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Since its release of Version 1.0 in 1998, Joinpoint software developed for cancer trend analysis by a team at the US National Cancer Institute has received a considerable attention in the trend analysis community and it became one of most widely used software for trend analysis. The paper published in Statistics in Medicine in 2000 (a previous study) describes the permutation test procedure to select the number of joinpoints, and Joinpoint Version 1.0 implemented the permutation procedure as the default model selection method and employed parametric methods for the asymptotic inference of the model parameters. Since then, various updates and extensions have been made in Joinpoint software. In this paper, we review basic features of Joinpoint, summarize important updates of Joinpoint software since its first release in 1998, and provide more information on two major enhancements. More specifically, these enhancements overcome prior limitations in both the accuracy and computational efficiency of previously used methods. The enhancements include: (i) data driven model selection methods which are generally more accurate under a broad range of data settings and more computationally efficient than the permutation test and (ii) the use of the empirical quantile method for construction of confidence intervals for the slope parameters and the location of the joinpoints, which generally provides more accurate coverage than the prior parametric methods used. We show the impact of these changes in cancer trend analysis published by the US National Cancer Institute. [ABSTRACT FROM AUTHOR]

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and noncancer aspects of a patient's health. [ABSTRACT FROM AUTHOR]

Background: Temporal trends in prostate cancer incidence and death rates have been attributed to changing patterns of screening and improved treatment (mortality only), among other factors. This study evaluated contemporary national-level trends and their relations with prostate-specific antigen (PSA) testing prevalence and explored trends in incidence according to disease characteristics with stage-specific, delay-adjusted rates.Methods: Joinpoint regression was used to examine changes in delay-adjusted prostate cancer incidence rates from population-based US cancer registries from 2000 to 2014 by age categories, race, and disease characteristics, including stage, PSA, Gleason score, and clinical extension. In addition, the analysis included trends for prostate cancer mortality between 1975 and 2015 by race and the estimation of PSA testing prevalence between 1987 and 2005. The annual percent change was calculated for periods defined by significant trend change points.Results: For all age groups, overall prostate cancer incidence rates declined approximately 6.5% per year from 2007. However, the incidence of distant-stage disease increased from 2010 to 2014. The incidence of disease according to higher PSA levels or Gleason scores at diagnosis did not increase. After years of significant decline (from 1993 to 2013), the overall prostate cancer mortality trend stabilized from 2013 to 2015.Conclusions: After a decline in PSA test usage, there has been an increased burden of late-stage disease, and the decline in prostate cancer mortality has leveled off. Cancer 2018;124:2801-2814. © 2018 American Cancer Society. [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]

Background: The European Randomized Study of Screening for Prostate Cancer (ERSPC) demonstrated that prostate-specific antigen (PSA) screening significantly reduced prostate cancer mortality (rate ratio, 0.79; 95% confidence interval, 0.69-0.91). The US Prostate, Lung, Colorectal, and Ovarian (PLCO) trial indicated no such reduction but had a wide 95% CI (rate ratio for prostate cancer mortality, 1.09; 95% CI, 0.87-1.36). Standard meta-analyses are unable to account for key differences between the trials that can impact the estimated effects of screening and the trials' point estimates.Methods: The authors calibrated 2 microsimulation models to individual-level incidence and mortality data from 238,936 men participating in the ERSPC and PLCO trials. A cure parameter for the underlying efficacy of screening was estimated by the models separately for each trial. The authors changed step-by-step major known differences in trial settings, including enrollment and attendance patterns, screening intervals, PSA thresholds, biopsy receipt, control arm contamination, and primary treatment, to reflect a more ideal protocol situation and differences between the trials.Results: Using the cure parameter estimated for the ERSPC, the models projected 19% to 21% and 6% to 8%, respectively, prostate cancer mortality reductions in the ERSPC and PLCO settings. Using this cure parameter, the models projected a reduction of 37% to 43% under annual screening with 100% attendance and biopsy compliance and no contamination. The cure parameter estimated for the PLCO trial was 0.Conclusions: The observed cancer mortality reduction in screening trials appears to be highly sensitive to trial protocol and practice settings. Accounting for these differences, the efficacy of PSA screening in the PLCO setting is not necessarily inconsistent with ERSPC results. Cancer 2018;124:1197-206. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t-distribution instead of the standard normal distribution. The second method is based on the empirical distribution of the residuals and the resampling using a uniform random sample, and its satisfactory performance is indicated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Cancer incidence rates and trends for cases diagnosed through 2014 using data reported to the Surveillance, Epidemiology, and End Results (SEER) program in February 2016 and a validation of rates and trends for cases diagnosed through 2013 and submitted in February 2015 using the November 2015 submission are reported. New cancer sites include the pancreas, kidney and renal pelvis, corpus and uterus, and childhood cancer sites for ages birth to 19 years inclusive.Methods: A new reporting delay model is presented for these estimates for more consistent results with the model used for the usual November SEER submissions, adjusting for the large case undercount in the February submission. Joinpoint regression methodology was used to assess trends. Delay-adjusted rates and trends were checked for validity between the February 2016 and November 2016 submissions.Results: Validation revealed that the delay model provides similar estimates of eventual counts using either February or November submission data. Trends declined through 2014 for prostate and colon and rectum cancer for males and females, male and female lung cancer, and cervical cancer. Thyroid cancer and liver and intrahepatic bile duct cancer increased. Pancreas (male and female) and corpus and uterus cancer demonstrated a modest increase. Slight increases occurred for male kidney and renal pelvis, and for all childhood cancer sites for ages birth to 19 years.Conclusions: Evaluating early cancer data submissions, adjusted for reporting delay, produces timely and valid incidence rates and trends. The results of the current study support using delay-adjusted February submission data for valid incidence rate and trend estimates over several data cycles. Cancer 2017;123:2524-34. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

The U.S. Preventive Services Task Force (USPSTF) recently updated their national lung screening guidelines and recommended low-dose computed tomography (LDCT) for lung cancer (LC) screening through age 80. However, the risk of overdiagnosis among older populations is a concern. Using four comparative models from the Cancer Intervention and Surveillance Modeling Network, we evaluate the overdiagnosis of the screening program recommended by USPSTF in the U.S. 1950 birth cohort. We estimate the number of LC deaths averted by screening (D) per overdiagnosed case (O), yielding the ratio D/ O, to quantify the trade-off between the harms and benefits of LDCT. We analyze 576 hypothetical screening strategies that vary by age, smoking, and screening frequency and evaluate efficient screening strategies that maximize the D/ O ratio and other metrics including D and life-years gained (LYG) per overdiagnosed case. The estimated D/ O ratio for the USPSTF screening program is 2.85 (model range: 1.5-4.5) in the 1950 birth cohort, implying LDCT can prevent ∼3 LC deaths per overdiagnosed case. This D/ O ratio increases by 22% when the program stops screening at an earlier age 75 instead of 80. Efficiency frontier analysis shows that while the most efficient screening strategies that maximize the mortality reduction (D) irrespective of overdiagnosis screen through age 80, screening strategies that stop at age 75 versus 80 produce greater efficiency in increasing life-years gained per overdiagnosed case. Given the risk of overdiagnosis with LC screening, the stopping age of screening merits further consideration when balancing benefits and harms. [ABSTRACT FROM AUTHOR]

Background: This article presents a first look at rates and trends for cases in the Surveillance, Epidemiology, and End Results (SEER) program diagnosed through 2013 using the February 2015 submission, and a validation of rates and trends from the February 2014 submission using the subsequent November 2014 submission. To the authors' knowledge, this is the second time SEER has published trends based on the early February submission. Three new cancer sites were added: cervix, thyroid, and liver/ intrahepatic bile duct.Methods: A reporting delay model adjusted for the undercount of cases, which is substantially larger for the February than the subsequent November submission, was used. Joinpoint regression methodology was used to assess trends. Delay-adjusted rates and trends were checked to assess validity between the February and November 2014 submissions.Results: The validation of rates and trends from the February and November 2014 submissions demonstrated even better agreement than the previously reported comparison between the February and November 2013 submissions, thereby affording additional confidence that the delay-adjusted February submission data can be used to produce valid estimates of incidence trends. Trends for cases diagnosed through 2013 revealed more rapid declines in female colon and rectal cancer and prostate cancer. A plateau in female melanoma trends and a slowing of the increases in thyroid cancer and male liver/intrahepatic bile duct cancer trends were observed.Conclusions: Analysis of early cancer data submissions can provide a preliminary indication of differences in incidence trends with an additional year of data. Although the delay adjustment correction adjusts for underreporting of cases, caution should be exercised when interpreting the results in this early submission. Cancer 2016;122:1579-87. © 2016 American Cancer Society. [ABSTRACT FROM AUTHOR]

BACKGROUND The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program collects and publishes population-based cancer incidence data from registries covering approximately 28% () of the US population. SEER incidence rates are released annually in April from data submitted the prior November. The time needed to identify, consolidate, clean, and submit data requires the latest diagnosis year included to be 3 years before release. Approaches, opportunities, and cautions for an earlier release of data based on a February submission are described. METHODS First, cases submitted in February for the latest diagnosis year represented 92% to 98% of those in the following November submission. A reporting delay model was used to statistically adjust counts in recent diagnosis years for cases projected in the future. February submissions required larger adjustment factors than November submissions. Second, trends were checked to assess the validity. RESULTS Most cancer sites had similar annual percent change (APC) trends for February and November 2013. Male colon and rectum cancer and female lung and bronchus cancer showed an acceleration in declining APC trends only in February. Average annual percent change (AAPC) trends for the 2 submissions were similar for all sites. CONCLUSIONS For the first time, preliminary 2012 incidence rates, based on February submissions, are provided. An accelerated decline starting in 2008 for male colon and rectum cancer rates and male lung cancer rates did not persist when 2012 data were added. An earlier release of SEER data is possible. Caution must be exercised when one is interpreting changing trends. Use of the more conservative AAPC is advised. Cancer 2015;121:2053-2062. © 2015 American Cancer Society. [ABSTRACT FROM AUTHOR]

In this paper, we propose methods to cluster groups of two-dimensional data whose mean functions are piecewise linear into several clusters with common characteristics such as the same slopes. To fit segmented line regression models with common features for each possible cluster, we use a restricted least squares method. In implementing the restricted least squares method, we estimate the maximum number of segments in each cluster by using both the permutation test method and the Bayes information criterion method and then propose to use the Bayes information criterion to determine the number of clusters. For a more effective implementation of the clustering algorithm, we propose a measure of the minimum distance worth detecting and illustrate its use in two examples. We summarize simulation results to study properties of the proposed methods and also prove the consistency of the cluster grouping estimated with a given number of clusters. The presentation and examples in this paper focus on the segmented line regression model with the ordered values of the independent variable, which has been the model of interest in cancer trend analysis, but the proposed method can be applied to a general model with design points either ordered or unordered. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level. METHODS Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously. RESULTS Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never-smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials. CONCLUSIONS The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low-dose computed tomography. Cancer 2014;120:1713-1724. © 2014 American Cancer Society. [ABSTRACT FROM AUTHOR]

Health indices provide information to the general public on the health condition of the community. They can also be used to inform the government's policy making, to evaluate the effect of a current policy or healthcare program, or for program planning and priority setting. It is a common practice that the health indices across different geographic units are ranked and the ranks are reported as fixed values. We argue that the ranks should be viewed as random and hence should be accompanied by an indication of precision (i.e., the confidence intervals). A technical difficulty in doing so is how to account for the dependence among the ranks in the construction of confidence intervals. In this paper, we propose a novel Monte Carlo method for constructing the individual and simultaneous confidence intervals of ranks for age-adjusted rates. The proposed method uses as input age-specific counts (of cases of disease or deaths) and their associated populations. We have further extended it to the case in which only the age-adjusted rates and confidence intervals are available. Finally, we demonstrate the proposed method to analyze US age-adjusted cancer incidence rates and mortality rates for cancer and other diseases by states and counties within a state using a website that will be publicly available. The results show that for rare or relatively rare disease (especially at the county level), ranks are essentially meaningless because of their large variability, while for more common disease in larger geographic units, ranks can be effectively utilized. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Cancer registries collect cancer incidence data that can be used to calculate incidence rates in a population and track changes over time. For incidence rates to be accurate, it is critical that diagnosed cases be reported in a timely manner. Registries typically allow a fixed amount of time (e.g. two years) for diagnosed cases to be reported before releasing the initial case counts for a particular diagnosis year. Inevitably, however, additional cases are reported after the initial counts are released; these extra cases are included in subsequent releases that become more complete over time, while incidence rates based on earlier releases will underestimate the true rates. Statistical methods have been developed to estimate the distribution of reporting delay (the amount of time until a diagnosed case is reported) and to correct incidence rates for underestimation due to reporting delay. Since the observed reporting delays must be less than the length of time the registry has been collecting data, most methods estimate a truncated delay distribution. These methods can be applied to a group of registries that began collecting data in the same diagnosis year. In this paper, we extend the methods to two groups of registries that began collecting data in two different diagnosis years (so that the delay distributions are truncated at different times). We apply the proposed method to data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) program, a consortium of U.S. cancer registries that includes nine registries with data collection beginning in 1981 and four registries with data collection beginning in 1992. We use the method to obtain delay-adjusted incidence rates for melanoma, liver cancer, and Hodgkin lymphoma. [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: Prostate cancer mortality rates in the United States declined by >40% between 1991 and 2005. The impact of changes in primary treatment and adjuvant and neoadjuvant hormone therapy on this decline is unknown. METHODS: The authors applied 3 independently developed models of prostate cancer natural history and disease detection under common assumptions about treatment patterns, treatment efficacy, and survival in the population. Primary treatment patterns were derived from the Surveillance, Epidemiology, and End Results registry; data on the frequency of hormone therapy were obtained from the CaPSURE (Cancer of the Prostate Strategic Urologic Research Endeavor) database; and treatment efficacy was based on estimates from randomized trials and comparative effectiveness studies of treatment alternatives. The models projected prostate cancer mortality without prostate-specific antigen screening and in the presence and absence of treatment benefit. The impact of primary treatment was expressed as a fraction of the difference between observed mortality and projected mortality in the absence of treatment benefit. RESULTS: The 3 models projected that changes in treatment explained 22% to 33% of the mortality decline by 2005. These contributions were accounted for mostly by surgery and radiation therapy, which increased in frequency until the 1990s, whereas hormone therapies contributed little to the mortality decline by 2005. Assuming that treatment benefit was less for older men, changes in treatment explained only 16% to 23% of the mortality decline by 2005. CONCLUSIONS: Changes in primary treatment explained a minority of the observed decline in prostate cancer mortality. The remainder of the decline probably was because of other interventions, such as prostate-specific antigen screening and advances in the treatment of recurrent and progressive disease. Cancer 2012. © 2012 American Cancer Society. [ABSTRACT FROM AUTHOR]

BACKGROUND: Population-based cancer registries that include patient follow-up generally provide information regarding net survival (ie, survival associated with the risk of dying of cancer in the absence of competing risks). However, registry data also can be used to calculate survival from cancer in the presence of competing risks, which is more clinically relevant. METHODS: Statistical methods were developed to predict the risk of death from cancer and other causes, as well as natural life expectancy if the patient did not have cancer based on a profile of prognostic factors including characteristics of the cancer, demographic factors, and comorbid conditions. The Surveillance, Epidemiology, and End Results (SEER) Program database was used to calculate the risk of dying of cancer. Because the risks of dying of cancer versus other causes are assumed to be independent conditional on the prognostic factors, a wide variety of independent data sources can be used to calculate the risk of death from other causes. Herein, the risk of death from other causes was estimated using SEER and Medicare claims data, and was matched to the closest fitting portion of the US life table to obtain a 'health status-adjusted age.' RESULTS: A nomogram was developed for prostate cancer as part of a Web-based Cancer Survival Query System that is targeted for use by physicians and patients to obtain information on a patient's prognosis. More nomograms currently are being developed. CONCLUSIONS: Nomograms of this type can be used as one tool to assist cancer physicians and their patients to better understand their prognosis and to weigh alternative treatment and palliative strategies. Cancer 2012. © 2012 American Cancer Society. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop life tables by smoking status removing lung cancer as a cause of death. These life tables are inputs to studies that compare the effectiveness of lung cancer treatments or interventions, and provide a way to quantify time until death from causes other than lung cancer. The study combined actuarial and statistical smoothing methods, as well as data from multiple sources, to develop separate life tables by smoking status, birth cohort, by single year of age, and by sex. For current smokers, separate life tables by smoking quintiles were developed based on the average number of cigarettes smoked per day by birth cohort. The end product is the creation of six non-lung-cancer life tables for males and six tables for females: five current smoker quintiles and one for never smokers. Tables for former smokers are linear combinations of the appropriate table based on the current smoker quintile before quitting smoking and the never smoker probabilities, plus added covariates for the smoking quit age and time since quitting. [ABSTRACT FROM AUTHOR]

To better understand the contribution of cigarette smoking, and its changing role in lung cancer, this article provides an introduction to a special issue of Risk Analysis, which considers the relationship between smoking and lung cancer death rates during the period 1975-2000 for U.S. men and women aged 30-84 years. Six models are employed, which are part of a consortium of lung cancer modelers funded by National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET). Starting with birth-cohort-specific smoking histories derived from National Health Interview Surveys, three scenarios are modeled: Actual Tobacco Control (observed trends in smoking), Complete Tobacco Control (a counter-factual lower bound on smoking rates that could have been achieved had all smoking ceased after the first Surgeon General's report in 1964), and No Tobacco Control (a counterfactual upper bound on smoking rates if smoking patterns that prevailed before the first studies in the 1950s began to inform the public about the hazards of smoking). Using these three scenarios and the lung cancer models, the number and percentage of lung cancer deaths averted from 1975-2000, among all deaths that could have been averted if tobacco control efforts been immediate and perfect, can be estimated. The variability of the results across multiple models provides a measure of the robustness of the results to model assumptions and structure. The results provide not only a portrait of the achieved impact of tobacco control on lung cancer mortality, but also the bounds of what still needs to be achieved. [ABSTRACT FROM AUTHOR]

We present methods for estimating five-year birth-cohort-specific trends in smoking behavior for individuals born between 1910 and 1984. We combine cross-sectional survey data on smoking behavior from the National Health Interview Surveys (NHIS) conducted between 1965 and 2001 into a single data set. The cumulative incidence of smoking by year of age and calendar year is constructed for each birth cohort from this data set and the effect of differential mortality on ever smoking prevalence is adjusted by modeling the ever smoking prevalence of each cohort for each survey year and back extrapolating that effect to age 30. Cumulative incidence is then scaled to match the ever smoking prevalence at age 30. Survival analyses generate the cumulative cessation among ever smokers across year of age and calendar year and are used to estimate current smoking prevalence. Data from Substance Abuse and Mental Health Services Administration (SAMHSA) National Survey on Drug Use and Health is used to divide those initiating smoking into quintiles of number of cigarettes smoked per day (CPD) and the mean CPD for each quintile in each calendar year is estimated from the NHIS data. For five-year birth cohorts of white, african-american, Hispanic and all race/ethnicity groupings of males and females born between 1910 and 1984, estimates are provided for prevalence of current and ever smoking, incidence of cessation, incidence of initiation, and the distribution of smoking duration and CPD for each calendar year and each single year of age through the year 1999. We believe that we are the first to provide birth-cohort-specific estimates of smoking behaviors for the U.S. population that include distributions of duration of smoking and number of cigarettes per day. These additional elements substantively enhance the utility of these estimates for estimating lung cancer risks. [ABSTRACT FROM AUTHOR]

To better understand the contribution of cigarette smoking, and its changing role in lung cancer, this article provides an introduction to a special issue of Risk Analysis, which considers the relationship between smoking and lung cancer death rates during the period 1975-2000 for U.S. men and women aged 30-84 years. Six models are employed, which are part of a consortium of lung cancer modelers funded by National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET). Starting with birth-cohort-specific smoking histories derived from National Health Interview Surveys, three scenarios are modeled: Actual Tobacco Control (observed trends in smoking), Complete Tobacco Control (a counterfactual lower bound on smoking rates that could have been achieved had all smoking ceased after the first Surgeon General's report in 1964), and No Tobacco Control (a counterfactual upper bound on smoking rates if smoking patterns that prevailed before the first studies in the 1950s began to inform the public about the hazards of smoking). Using these three scenarios and the lung cancer models, the number and percentage of lung cancer deaths averted from 1975-2000, among all deaths that could have been averted if tobacco control efforts been immediate and perfect, can be estimated. The variability of the results across multiple models provides a measure of the robustness of the results to model assumptions and structure. The results provide not only a portrait of the achieved impact of tobacco control on lung cancer mortality, but also the bounds of what still needs to be achieved. [ABSTRACT FROM AUTHOR]

We present methods for estimating five-year birth-cohort-specific trends in smoking behavior for individuals born between 1910 and 1984. We combine cross-sectional survey data on smoking behavior from the National Health Interview Surveys (NHIS) conducted between 1965 and 2001 into a single data set. The cumulative incidence of smoking by year of age and calendar year is constructed for each birth cohort from this data set and the effect of differential mortality on ever smoking prevalence is adjusted by modeling the ever smoking prevalence of each cohort for each survey year and back extrapolating that effect to age 30. Cumulative incidence is then scaled to match the ever smoking prevalence at age 30. Survival analyses generate the cumulative cessation among ever smokers across year of age and calendar year and are used to estimate current smoking prevalence. Data from Substance Abuse and Mental Health Services Administration (SAMHSA) National Survey on Drug Use and Health is used to divide those initiating smoking into quintiles of number of cigarettes smoked per day (CPD) and the mean CPD for each quintile in each calendar year is estimated from the NHIS data. For five-year birth cohorts of white, african-american, Hispanic and all race/ethnicity groupings of males and females born between 1910 and 1984, estimates are provided for prevalence of current and ever smoking, incidence of cessation, incidence of initiation, and the distribution of smoking duration and CPD for each calendar year and each single year of age through the year 1999. We believe that we are the first to provide birth-cohort-specific estimates of smoking behaviors for the U.S. population that include distributions of duration of smoking and number of cigarettes per day. These additional elements substantively enhance the utility of these estimates for estimating lung cancer risks. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop life tables by smoking status removing lung cancer as a cause of death. These life tables are inputs to studies that compare the effectiveness of lung cancer treatments or interventions, and provide a way to quantify time until death from causes other than lung cancer. The study combined actuarial and statistical smoothing methods, as well as data from multiple sources, to develop separate life tables by smoking status, birth cohort, by single year of age, and by sex. For current smokers, separate life tables by smoking quintiles were developed based on the average number of cigarettes smoked per day by birth cohort. The end product is the creation of six non-lung-cancer life tables for males and six tables for females: five current smoker quintiles and one for never smokers. Tables for former smokers are linear combinations of the appropriate table based on the current smoker quintile before quitting smoking and the never smoker probabilities, plus added covariates for the smoking quit age and time since quitting. [ABSTRACT FROM AUTHOR]

This paper describes how population cancer registry data from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute can be used to develop a prognostic model to predict the absolute risk of mortality from cancer and from other causes for an individual with specific covariates. It incorporates previously developed methods for competing risk modeling along with an imputation method to address missing cause of death information. We illustrate these approaches with colorectal cancer and evaluate the model discriminatory and calibration accuracy by time-dependent area under the receiver operating characteristic curve and calibration plot. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND. The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases. METHODS. Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age-specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay-adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted. RESULTS. The differences between the spatiotemporal model-estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method. CONCLUSIONS. The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts. Cancer 2012;. © 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]

Analysis of cumulative incidence (sometimes called absolute risk or crude risk) can be difficult if the cause of failure is missing for some subjects. Assuming missingness is random conditional on the observed data, we develop asymptotic theory for multiple imputation methods to estimate cumulative incidence. Covariates affect cause-specific hazards in our model, and we assume that separate proportional hazards models hold for each cause-specific hazard. Simulation studies show that procedures based on asymptotic theory have near nominal operating characteristics in cohorts of 200 and 400 subjects, both for cumulative incidence and for prediction error. The methods are illustrated with data on survival after breast cancer, obtained from the National Surgical Adjuvant Breast and Bowel Project (NSABP). [ABSTRACT FROM AUTHOR]

Trends in incidence or mortality rates over a specified time interval are usually described by the conventional annual per cent change (cAPC), under the assumption of a constant rate of change. When this assumption does not hold over the entire time interval, the trend may be characterized using the annual per cent changes from segmented analysis (sAPCs). This approach assumes that the change in rates is constant over each time partition defined by the transition points, but varies among different time partitions. Different groups (e.g. racial subgroups), however, may have different transition points and thus different time partitions over which they have constant rates of change, making comparison of sAPCs problematic across groups over a common time interval of interest (e.g. the past 10 years). We propose a new measure, the average annual per cent change (AAPC), which uses sAPCs to summarize and compare trends for a specific time period. The advantage of the proposed AAPC is that it takes into account the trend transitions, whereas cAPC does not and can lead to erroneous conclusions. In addition, when the trend is constant over the entire time interval of interest, the AAPC has the advantage of reducing to both cAPC and sAPC. Moreover, because the estimated AAPC is based on the segmented analysis over the entire data series, any selected subinterval within a single time partition will yield the same AAPC estimate--that is it will be equal to the estimated sAPC for that time partition. The cAPC, however, is re-estimated using data only from that selected subinterval; thus, its estimate may be sensitive to the subinterval selected. The AAPC estimation has been incorporated into the segmented regression (free) software Joinpoint, which is used by many registries throughout the world for characterizing trends in cancer rates. [ABSTRACT FROM AUTHOR]

The article focuses on the breast cancer survivors in the U.S. It says that there are 2,477,847 breast cancer survivors in the country. It also says that breast cancer continues to place a significant burden on the country's health care system. It further states that the U.S. public health programs have developed effective breast cancer screening techniques, minimizing the toxic effects of treatment and decreasing cancer recurrence.

The article discusses the findings of a clinical study that was conducted to examine the effect of treatment advances on the survival of patients with late-stage ovarian, colorectal and testicular cancers. It states that survival cure models were utilized to estimate the gain in life expectancy (GLE) accredited to a rise in the number of cured patients and to the survival of uncured patients. The study found that treatment improvement for ovarian cancer resulted in a total GLE of 2 years while the cure rate in colorectal cancer rose from 29% to 47%.

The article presents a study which determined the extent of any racial disparity in prostate-specific antigen (PSA) use by reconstructing patterns of PSA testing between black and white men in the U.S. The study used data from the 2000 National Health Interview Survey to model the adoption of PSA and to estimate the distribution of age at first test. It was found that 45% of white men and 43% of black men within ages 40-84 years had at least one PSA test by the year 2000.

The article discusses the statistical method used by the American Cancer Society (ACS) in estimating the United States and state-level cancer incidence counts for the current year. The estimated number of new cancer cases and deaths in the current year are commonly used by cancer control planners and the media. The model incorporates potential predictors of spatial and temporal variation of cancer occurrence.

Provides information on the number of new cancer cases and deaths expected in the United States in 2005. Incidence and death rates; Estimated new cases and deaths; Decrease in the mortality rates from the three most common cancer sites; Decline of cancer incidence and death rates in other racial and ethnic groups; Acceleration of progress in reducing the burden of suffering and death from cancer by applying existing cancer control knowledge; Probability of developing cancers within selected age intervals; Stages of diagnosis.

Mixture cure models are usually used to model failure time data with long-term survivors. These models have been applied to grouped survival data. The models provide simultaneous estimates of the proportion of the patients cured from disease and the distribution of the survival times for uncured patients (latency distribution). However, a crucial issue with mixture cure models is the identifiability of the cure fraction and parameters of kernel distribution. Cure fraction estimates can be quite sensitive to the choice of latency distributions and length of follow-up time. In this paper, sensitivity of parameter estimates under semi-parametric model and several most commonly used parametric models, namely lognormal, loglogistic, Weibull and generalized Gamma distributions, is explored. The cure fraction estimates from the model with generalized Gamma distribution is found to be quite robust. A simulation study was carried out to examine the effect of follow-up time and latency distribution specification on cure fraction estimation. The cure models with generalized Gamma latency distribution are applied to the population-based survival data for several cancer sites from the Surveillance, Epidemiology and End Results (SEER) Program. Several cautions on the general use of cure model are advised. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Describes a method for predicting nationwide and state-level cancer mortality counts in the United States for the calendar year. Features of the method; Characteristics of the quadratic time series model used by the American Cancer Society; Ways of using the state-space method to predict mortality counts.

Presents statistical reports on cancer in the United States for the year 2004. Number of reported cancer cases during the year; Estimated cancer cases and deaths by sex; Leading cancer types in the country; Age-standardized death rates and estimated cancer deaths from selected cancers by state.

We propose an estimator of the probability of developing a disease in a given age range, conditional on never having developed the disease prior to the beginning of the age range. Our estimator improves the one described by Wun, Merrill and Feuer ( Lifetime Data Analysis 1998; 4, 169-186) that is currently used by the U.S. National Cancer Institute for the SEER Cancer Statistics Review. Both estimators use cross-sectional disease rates and provide an interpretation of these rates in terms of the age-conditional probability of developing disease in a hypothetical cohort. The difficulty of this problem is that rates are not available per person-years alive and disease free, but only per person-years alive. Wun et al. used ad hoc methods to handle this problem which did not properly account for competing risks, did not provide a measure of variability, and only allowed age ranges using prespecified 5-year age intervals. Here we solve the problem under a unified competing risks framework, which allows the calculation of the age-conditional probabilities for any age range. We generalize gamma confidence intervals to apply to our new statistic. Although our new method provides estimates which are numerically similar to that of Wun et al., this paper provides a comprehensive theoretical basis for estimation and inference about the age-conditional probability of developing a disease. [ABSTRACT FROM AUTHOR]