Population-based Cancer Studies Research Articles

Abstract A080: Improving outcomes: Addressing racial and ethnic disparities in colorectal cancer genomic research

Abstract Colorectal cancer (CRC) is the fourth most common cancer diagnosed in the United States (U.S.). This disease presents notable health disparities among racial and ethnic minorities in the U.S. Native Americans (NA) and non-Hispanic Blacks (NHB) exhibit the highest incidence and mortality rates for CRC compared to non-Hispanic Whites (NHW). These disparities are driven by a combination of factors, including socioeconomic inequalities, access to healthcare, lifestyle habits, and genetic susceptibilities. Minority populations often face delays in diagnosis and treatment, leading to advanced disease stages at presentation and poorer outcomes. Addressing these disparities through cancer genomics is essential for developing effective, personalized treatments that can improve outcomes for underrepresented populations. Comprehensive genomic studies can reveal genetic mutations and biomarkers unique to these groups, facilitating targeted therapies and preventive measures tailored to their specific needs. To address these disparities, we conducted an analysis of U.S. population-based colorectal cancer studies within 3 cancer genomic databases: the National Cancer Institute’s Genomic Data Commons, the AACR Project GENIE, and cBioPortal. We identified 13 studies suitable for inclusion, calculating incidence percentages across all racial and ethnic categories. Our analysis encompassed a total of 20,295 patients. Among the analyzed patients, the racial distribution was as follows: NHW (68.5%), Asians (5.30%), NHB (7.7%), Other (5.6%), NA (0.2%), Pacific Islander/Native Hawaiian (0.07%), and not reported (12.7%). Ethnic data was missing for 24.1% (n=4,895) of the patients. Samples were predominantly from individuals identified as Not Hispanic (68.4%), while Hispanic individuals constituted 7.5%. Our results show significant underrepresentation of NHB, NA and Hispanic patients in CRC genomic databases highlighting a disparity between expected and actual representation. These findings underscore the critical need for inclusive research practices. Addressing these disparities in cancer genomics is imperative to develop equitable, effective treatments and improve health outcomes for all populations. Ensuring diverse representation in genomic databases will enhance the understanding of genetic differences across populations, inform targeted therapies, and ultimately contribute to reducing CRC disparities. By addressing these disparities, we can move towards a future where all populations benefit equally from advancements in CRC treatment and prevention, thus improving survival rates and quality of life for minority groups. Citation Format: Gabriela Arroyo Figueroa, Tim F. Greten, Cecilia Monge. Improving outcomes: Addressing racial and ethnic disparities in colorectal cancer genomic research [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A080.

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

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

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

BackgroundIn 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. MethodsWe 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. ResultsUsing 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. ConclusionIn 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.

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

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

Cumulative menstrual months and breast cancer risk by hormone receptor status and ethnicity: The Breast Cancer Etiology in Minorities Study.

Reproductive and hormonal factors may influence breast cancer risk via endogenous estrogen exposure. Cumulative menstrual months (CMM) can be used as a surrogate measure of this exposure. Using harmonized data from four population-based breast cancer studies (7284 cases and 7242 controls), we examined ethnicity-specific associations between CMM and breast cancer risk using logistic regression, adjusting for menopausal status and other risk factors. Higher CMM was associated with increased breast cancer risk in non-Hispanic Whites, Hispanics and Asian Americans regardless of menopausal status (all FDR adjusted P trends=.0004), but not in African Americans. In premenopausal African Americans, there was a suggestive trend of lower risk with higher CMM. Stratification by body mass index (BMI) among premenopausal African American women showed a nonsignificant positive association with CMM in nonobese (BMI <30 kg/m2 ) women and a significant inverse association in obese women (OR per 50 CMM=0.56, 95% CI 0.37-0.87, Ptrend =.03). Risk patterns were similar for hormone receptor positive (HR+; ER+ or PR+) breast cancer; a positive association was found in all premenopausal and postmenopausal ethnic groups except in African Americans. HR- (ER- and PR-) breast cancer was not associated with CMM in all groups combined, except for a suggestive positive association among premenopausal Asian Americans (OR per 50 CMM=1.33, P=.07). In summary, these results add to the accumulating evidence that established reproductive and hormonal factors impact breast cancer risk differently in African American women compared to other ethnic groups, and also differently for HR- breast cancer than HR+ breast cancer.

Analysis of cancer diagnoses from 2015-2019 within Machakos County, Kenya, support establishment of Cancer Centre in 2019 likely changing referral patterns

Background: In Kenya, cancer is an increasing public health challenge, with an estimated 48,000 new cancer cases and over 33,000 deaths recorded each year. Machakos County, Kenya, recently opened a cancer centre with an aim of bringing cancer services such as surgical oncology, chemotherapy, radiotherapy, and pathological services closer to the residents of Machakos County. The county is also one of four counties selected for the universal health coverage pilot programme making it uniquely poised to inform cancer control programs at the local, national, and international levels. Methods: This study aimed to build a cancer database to enable future population-based cancer studies by reviewing cancer diagnosis records for selected major public hospitals in Machakos County from 2015-2019. Medical records data were retrieved from Machakos Level 5 Hospital and Kangundo, Matuu, Kathiani and Mwala Level 4 Hospitals. Results: A total of 522 cancer cases were recorded across the study period with more than a third (N=172, 33%) diagnosed August-December of 2019 when the Machakos Cancer Centre opened. Among the cancers diagnosed, the majority were in women (59.2%), with cervix uteri (n=106, 34.3%) followed by breast (n=62, 20.1%) as the most common cancers. For males, oesophagus (n=52, 24.4%) followed by prostate (n=43, 20.2%) were the most common cancer types. The highest crude rates per 100,000 persons were for Kangundo 67.4 and Matungulu 53.2 subcounties. Conclusion: It is clear that access to cancer care treatment will change referral patterns for residents in Machakos County and with the establishment of this database we expect to enable future population-based surveillance of the cancer burden and research studies, to inform cancer control programs.

Reference-adjusted and standardized all-cause and crude probabilities as an alternative to net survival in population-based cancer studies.

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. 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. We illustrate the method and some potential choices using data from England for men diagnosed with melanoma. Various marginal measures are presented and compared. The new measures help enhance understanding of cancer survival and are a complement to the more commonly used measures.

Semiparametric estimation of the cure fraction in population-based cancer survival analysis.

With rapid development in medical research, the treatment of diseases including cancer has progressed dramatically and those survivors may die from causes other than the one under study, especially among elderly patients. Motivated by the Surveillance, Epidemiology, and End Results (SEER) female breast cancer study, background mortality is incorporated into the mixture cure proportional hazards (MCPH) model to improve the cure fraction estimation in population-based cancer studies. Here, that patients are "cured" is defined as when the mortality rate of the individuals in diseased group returns to the same level as that expected in the general population, where the population level mortality is presented by the mortality table of the United States. The semiparametric estimation method based on the EM algorithm for the MCPH model with background mortality (MCPH+BM) is further developed and validated via comprehensive simulation studies. Real data analysis shows that the proposed semiparametric MCPH+BM model may provide more accurate estimation in population-level cancer study.

Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

SummaryIn population-based cancer studies, net survival is a crucial measure for population comparison purposes. However, alternative measures, namely the crude probability of death (CPr) and the number of life years lost (LYL) due to death according to different causes, are useful as complementary measures for reflecting different dimensions in terms of prognosis, treatment choice, or development of a control strategy. When the cause of death (COD) information is available, both measures can be estimated in competing risks setting using either cause-specific or subdistribution hazard regression models or with the pseudo-observation approach through direct modeling. We extended the pseudo-observation approach in order to model the CPr and the LYL due to different causes when information on COD is unavailable or unreliable (i.e., in relative survival setting). In a simulation study, we assessed the performance of the proposed approach in estimating regression parameters and examined models with different link functions that can provide an easier interpretation of the parameters. We showed that the pseudo-observation approach performs well for both measures and we illustrated their use on cervical cancer data from the England population-based cancer registry. A tutorial showing how to implement the method in R software is also provided.

Robustness of RNA sequencing on older formalin-fixed paraffin-embedded tissue from high-grade ovarian serous adenocarcinomas.

Formalin-fixed paraffin-embedded (FFPE) tissues are among the most widely available clinical specimens. Their potential utility as a source of RNA for transcriptome studies would greatly enhance population-based cancer studies. Although preliminary studies suggest FFPE tissue may be used for RNA sequencing, the effect of storage time on these specimens needs to be determined. We conducted this study to determine whether RNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries was present in sufficient quantity and quality for RNA-Seq analysis. FFPE tissues, stored from 7 to 32 years, were obtained from three SEER sites. RNA was extracted, quantified, quality assessed, and subjected to RNA-Seq (a whole transcriptome sequencing technology). FFPE specimens stored for longer periods of time had poorer RNA sample quality as indicated by negative correlations between specimen storage time and fragment distribution values (DV). In addition, sample contamination was a common issue among the RNA, with 41 of 67 samples having 5% to 48% bacterial contamination. However, regardless of specimen storage time and bacterial contamination, 60% of the samples yielded data that enabled gene expression quantification, identifying more than 10,000 genes, with the correlations among most biological replicates above 0.7. This study demonstrates that FFPE high-grade ovarian serous adenocarcinomas specimens stored in repositories for up to 32 years and under varying storage conditions are a promising source of RNA for RNA-Seq. We also describe certain caveats to be considered when designing RNA-Seq studies using archived FFPE tissues.

Residual tissue repositories as a resource for population-based cancer proteomic studies

BackgroundMass spectrometry-based proteomics has become a powerful tool for the identification and quantification of proteins from a wide variety of biological specimens. To date, the majority of studies utilizing tissue samples have been carried out on prospectively collected fresh frozen or optimal cutting temperature (OCT) embedded specimens. However, such specimens are often difficult to obtain, in limited in supply, and clinical information and outcomes on patients are inherently delayed as compared to banked samples. Annotated formalin fixed, paraffin embedded (FFPE) tumor tissue specimens are available for research use from a variety of tissue banks, such as from the surveillance, epidemiology and end results (SEER) registries’ residual tissue repositories. Given the wealth of outcomes information associated with such samples, the reuse of archived FFPE blocks for deep proteomic characterization with mass spectrometry technologies would provide a valuable resource for population-based cancer studies. Further, due to the widespread availability of FFPE specimens, validation of specimen integrity opens the possibility for thousands of studies that can be conducted worldwide.MethodsTo examine the suitability of the SEER repository tissues for proteomic and phosphoproteomic analysis, we analyzed 60 SEER patient samples, with time in storage ranging from 7 to 32 years; 60 samples with expression proteomics and 18 with phosphoproteomics, using isobaric labeling. Linear modeling and gene set enrichment analysis was used to evaluate the impacts of collection site and storage time.ResultsAll samples, regardless of age, yielded suitable protein mass after extraction for expression analysis and 18 samples yielded sufficient mass for phosphopeptide analysis. Although peptide, protein, and phosphopeptide identifications were reduced by 50, 20 and 76% respectively, from comparable OCT specimens, we found no statistically significant differences in protein quantitation correlating with collection site or specimen age. GSEA analysis of GO-term level measurements of protein abundance differences between FFPE and OCT embedded specimens suggest that the formalin fixation process may alter representation of protein categories in the resulting dataset.ConclusionsThese studies demonstrate that residual FFPE tissue specimens, of varying age and collection site, are a promising source of protein for proteomic investigations if paired with rigorously verified mass spectrometry workflows.

Challenges in the estimation of Net SURvival: The CENSUR working survival group

BackgroundNet survival, the survival probability that would be observed, in a hypothetical world, where the cancer of interest would be the only possible cause of death, is a key indicator in population-based cancer studies. Accounting for mortality due to other causes, it allows cross-country comparisons or trends analysis and provides a useful indicator for public health decision-making. The objective of this study was to show how the creation and formalization of a network comprising established research teams, which already had substantial and complementary experience in both cancer survival analysis and methodological development, make it possible to meet challenges and thus provide more adequate tools, to improve the quality and the comparability of cancer survival data, and to promote methodological transfers in areas of emerging interest. MethodThe Challenges in the Estimation of Net SURvival (CENSUR) working survival group is composed of international researchers highly skilled in biostatistics, methodology, and epidemiology, from different research organizations in France, the United Kingdom, Italy, Slovenia, and Canada, and involved in French (FRANCIM) and European (EUROCARE) cancer registry networks. ResultsThe expected advantages are an interdisciplinary, international, synergistic network capable of addressing problems in public health, for decision-makers at different levels; tools for those in charge of net survival analyses; a common methodology that makes unbiased cross-national comparisons of cancer survival feasible; transfer of methods for net survival estimations to other specific applications (clinical research, occupational epidemiology); and dissemination of results during an international training course. ConclusionThe formalization of the international CENSUR working survival group was motivated by a need felt by scientists conducting population-based cancer research to discuss, develop, and monitor implementation of a common methodology to analyze net survival in order to provide useful information for cancer control and cancer policy. A “team science” approach is necessary to address new challenges concerning the estimation of net survival.

A log-rank-type test to compare net survival distributions.

In population-based cancer studies, it is often interesting to compare cancer survival between different populations. However, in such studies, the exact causes of death are often unavailable or unreliable. Net survival methods were developed to overcome this difficulty. Net survival is the survival that would be observed if the disease under study was the only possible cause of death. The Pohar-Perme estimator (PPE) is a nonparametric consistent estimator of net survival. In this article, we present a log-rank-type test for comparing net survival functions (as estimated by PPE) between several groups. We put the test within the counting process framework to introduce the inverse probability weighting procedure as required by the PPE. We built a stratified version to control for categorical covariates that affect the outcome. We performed simulation studies to evaluate the performance of this test and worked an application on real data.

Non-Hodgkin Lymphoma and American Indians/Alaska Natives: A SEER Population Study from 2000 to 2012

Background: American Indians and Alaska Natives (AI/AN) currently represent approximately 2% of the US population. However, population-based cancer studies are generally limited. Utilizing the Surveillance, Epidemiology and End Results Program (SEER), we investigated the incidence, major subtype distribution, and survival of non-Hodgkin lymphomas (NHL) in the AI/NA population.Methods: We queried the SEER 2000-2012 database for AI/AN patients with NHL using ICD-O-3 codes 9590-9823. We calculated their incidence rate (case/1,000,000) and trend as well as risk ratios compared to other races, age-adjusted to the US 2000 standard population. Patient level data were extracted to perform summary statistics and calculate overall survival (OS) using STATA/IC 12.1.Results: We identified 1,135 AI/NA patients with NHL. The average age was 59.6 years (range, 20-85) and 53% were males. The distribution of the major NHL subtypes resembles that seen among the rest of the population and is as follows: diffuse large B-cell (DLBCL; 28%), marginal zone (MZL; 19%), follicular (FL; 13%), small lymphocytic/chronic lymphocytic leukemia (SLL/CLL; 10%), NHL not-otherwise specified (NOS, 4%), mantle cell (MCL; 2%), anaplastic large cell (ALCL; 2%), lymphoplasmacytic (LPL; 1%), and peripheral T-cell (PTCL; 1%). The overall incidence of NHL is 26.4 and did not significantly change over the 12-year study period (Annual Percent Change 0.0, 95% CI [-0.3, 0.3]). Compared to other races, AI/NA have lower incidence of NHL including most of the major subtypes (Table). The median OS for all types of NHL combined is 4.16 years. AI/AN OS is statistically inferior compared to Whites(Figure).Conclusions: The NHLincidence in AI/NA is lowest among the major race groups and has remained stable in the past 12 years. However their OS is inferior when compared to Whites. The distribution of major NHL subtypes and OS are similar to the rest of the population. [Display omitted] [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Robustness of Next Generation Sequencing on Older Formalin-Fixed Paraffin-Embedded Tissue.

Next Generation Sequencing (NGS) technologies are used to detect somatic mutations in tumors and study germ line variation. Most NGS studies use DNA isolated from whole blood or fresh frozen tissue. However, formalin-fixed paraffin-embedded (FFPE) tissues are one of the most widely available clinical specimens. Their potential utility as a source of DNA for NGS would greatly enhance population-based cancer studies. While preliminary studies suggest FFPE tissue may be used for NGS, the feasibility of using archived FFPE specimens in population based studies and the effect of storage time on these specimens needs to be determined. We conducted a study to determine whether DNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries Residual Tissue Repositories (RTR) was present in sufficient quantity and quality for NGS assays. Fifty-nine FFPE tissues, stored from 3 to 32 years, were obtained from three SEER RTR sites. DNA was extracted, quantified, quality assessed, and subjected to whole exome sequencing (WES). Following DNA extraction, 58 of 59 specimens (98%) yielded DNA and moved on to the library generation step followed by WES. Specimens stored for longer periods of time had significantly lower coverage of the target region (6% lower per 10 years, 95% CI: 3-10%) and lower average read depth (40x lower per 10 years, 95% CI: 18-60), although sufficient quality and quantity of WES data was obtained for data mining. Overall, 90% (53/59) of specimens provided usable NGS data regardless of storage time. This feasibility study demonstrates FFPE specimens acquired from SEER registries after varying lengths of storage time and under varying storage conditions are a promising source of DNA for NGS.

Fitting and Modeling Cure in Population-Based Cancer Studies within the Framework of Flexible Parametric Survival Models

When the mortality among a cancer patient group returns to the same level as in the general population, that is, when the patients no longer experience excess mortality, the patients still alive are considered “statistically cured”. Cure models can be used to estimate the cure proportion as well as the survival function of the “uncured”. One limitation of parametric cure models is that the functional form of the survival of the uncured has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Here we use flexible parametric survival models that incorporate cure as a special case to estimate the cure proportion and the survival of the uncured. Flexible parametric survival models use splines to model the underlying hazard function; therefore, no parametric distribution has to be specified. We have updated the stpm2 command for flexible parametric models to enable cure modeling.

Five-year trends in mortality indices among gynecological cancer patients in Canada

ObjectiveTo estimate and compare the five-year trends in excess mortality rate, net probability of death, and crude probability of death for patients diagnosed with epithelial invasive gynecological cancers in Canada. We compared these trends among ovarian, uterine, and cervical cancers. MethodsA flexible parametric model was used to estimate the three mortality indices for gynecological cancers. We incorporated age group, type of cancer, and year of diagnosis in the model to estimate these indices over a five-year period after diagnosis. ResultsIn total, 39,681 women who were diagnosed with epithelial invasive gynecological cancers were included in this analysis with a mean age of 57.9 (SD=15.1) years at diagnosis. Approximately 30% of patients were younger than 50years old at diagnosis and 45% were between 50 and 69years old. Ovarian cancer had the worst prognosis among the gynecological cancers based on all three mortality indices. ConclusionsThe three mortality indices provide a clear insight in understanding the elements of mortality in population-based cancer studies where the underlying cause of death is not correctly identified, the accuracy of death certificates varies overtime and among countries, and death tends to be a multi-factorial event.

The impact of additional life‐table variables on excess mortality estimates

Regression-based relative survival models are commonly used in population-based cancer studies to estimate the real impact on the excess mortality of covariates that influence overall mortality. Usually, the mortality observed in a study cohort is corrected by the expected mortality hazard in the general population, which is given by life tables provided by national statistics institutes. These life tables are stratified by age, sex, calendar year, and, sometimes, other demographic data (ethnicity, deprivation, and others). However, in most cases, the same demographic data are not available for the study cohort and the general population; this leads to differences between the expected mortality of the general population and that of the study cohort. More generally, the absence of some demographic variables in life tables may introduce a measurement bias into the estimation of the excess mortality. In the present article, we used a simulation approach with different plausible scenarios to evaluate the impact of an additional life-table variable on excess mortality estimates and study the extent and the direction of the biases in estimating the effect of each covariate on the excess mortality. We showed that the use of life table that lacks stratification by a variable present in the excess hazard model results in a measurement bias not only in the estimate of the effect of this variable but also, to a lesser extent, in the estimates of the effects of the other covariates included in the model. We also demonstrated this measurement bias by a population-based colorectal cancer analysis.

Adjusting for the proportion of cancer deaths in the general population when using relative survival: A sensitivity analysis

Background: Relative survival is an extensively used method in population based cancer studies as it provides a measure of survival without the need for accurate cause of death information. It gives an estimate for the probability of dying from cancer in the absence of other causes by estimating the excess mortality in the study population when compared to an external group. The external group is usually the general population within a country or state and mortality estimates are taken from national life tables that are broken down by age, sex, calendar year and, where applicable, race/ethnicity. One potential bias when using relative survival that is most often overlooked occurs when there are a high proportion of deaths due to a specific cancer in the external group. Methods: This paper uses data from the Finnish Cancer Registry to illustrate, through the use of a simple sensitivity analysis, the impact that specific cancer deaths in the population mortality figures can have on the estimate of relative survival. Results: We found that when examining specific diseases such as breast cancer and colon cancer, the proportion of deaths due to these specific cancers in the general population is so small in comparison to the total mortality that they make little difference to the relative survival estimates. However, prostate cancer proved to be an exception to this. For all cancer sites combined the sensitivity analysis illustrates a major limitation for this type of analysis, particularly with the older age groups. Conclusion: We recommend that, with a classification of diseases as wide as all cancer sites, relative survival should not be used without appropriate adjustment.

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models

BackgroundWhen the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models.MethodsHere we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified.ResultsWe have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates.ConclusionsCure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models.