cause of death data with the Fine-Gray method, and second, using life tables with the Cronin-Feuer method.

Results

Multivariable regression using the relative survival, cause-specific survival, and competing risk analysis produced similar results. In the presence of competing risks, the Cronin-Feuer method produced similar results to Fine-Gray in the estimation of cancer death probability (higher Indigenous cancer death probabilities for all cancers) and non-cancer death probabilities (higher Indigenous non-cancer death probabilities for all cancers except lung cancer and head and neck cancers). Cronin-Feuer estimated much lower non-cancer death probabilities than Fine-Gray for non-Indigenous patients with head and neck cancers and lung cancers (both smoking-related cancers).

Conclusion

Despite the limitations of the Cronin-Feuer method, it is a reasonable alternative to the Fine-Gray method for assessing the Indigenous survival differential in the presence of competing risks when valid and reliable subgroup-specific life tables are available and cause of death data are unavailable or unreliable.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-016-0118-9) contains supplementary material, which is available to authorized users.

A flexible parametric approach to examining spatial variation in relative survival

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.

Incidence and Survival of urothelial carcinoma of the urinary bladder in Norway 1981-2014

Background

Urothelial carcinoma of the urinary bladder (UCB) is the 4^th most common cancer type in men in developed countries, and tumor recurrence or progression occurs in more than half of the patients. Previous studies report contradictory trends in incidence and survival over the past decades. This article describes the trends of UCB incidence and survival from 1981 to 2014, including both invasive and non-invasive UCB using data from the Cancer Registry of Norway.

Methods

In Norway, 33,761 patients were diagnosed with UCB between 1981 and 2014. Incidence and 5-year relative survival were calculated, stratified by sex, morphology, stage, age and diagnostic period. Age-period-cohort models were used to distinguish period- and cohort effects. Temporal trends were summarized by calculating the average absolute annual change in incidence and relative survival allowing for breaks in this trend by incorporating a joinpoint analysis. Excess mortality rate ratios (EMRR) quantify the relative risks by using a proportional excess hazard model.

Results

The incidence of UCB in men increased from 18.5 (1981-85) to 21.1 (1991-95) per 100 000 person-years and was rather stable thereafter (1996–2014). The incidence rates of UCB were lower in women increasing linearly from 4.7 to 6.2 over the past 34 years (p = 5.9 · 10^-7). These trends could be explained by an increase of the incidence rates of non-invasive tumors. Furthermore, the observed pattern seemed to represent a birth cohort effect. Five-year relative survival increased annually with 0.004 in men (p = 1.3 · 10^-6) and 0.003 in women (p = 4.5 · 10^-6). There is a significant increase over the past 34 years in survival of UCB in both genders for local tumors but not for advanced stages.

Conclusions

Increasing and stable incidence trends mirror little improvement in primary and secondary prevention of UCB for more than three decades. Survival proportions increased only marginally. Thus, any changes in treatment and follow-up care did not lead to notable improvement with respect to survival of the patients. High estimates of preventable cases together with large recurrence rates of this particular cancer type, demand more research on prevention guidelines, diagnostic tools and treatment for UCB.

Population-level trends in relative survival for cervical cancer

OBJECTIVE

While the last 3 decades have seen numerous advances in the treatment of cervical cancer, it remains unclear if population-level survival has improved. We examined relative survival, the ratio of survival in cervical cancer patients to matched controls over time.

STUDY DESIGN

Patients with cervical cancer diagnosed from 1983 through 2009 and recorded in the Surveillance, Epidemiology, and End Results database were examined. Survival models were adjusted for age, race, stage, year of diagnosis, and time since diagnosis. Changes in stage-specific relative survival for patients with cervical cancer compared to the general population matched by age, race, and calendar year were examined over time.

RESULTS

A total of 46,932 patients were identified. For women with stage I tumors, the excess hazard ratio for women diagnosed in 2009 was 0.91 (95% confidence interval [CI], 0.86-0.95) compared to 2000, 0.81 (95% CI, 0.73-0.91) compared to 1990, and 0.75 (95% CI, 0.64-0.88) compared to 1983. For patients with stage III tumors, the excess hazard ratios for patients diagnosed in 2009 (relative to those diagnosed in 2000, 1990, and 1983) were 0.83 (95% CI, 0.80-0.87), 0.68 (95% CI, 0.62-0.75), and 0.59 (95% CI, 0.52-0.68). Similar trends in improved survival over time were noted for women with stage II tumors. There were no statistically significant improvements in relative survival over time for women with stage IV tumors.

CONCLUSION

Relative survival has improved over time for women with stage I-III cervical cancer, but has changed little for those with metastatic disease.

Trends in relative survival for ovarian cancer from 1975 to 2011

OBJECTIVE

To examine relative survival (a metric that incorporates changes in survival within a population) in women with ovarian cancer from 1975 to 2011.

METHODS

Women diagnosed with ovarian cancer from 1975 to 2011 and recorded in the National Cancer Institute's Surveillance, Epidemiology, and End Results database were examined. Relative survival, estimated as the ratio of the observed survival of cancer patients (all-cause mortality) to the expected survival of a comparable group from the general population, was matched to the patients with the main factors that are considered to affect patient survival such as age, calendar time, and race. Hazard ratios were adjusted for age, race, year of diagnosis, time since diagnosis, and the interaction of age and years since diagnosis (except for stage II).

RESULTS

A total of 49,932 women were identified. For stage I ovarian cancer, the adjusted excess hazard ratio for death in 2006 was 0.51 (95% confidence interval [CI] 0.41-0.63) compared with those diagnosed in 1975. The reduction in excess mortality remained significant when compared with 1980 and 1985. For women with stage III-IV tumors, the excess hazard of mortality was lower in 2006 compared with all other years of study ranging from 0.49 (95% CI 0.44-0.55) compared with 1975 to 0.93 (95% CI 0.87-0.99) relative to 2000. For women aged 50-59 years, 10-year relative survival was 0.85 (99% CI 0.61-0.95) for stage I disease and 0.18 (99% CI 0.10-0.27) for stage III-IV tumors. For women aged 60-69 years, the corresponding 10-year relative survival estimates were 0.89 (99% CI 0.58-0.98) and 0.15 (99% CI 0.09-0.21).

CONCLUSION

Relative survival has improved for all stages of ovarian cancer from 1975 to 2011.

LEVEL OF EVIDENCE

II.

Temporal trends in the incidence and relative survival of non-small cell lung cancer in Canada: A population-based study

OBJECTIVES

The objective of this study was to estimate trends in incidence and relative survival ratio in patients diagnosed with invasive lung cancer in Canada over the period of 1992-2007.

MATERIALS AND METHODS

We identified patients with primary invasive non-small cell lung cancers in the Canadian Cancer Registry (CCR) dataset. Patients younger than 18 years of age were excluded in this analysis. A flexible parametric model was used to estimate one- and five-year relative survival ratios and excess mortality rate.

RESULTS

In total 182,417, patients from CCR dataset with invasive lung cancer were identified of which 57.2% (n=106,197) were male and the mean age at diagnosis was 68.8 (SD=11.0) years. The incidence rate of lung cancer decreased in men and increased in women. Although one-year relative survival ratio slightly improved over time for both genders and most age groups, five-year relative survival decreased for most of the groups.

CONCLUSIONS

Although the incidence rate of invasive lung cancer continued to decrease in men, it is increasing in women and the gap in incidence between men and women is narrowing. The one-year relative survival ratio gradually increased for most age groups over the study period, particularly for the younger age groups. Additionally, excess mortality rate is at its peak shortly after diagnosis and for the first 6 months and thereafter gradually decreases.

Cancer survival: an overview of measures, uses, and interpretation

Survival statistics are of great interest to patients, clinicians, researchers, and policy makers. Although seemingly simple, survival can be confusing: there are many different survival measures with a plethora of names and statistical methods developed to answer different questions. This paper aims to describe and disseminate different survival measures and their interpretation in less technical language. In addition, we introduce templates to summarize cancer survival statistic organized by their specific purpose: research and policy versus prognosis and clinical decision making.

The impact of state-specific life tables on relative survival

BACKGROUND

Relative survival is based on estimating excess cancer mortality in a study population compared to expected mortality of a comparable population without cancer. In the United States, expected mortality is estimated from national life tables matched by age, sex, race, and calendar year to each individual in the study population. We compared five-year relative survival using state life tables to five-year relative survival using US decennial life tables. We assessed variations by age, race, and cancer site for all cancers combined, lung, colorectal, prostate, and female breast cancers.

METHODS

We used data from 17 National Cancer Institute Surveillance, Epidemiology, and End Results Program registries, including diagnoses from January 1, 2000 to December 31, 2009 with follow-up through December 31, 2010. Five-year relative survival was calculated using US-based life tables (USLT) and state-specific life tables (SLT).

RESULTS

Differences in SLT- and USLT-based survival were generally small (SLT < 4 survival percentage points lower than USLT). Differences were higher for states with high SES and low mortality and for prostate cancer. Differences were largest for all cancers combined, colon and rectum, and prostate cancer among males aged 85+ ranging from -10 to -17 survival points for whites and +9 to +17 for blacks.

CONCLUSION

Differences between relative survival based on USLT and SLT were small and state-based estimates were less reliable than US-based estimates for older populations aged 85+. Our findings underscore the need to develop more appropriate life tables that better represent the varying mortality patterns in different populations in order to obtain accurate estimates of relative survival.

Long-term survival, prevalence, and cure of cancer: a population-based estimation for 818 902 Italian patients and 26 cancer types

Original, population-based estimates of indicators of long-term survival and cure in cancer patients are provided. More than a quarter of cancer patients in Italy have reached death rates similar to those of the general population. Nearly three quarters of them will not die as a result of cancer. These estimates are potentially helpful to health-care planners, clinicians, and patients.

Background

Persons living after a cancer diagnosis represent 4% of the whole population in high-income countries. The aim of the study was to provide estimates of indicators of long-term survival and cure for 26 cancer types, presently lacking.

Patients and methods

Data on 818 902 Italian cancer patients diagnosed at age 15–74 years in 1985–2005 were included. Proportions of patients with the same death rates of the general population (cure fractions) and those of prevalent patients who were not at risk of dying as a result of cancer (cure prevalence) were calculated, using validated mixture cure models, by cancer type, sex, and age group. We also estimated complete prevalence, conditional relative survival (CRS), time to reach 5- and 10-year CRS >95%, and proportion of patients living longer than those thresholds.

Results

The cure fractions ranged from >90% for patients aged <45 years with thyroid and testis cancers to <10% for liver and pancreatic cancers of all ages. Five- or 10-year CRS >95% were both reached in <10 years by patients with cancers of the stomach, colon–rectum, pancreas, corpus and cervix uteri, brain, and Hodgkin lymphoma. For breast cancer patients, 5- and 10-year CRSs reached >95% after 19 and 25 years, respectively, and in 15 and 18 years for prostate cancer patients. Five-year CRS remained <95% for >25 years after cancer diagnosis in patients with liver and larynx cancers, non-Hodgkin lymphoma, myeloma, and leukaemia. Overall, the cure prevalence was 67% for men and 77% for women. Therefore, 21% of male and 31% of female patients had already reached 5-year CRS >95%, whereas 18% and 25% had reached 10-year CRS >95%.

Conclusions

A quarter of Italian cancer patients can be considered cured. This observation has a high potential impact on health planning, clinical practice, and patients' perspective.

The impact of life tables adjusted for smoking on the socio-economic difference in net survival for laryngeal and lung cancer

BACKGROUND

Net survival is a key measure in cancer control, but estimates for cancers that are strongly associated with smoking may be biased. General population life tables represent background mortality in net survival, but may not adequately reflect the higher mortality experienced by smokers.

METHODS

Life tables adjusted for smoking were developed, and their impact on net survival and inequalities in net survival for laryngeal and lung cancers was examined.

RESULTS

The 5-year net survival estimated with smoking-adjusted life tables was consistently higher than the survival estimated with unadjusted life tables: 7% higher for laryngeal cancer and 1.5% higher for lung cancer. The impact of using smoking-adjusted life tables was more pronounced in affluent patients; the deprivation gap in 5-year net survival for laryngeal cancer widened by 3%, from 11% to 14%.

CONCLUSIONS

Using smoking-adjusted life tables to estimate net survival has only a small impact on the deprivation gap in survival, even when inequalities are substantial. Adjusting for the higher, smoking-related background mortality did increase the estimates of net survival for all deprivation groups, and may be more important when measuring the public health impact of differences or changes in survival, such as avoidable deaths or crude probabilities of death.

Interaction of ribosomal protein L22 with casein kinase 2α: a novel mechanism for understanding the biology of non-small cell lung cancer

Dysfunction of ribosomal proteins (RPs) may play an important role in molecular tumorigenesis, such as lung cancer, acting in extraribosomal functions. Many protein-protein interaction studies and genetic screens have confirmed the extraribosomal capacity of RPs. As reported, ribosomal protein L22 (RPL22) dysfunction could increase cancer risk. In the present study, we examined RPL22-protein complexes in lung cancer cells. Tandem affinity purification (TAP) was used to screen the RPL22-protein complexes, and GST pull-down experiments and confocal microscopy were used to assess the protein-protein interaction. The experiment of kinase assay was used to study the function of the RPL22-protein complexes. The results showed that several differentially expressed proteins were isolated and identified by LC-MS/MS, which revealed that one of the protein complexes included casein kinase 2α (CK2α). RPL22 and CK2α interact in vitro. RPL22 also inhibited CK2α substrate phosphorylation in vitro. This is the first report of the RPL22-CK2α relationship in lung cancer. Dysregulated CK2 may impact cell proliferation and apoptosis, key features of cancer cell biology. Our results indicate that RPL22 may be a candidate anticancer agent due to its CK2α-binding and -inhibitory functions in human lung cancer.

A comparison of relative and cause-specific survival by cancer site, age and time since diagnosis

Relative survival (RS) estimates are widely used by cancer registries, mainly because they do not rely on the well-documented deficiencies of cause of death information. The aim of our study was to compare 5-year cause-specific survival (CSS) estimates and 5-year RS estimates for different cancer sites by age and time since diagnosis, and discuss possible reasons for observed differences. Using data from the Cancer Registry of Norway, we identified 200,008 patients diagnosed with cancer at one of the 48 sites included in this analysis during the period 1996-2005, and followed them up until the end of 2010. CSS estimates were calculated (i) considering cause of death to be the cancer that was originally diagnosed and (ii) considering the cause of death to be a cancer within the same organ system. For most cancer sites the difference between CSS and RS estimates was small (<5%). The greatest differences were seen for rarer cancers such as mediastinum and Kaposi sarcoma. Including deaths from the same organ system in the calculation of CSS further reduced the differences for many sites. For younger age groups and shorter time since diagnosis, RS and CSS estimates tended to be similar, whereas CSS estimates tended to be lower than RS estimates with longer time since diagnosis in the oldest age groups. When compared to RS estimates CSS estimates were reliable for most of the cancer sites included in our analysis. There are, however, some exceptions where CSS estimates may not be recommended, including for rarer cancers and for patients aged 85 and above.