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Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

Background: The American Cancer Society, Centers for Disease Control and Prevention, National Cancer Institute, and North American Association of Central Cancer Registries provide annual updates on cancer occurrence and trends by cancer type, sex, race, ethnicity, and age in the United States. This year's report highlights the cancer burden among men and women age 20-49 years.Methods: Incidence data for the years 1999 to 2015 from the Centers for Disease Control and Prevention- and National Cancer Institute-funded population-based cancer registry programs compiled by the North American Association of Central Cancer Registries and death data for the years 1999 to 2016 from the National Vital Statistics System were used. Trends in age-standardized incidence and death rates, estimated by joinpoint, were expressed as average annual percent change.Results: Overall cancer incidence rates (per 100 000) for all ages during 2011-2015 were 494.3 among male patients and 420.5 among female patients; during the same time period, incidence rates decreased 2.1% (95% confidence interval [CI] = -2.6% to -1.6%) per year in men and were stable in females. Overall cancer death rates (per 100 000) for all ages during 2012-2016 were 193.1 among male patients and 137.7 among female patients. During 2012-2016, overall cancer death rates for all ages decreased 1.8% (95% CI = -1.8% to -1.8%) per year in male patients and 1.4% (95% CI = -1.4% to -1.4%) per year in females. Important changes in trends were stabilization of thyroid cancer incidence rates in women and rapid declines in death rates for melanoma of the skin (both sexes). Among adults age 20-49 years, overall cancer incidence rates were substantially lower among men (115.3 per 100 000) than among women (203.3 per 100 000); cancers with the highest incidence rates (per 100 000) among men were colon and rectum (13.1), testis (10.7), and melanoma of the skin (9.8), and among women were breast (73.2), thyroid (28.4), and melanoma of the skin (14.1). During 2011 to 2015, the incidence of all invasive cancers combined among adults age 20-49 years decreased -0.7% (95% CI = -1.0% to -0.4%) among men and increased among women (1.3%, 95% CI = 0.7% to 1.9%). The death rate for (per 100 000) adults age 20-49 years for all cancer sites combined during 2012 to 2016 was 22.8 among men and 27.1 among women; during the same time period, death rates decreased 2.3% (95% CI = -2.4% to -2.2%) per year among men and 1.7% (95% CI = -1.8% to -1.6%) per year among women.Conclusions: Among people of all ages and ages 20-49 years, favorable as well as unfavorable trends in site-specific cancer incidence were observed, whereas trends in death rates were generally favorable. Characterizing the cancer burden may inform research and cancer-control efforts. [ABSTRACT FROM AUTHOR]

Background: The National Cancer Institute's cancer incidence estimates through 2015 from the Surveillance, Epidemiology, and End Results (SEER) registries' November 2017 submission are released in April 2018.Methods: Early estimates (February 2017) of cancer incidence rates and trends from the SEER 18 registries for diagnoses in 2000 through 2015 were evaluated with a revised delay-adjustment model, which was used to adjust for the undercount of cases in the early release. For the first time, early estimates were produced for race (whites and blacks) along with estimates for new sites: the oral cavity and pharynx, leukemia, and myeloma.Results: Model validation comparing delay-adjusted rates and trends through 2014 and using 2016 submissions showed good agreement. Differences in trends through 2015 in comparison with those through 2014 were evident. The rate of female breast cancer rose significantly from 2004 to 2015 by 0.3% per year (annual percent change [APC] = 0.3%); the prior trend through 2014 (the same magnitude) was not yet significant. The female colon and rectum cancer trend for whites became flat after previously declining. Lung and bronchus cancer for whites showed a significant decline (APC for males = -2.3%, 2012-2015; APC for females = -0.7%, 2011-2015). Thyroid cancer for black females changed from a continuous rise to a flat final segment (APC = 1.6%, not significant, 2011-2015). Both kidney and renal pelvis cancer (APC = 1.5%, 2011-2015) and childhood cancers (APC = 0.5%, 2000-2015) for white males showed a significant rise in the final segments from previously flat trends. Kidney and renal pelvis cancer for black males showed a change from a significant rise to a flat trend.Conclusions: The early release of SEER data continues to be useful as a preliminary estimate of the most current cancer incidence trends. Cancer 2018;124:2192-204. © 2018 American Cancer Society. [ABSTRACT FROM AUTHOR]

The article focuses on the initiative "Up For A Challenge (U4C)—Stimulating Innovation in Breast Cancer Genetic Epidemiology" by the American organization National Cancer Institute (NCI). Topics discussed include understanding the role of disease risk through genome-wide association studies (GWAS) of the genetic variants of breast cancer, review by National Institutes of Health (NIH) of entries, and chart on U4C entries received.

The National Cancer Institute's Surveillance, Epidemiology, and End Results Program releases research files of cancer registry data. These files include geographic information at the county level, but no finer. Access to finer geography, such as census tract identifiers, would enable richer analyses--for example, examination of health disparities across neighborhoods. To date, tract identifiers have been left off the research files because they could compromise the confidentiality of patients' identities. We present an approach to inclusion of tract identifiers based on multiply imputed, synthetic data. The idea is to build a predictive model of tract locations, given patient and tumor characteristics, and randomly simulate the tract of each patient by sampling from this model. For the predictive model, we use multivariate regression trees fitted to the latitude and longitude of the population centroid of each tract. We implement the approach in the registry data from California. The method results in synthetic data that reproduce a wide range (but not all) of analyses of census tract socioeconomic cancer disparities and have relatively low disclosure risks, which we assess by comparing individual patients' actual and synthetic tract locations. We conclude with a discussion of how synthetic data sets can be used by researchers with cancer registry data. [ABSTRACT FROM AUTHOR]

In the standard analysis of competing risks data, proportional hazards models are fit to the cause-specific hazard functions for all causes on the same time scale. These regression analyses are the foundation for predictions of cause-specific cumulative incidence functions based on combining the estimated cause-specific hazard functions. However, in predictions arising from disease registries, where only subjects with disease enter the database, disease-related mortality may be more naturally modeled on the time since diagnosis time scale while death from other causes may be more naturally modeled on the age time scale. The single time scale methodology may be biased if an incorrect time scale is employed for one of the causes and an alternative methodology is not available. We propose inferences for the cumulative incidence function in which regression models for the cause-specific hazard functions may be specified on different time scales. Using the disease registry data, the analysis of other cause mortality on the age scale requires left truncating the event time at the age of disease diagnosis, complicating the analysis. In addition, standard Martingale theory is not applicable when combining regression models on different time scales. We establish that the covariate conditional predictions are consistent and asymptotically normal using empirical process techniques and propose consistent variance estimators for constructing confidence intervals. Simulation studies show that the proposed two time scales method performs well, outperforming the single time-scale predictions when the time scale is misspecified. The methods are illustrated with stage III colon cancer data obtained from the Surveillance, Epidemiology, and End Results program of National Cancer Institute. [ABSTRACT FROM AUTHOR]

Background: The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking. Methods and Findings: We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. ‘Efficient’ (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 ‘consensus efficient’ (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient. Conclusions: Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics. [ABSTRACT FROM AUTHOR]

Background: The optimum screening policy for lung cancer is unknown. Objective: To identify efficient computed tomography (CT) screening scenarios in which relatively more lung cancer deaths are averted for fewer CT screening examinations. Design: Comparative modeling study using 5 independent models. Data Sources: The National Lung Screening Trial; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial; the Surveillance, Epidemiology, and End Results program; and the U.S. Smoking History Generator. Target Population: U.S. cohort born in 1950. Time Horizon: Cohort followed from ages 45 to 90 years. Perspective: Societal. Intervention: 576 scenarios with varying eligibility criteria (age, pack-years of smoking, years since quitting) and screening intervals. Outcome Measures: Benefits included lung cancer deaths averted or life-years gained. Harms included CT examinations, false-positive results (including those obtained from biopsy/surgery), overdiagnosed cases, and radiation-related deaths. Results of Best-Case Scenario: The most advantageous strategy was annual screening from ages 55 through 80 years for eversmokers with a smoking history of at least 30 pack-years and ex-smokers with less than 15 years since quitting. It would lead to 50% (model ranges, 45% to 54%) of cases of cancer being detected at an early stage (stage I/II), 575 screening examinations per lung cancer death averted, a 14% (range, 8.2% to 23.5%) reduction in lung cancer mortality, 497 lung cancer deaths averted, and 5250 life-years gained per the 100 000-member cohort. Harms would include 67 550 false-positive test results, 910 biopsies or surgeries for benign lesions, and 190 overdiagnosed cases of cancer (3.7% of all cases of lung cancer [model ranges, 1.4% to 8.3%]). Results of Sensitivity Analysis: The number of cancer deaths averted for the scenario varied across models between 177 and 862; the number of overdiagnosed cases of cancer varied between 72 and 426. Limitations: Scenarios assumed 100% screening adherence. Data derived from trials with short duration were extrapolated to lifetime follow-up. Conclusion: Annual CT screening for lung cancer has a favorable benefit-harm ratio for individuals aged 55 through 80 years with 30 or more pack-years' exposure to smoking. [ABSTRACT FROM AUTHOR]

BACKGROUND: Over the past several decades, the incidence of esophageal adenocarcinoma (EAC) has rapidly increased. The purpose of this analysis was to examine temporal trends in EAC incidence and mortality within the US population and, in addition, to explore these trends within subgroups of the population. METHODS: The National Cancer Institute (NCI) Surveillance, Epidemiology and End Results (SEER 9) data were used to examine incidence and incidence-based (IB) mortality in EAC from 1975 to 2009. Secular trends in incidence and IB mortality by cancer stage, sex, and race were further characterized using the NCI's Joinpoint Regression program. RESULTS: Based on SEER 9 data, EAC incidence and IB mortality continues to increase in the United States. However, since the mid-1990s, the overall rate of increase in both EAC incidence and IB mortality appears to be slowing. In addition, in early-stage cancers, there is a noticeable leveling off of IB mortality rates and divergence from incidence starting in the late 1990s. Over the study period, the average annual percentage increase in incidence was 6.1% in men and 5.9% in women. CONCLUSIONS: EAC incidence and IB mortality rates continue to rise in the United States, although at a slower rate in more recent years. In early-stage cancers, IB mortality and incidence rates have diverged primarily because IB mortality rates have plateaued beginning in the late 1990s. Although EAC continues to be less common in women, the rate of increase in EAC incidence is similar in both sexes. Cancer 2013. © 2012 American Cancer Society. [ABSTRACT FROM AUTHOR]

BACKGROUND: A study was undertaken to evaluate the temporal projection methods that are applied by the American Cancer Society to predict 4-year-ahead projections. METHODS: Cancer mortality data recorded in each year from 1969 through 2007 for the United States overall and for each state from the National Center for Health Statistics was obtained. Based on the mortality data through 2000, 2001, 2002, and 2003, Projections were made 4 years ahead to estimate the expected number of cancer deaths in 2004, 2005, 2006, 2007, respectively, in the United States and in each state, using 5 projection methods. These predictive estimates were compared to the observed number of deaths that occurred for all cancers combined and 47 cancer sites at the national level, and 21 cancer sites at the state level. RESULTS: Among the models that were compared, the joinpoint regression model with modified Bayesian information criterion selection produced estimates that are closest to the actual number of deaths. Overall, results show the 4-year-ahead projection has larger error than 3-year-ahead projection of death counts when the same method is used. However, 4-year-ahead projection from the new method performed better than the 3-year-ahead projection from the current state-space method. CONCLUSIONS: The Joinpoint method with modified Bayesian information criterion model has the smallest error of all the models considered for 4-year-ahead projection of cancer deaths to the current year for the United States overall and for each state. This method will be used by the American Cancer Society to project the number of cancer deaths starting in 2012. Cancer 2012; © 2012 American Cancer Society. [ABSTRACT FROM AUTHOR]

The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute is an authoritative source of cancer incidence statistics in the United States. The SEER program is a consortium of population-based cancer registries from different areas of the country. Each registry is charged with collecting data on all cancers that occur within its geographic area. As with any disease registry, there is a delay between the time that the disease (cancer) is first diagnosed and the time that it is reported to the registry. The SEER program has allowed for reporting delays of up to 19-months before releasing data for public use. Nevertheless, additional cases are discovered after the 19-month delay, and these cases are added in subsequent releases of the data. Further, any errors discovered are corrected in subsequent releases. Such reporting delays and corrections typically lead to underestimation of the cancer incidence rates in recent diagnosis years, making it difficult to monitor trends. In this article we study models that account for reporting delays and corrections in predicting eventual cancer counts for a diagnosis year from the preliminary counts. Previous models of this type have been studied, especially as applied to AIDS registries. We offer several additions to existing models. First, we explicitly model the reporting corrections. Second, we model the delay distribution with very general models, combining aspects of previous nonparametric-like models (i.e., models that have a separate parameter for each delay time) with more parametric models. Third, we allow random reporting-year effects in the model. Practical issues of model selection and how the data are classified are also discussed, particularly how the definition of a reporting correction may change depending on how subpopulations are defined. An example with SEER melanoma data is studied in detail. [ABSTRACT FROM AUTHOR]

Surveillance data represent a vital resource for understanding the impact of cancer control interventions on the population cancer burden. However, population cancer trends are a complex product of many factors, and estimating the contribution of any one of these factors can be challenging. Surveillance modeling is a technique for estimating the contribution of one or more interventions of interest to trends in disease incidence and mortality. In this article, we present several approaches to surveillance modeling of cancer screening interventions. We classify models as biological or epidemiological, depending on whether they model the full unobservable aspects of disease onset and progression, or models which reduce the complex process to simpler terms by summarizing portions of the disease process using mostly observed population level measures. We also describe differences between macrolevel models, microsimulation models and mechanistic models. We discuss procedures for model calibration and validation, and methods for presenting model results which are robust with respect to certain types of biased model estimates. As examples, we present several models of the impact of mammography screening on breast cancer mortality, and PSA screening on prostate cancer mortality. Both these examples are appropriate uses of surveillance modeling, even though for mammography there is extensive (although somewhat controversial) randomized trial evidence, whereas for PSA this biomarker has seen extensive use as a screening test prior to any controlled trial evidence of its efficacy. Some of the models presented here were developed as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network. [ABSTRACT FROM AUTHOR]

Presents a graph showing the incidence of testicular cancer in American males. Age group most commonly affected; Reason for the rising incidence rate of testicular cancer in white men; Speculations on an environmental factor responsible for increased incidence in white males.

An initial analysis of data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) registries shows that the age-adjusted incidence rate of breast cancer in women in the United States fell sharply (by 6.7%) in 2003, as compared with the rate in 2002. Data from 2004 showed a leveling off relative to the 2003 rate, with little additional decrease. Regression analysis showed that the decrease began in mid-2002 and had begun to level off by mid-2003. A comparison of incidence rates in 2001 with those in 2004 (omitting the years in which the incidence was changing) showed that the decrease in annual age-adjusted incidence was 8.6% (95% confidence interval [CI], 6.8 to 10.4). The decrease was evident only in women who were 50 years of age or older and was more evident in cancers that were estrogen-receptor — positive than in those that were estrogen-receptor — negative. The decrease in breast-cancer incidence seems to be temporally related to the first report of the Women's Health Initiative and the ensuing drop in the use of hormone-replacement therapy among postmenopausal women in the United States. The contributions of other causes to the change in incidence seem less likely to have played a major role but have not been excluded. [ABSTRACT FROM AUTHOR]

Summary: Patient survival is one of the most important measures of cancer patient care (the diagnosis and treatment of cancer). The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, “statistical cure”, is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Mixture cure models have been widely used to model failure time data. The models provide simultaneous estimates of the proportion of the patients cured from cancer and the distribution of the failure times for the uncured patients (latency distribution). CANSURV (CAN-cer SURVival) is a Windows software fitting both the standard survival models and the cure models to population-based cancer survival data. CANSURV can analyze both cause-specific survival data and, especially, relative survival data, which is the standard measure of net survival in population-based cancer studies. It can also fit parametric (cure) survival models to the individual data. The program is available at http://srab.cancer.gov/cansurv. The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program [Surveillance, Epidemiology and End Results Program, The Portable Survival System/Mainframe Survival System, National Cancer Institute, Bethesda, 1999.] of the National Cancer Institute, NIH is used to demonstrate the use of CANSURV program. [Copyright &y& Elsevier]