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Background: TAILORx demonstrated that women with node-negative, hormone receptor-positive, HER2-negative breast cancers and Oncotype DX recurrence scores (RS) of 0-25 had similar 9-year outcomes with endocrine vs chemo-endocrine therapy; evidence for women aged 50 years and younger and RS 16-25 was less clear. We estimated how expected changes in practice following the trial might affect US costs in the initial 12 months of care (initial costs).Methods: Data from Surveillance, Epidemiology, and End Results (SEER), SEER-Medicare, and SEER-Genomic Health Inc datasets were used to estimate Oncotype DX testing and chemotherapy rates and mean initial costs pre- and post-TAILORx (in 2018 dollars), assuming all women received Oncotype DX testing and score-suggested therapy posttrial. Sensitivity analyses tested the impact on costs of assumptions about compliance with testing and score-suggested treatment and estimation methods.Results: Pretrial mean initial costs were $2.816 billion. Posttrial, Oncotype DX testing costs were projected to increase from $115 to $231 million and chemotherapy use to decrease from 25% to 17%, resulting in initial care costs of $2.766 billion, or a net savings of $49 million (1.8% decrease). A small net savings was seen under most assumptions. The one exception was if all women aged 50 years and younger with tumors with RS 16-25 elected to receive chemotherapy, initial care costs could increase by $105 million (4% increase).Conclusions: Personalizing breast cancer treatment based on tumor genetic profiles could result in small cost decreases in the initial 12 months of care. Studies are needed to evaluate the long-term costs and nonmonetary benefits of personalized cancer care. [ABSTRACT FROM AUTHOR]

Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution. [ABSTRACT FROM AUTHOR]

Background: We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates.Methods: The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, stage I breast cancer with an Oncotype DX score of 18 or lower. The primary endpoint was recurrence-free interval (RFI), including locoregional recurrence, distant recurrence, and breast cancer death. Noninferiority required the two-sided 90% confidence interval of the RFI hazard ratio (HR) for no-RT vs RT to be entirely below 1.7. Model inputs included published data. The trial was simulated 1000 times, and results were summarized as percent concluding noninferiority and mean (standard deviation) of hazard ratios for Model GE and Model M, respectively.Results: Noninferiority was demonstrated in 18.0% and 3.7% for the two models. The respective means (SD) of the RFI hazard ratios were 1.8 (0.7) and 2.4 (0.9); most were locoregional recurrences. The mean five-year RFI rates for no-RT vs RT (SD) were 92.7% (2.9%) vs 95.5% (2.2%) and 88.4% (2.0%) vs 94.5% (1.6%). Both models showed little or no difference in breast cancer-specific or overall survival. Alternative definitions of low risk based on combinations of age and grade produced similar results.Conclusions: The proposed trial was unlikely to show noninferiority of omitting radiotherapy even using alternative definitions of low-risk, as the endpoint included local recurrence. Future trials regarding radiotherapy should address absolute reduction in recurrence and impact of type of recurrence on the patient. [ABSTRACT FROM AUTHOR]

Background: Radiotherapy after breast conservation has become the standard of care. Prior meta-analyses on effects of radiotherapy predated availability of gene expression profiling (GEP) to assess recurrence risk and/or did not include all relevant outcomes. This analysis used GEP information with pooled individual-level data to evaluate the impact of omitting radiotherapy on recurrence and mortality.Methods: We considered trials that evaluated or administered radiotherapy after lumpectomy in women with low-risk breast cancer. Women included had undergone lumpectomy and were treated with hormonal therapy for stage I, ER+ and/or PR+, HER2- breast cancer with Oncotype scores no greater than 18. Recurrence-free interval (RFI), type of RFI (locoregional or distant), and breast cancer-specific and overall survival were compared between no radiotherapy and radiotherapy using adjusted Cox models. All statistical tests were two-sided.Results: The final sample included 1778 women from seven trials. Omission of radiotherapy was associated with an overall adjusted hazard ratio of 2.59 (95% confidence interval [CI] = 1.38 to 4.89, P = .003) for RFI. There was a statistically significant increase in any first locoregional recurrence (P = .001), but not distant recurrence events (P = .90), or breast cancer-specific (P = .85) or overall survival (P = .61). Five-year RFI rate was high (93.5% for no radiotherapy vs 97.9% for radiotherapy; absolute reduction = 4.4%, 95% CI = 0.7% to 8.1%, P = .03). The effects of radiotherapy varied across subgroups, with lower RFI rates for those with Oncotype scores of less than 11 (vs 11-18), older (vs younger), and ER+/PR+ status (vs other).Conclusions: Omission of radiotherapy in hormone-sensitive patients with low recurrence risk may lead to a modest increase in locoregional recurrence event rates, but does not appear to increase the rate of distant recurrence or death. [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]

Background Current estimates of the costs of cancer care in the United States are based on data from 2003 and earlier. However, incidence, survival, and practice patterns have been changing for the majority of cancers. Methods Cancer prevalence was estimated and projected by phase of care (initial year following diagnosis, continuing, and last year of life) and tumor site for 13 cancers in men and 16 cancers in women through 2020. Cancer prevalence was calculated from cancer incidence and survival models estimated from Surveillance, Epidemiology, and End Results (SEER) Program data. Annualized net costs were estimated from recent SEER–Medicare linkage data, which included claims through 2006 among beneficiaries aged 65 years and older with a cancer diagnosis. Control subjects without cancer were identified from a 5% random sample of all Medicare beneficiaries residing in the SEER areas to adjust for expenditures not related to cancer. All cost estimates were adjusted to 2010 dollars. Different scenarios for assumptions about future trends in incidence, survival, and cost were assessed with sensitivity analysis. Results Assuming constant incidence, survival, and cost, we projected 13.8 and 18.1 million cancer survivors in 2010 and 2020, respectively, with associated costs of cancer care of 124.57 and 157.77 billion 2010 US dollars. This 27% increase in medical costs reflects US population changes only. The largest increases were in the continuing phase of care for prostate cancer (42%) and female breast cancer (32%). Projections of current trends in incidence (declining) and survival (increasing) had small effects on 2020 estimates. However, if costs of care increase annually by 2% in the initial and last year of life phases of care, the total cost in 2020 is projected to be $173 billion, which represents a 39% increase from 2010. Conclusions The national cost of cancer care is substantial and expected to increase because of population changes alone. Our findings have implications for policy makers in planning and allocation of resources. [ABSTRACT FROM PUBLISHER]

Background: Despite trials of mammography and widespread use, optimal screening policy is controversial. Objective: To evaluate U.S. breast cancer screening strategies. Design: 6 models using common data elements. Data Sources: National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects. Target Population: A contemporary population cohort. Time Horizon: Lifetime. Perspective: Societal. Interventions: 20 screening strategies with varying initiation and cessation ages applied annually or biennially. Outcome Measures: Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis. Results of Base-Case Analysis: The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages. Results of Sensitivity Analysis: Varying test sensitivity or treatment patterns did not change conclusions. Limitation: Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment. Conclusion: Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. Primary Funding Source: National Cancer Institute. [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 CISNET breast cancer program is a consortium of seven research groups modeling the impact of various cancer interventions on the national trends of breast cancer incidence and mortality. Each of the modeling groups participated in a CISNET breast cancer base case analysis with the objective of assessing the impact of mammography and adjuvant therapy on breast cancer mortality between 1975 and 2000. The comparative modeling approach used to address this question allowed for a unique view into the process of modeling. Results shown here expand on those recently reported in the New England Journal of Medicine (Berry et al., N Engl J Med 2005;353:1784-92) by presenting mortality impact in several different ways to facilitate comparisons between models. Comparisons of each group's results in the context of modeling assumptions made during the process gave insight into how specific model assumptions may have affected the results. The median estimate for the percent decline in breast cancer mortality due to mammography was 15% (range of 8%-23%), and the median estimate for the percent decline in mortality due to adjuvant treatment was 19% (range of 12%-21%). A detailed discussion of the differences in modeling approaches and how those differences may have influenced the mortality results concludes the chapter. [ABSTRACT FROM AUTHOR]

Background: Clinical trials have shown tamoxifen to be effective only in women with estrogen receptor (ER)-positive tumors, in a previous model, trends in the utilization of adjuvant therapy were modeled only as a function of age and stage of the disease and not ER status. In this paper, we integrate this previous estimate on the use of adjuvant systemic therapy for breast cancer in the United States with information on ER status from the Patterns of Care (POC) data to estimate the dissemination of adjuvant therapy for women with different ER-status tumors. We also summarize efficacy of adjuvant systemic therapy reported in the over- views of early breast cancer clinical trials. These two inputs, dissemination and efficacy, are key pieces for models that investigate the effect of breast cancer adjuvant therapy on the decline of U.S. breast cancer mortality. Methods: The adjustments to the previous models are calculated using the POC data on 7116 women with breast cancer diagnosed from 1987 to 1991 and in 1995 who were randomly selected from the Surveillance, and Epidemiology, and End Results (SEER) program registries. The POC data provide more accurate information on treatment and clinical variables (e. g., ER status) than the SEER data because medical records are reabstracted and further verified with treating physicians. Results: Use of multiagent chemotherapy is higher for younger women, (<50 years) and for women whose tumors were shown to be ER negative or borderline. The use of tamoxifen is higher among older women and women with ER-positive tumors. After 1980 the combined use of multiagent chemotherapy and tamoxifen for women diagnosed with breast cancer at ages 69 or younger increased more for women whose tumors were ER status positive or unknown than ER status negative. Older women (>69 years) seem to receive almost exclusively tamoxifen irrespective of ER status, except for a small percentage of those with more advanced stages (II- and II+/IIIA) who also receive multiagent chemotherapy. Discussion: The estimated dissemination trends by ER status, based on modeling the POC data, reveal that treatment strategies with demonstrated efficacy in clinical trials have been adopted into practice. The dissemination and efficacy are the two factors necessary to input into models to determine the population impact of these therapies on U.S. breast cancer mortality. The largest decline in mortality would be expected for younger women (<60 years) with ER-positive tumors or whose tumors are of unknown status because of the largest efficacy and dissemination of adjuvant therapy in this group. [ABSTRACT FROM AUTHOR]

The article reports on the Cancer Intervention and Surveillance Modeling Network's (CISNET) investigation of the impact of adjuvant therapy and screening mammography on breast cancer mortality in the United States between 1975 and 2000. CISNET is a consortium whose focus is modeling the impact of cancer control interventions on population trends in incidence and mortality for breast cancer. The results of these investigations are highlighted in the article.

The article discusses the works of the Cancer Intervention and Surveillance Modeling Network (CISNET) breast group in the United States. The authors analyzes the aims, results and programs initiated by CISNET to better study the mortality trends and potential treatment methods for breast cancer. The implication of the CISNET programs on women's health are also discussed.

The CISNET Breast Cancer program is a National Cancer Institute-sponsored collaboration composed of seven research groups that have modeled the impact of screening and adjuvant treatment on trends in breast cancer incidence and mortality over the period 1975-2000 (base case). This collaboration created a unique opportunity to make direct comparison of results from different models of population- based cancer screening produced in response to the same question. Comparing results in all but the most cursory way necessitates comparison of the models themselves. Previous chapters have discussed the models individual in detail. This chapter will aid the reader in understanding key areas of difference between the models. A focused analysis of differences and similarities between the models is presented with special attention paid to areas deemed most likely to contribute substantially to the results of the target analysis. [ABSTRACT FROM AUTHOR]

In estimating the impact of mammography and adjuvant treatment on U.S. breast cancer mortality rates, several parameters were common to all the Cancer Intervention and Surveillance Modeling Network (CISNET) models participating in the breast cancer base case. Models either used the parameters directly as input or calibrated their models to reproduce the common set of parameters. This chapter describes the common input parameters that are not specifically discussed elsewhere in the monograph. [ABSTRACT FROM AUTHOR]

Incidence rates for breast cancer in U.S. women have steadily increased for decades, but the reasons are not well understood. A recent upturn in these trends suggests that one component may be the effect of more aggressive screening in the population. The age-period-cohort framework, in which the temporal components associated with year of diagnosis and generation are evaluated, can assist in interpreting the elements associated with these trends. A unique approach for exploring other ways of partitioning the contribution of the different temporal components is described and applied to breast cancer incidence data (ICDO 174.0-174.9) from the Surveillance, Epidemiology and End Results (SEER) registries. Single-year intervals for age and year of diagnosis were used to fit models that provide estimates of the trends associated with the individual temporal elements. A log-linear model for age, period, and cohort was fitted using Poisson regression, and estimates of the separate time trends were calculated. The trends with period increased after 1982, when more aggressive screening began, and the trend is steeper for women older than 40 years. Cohort trends have increased steadily, although recent cohorts appear to be somewhat flat for women aged 50 years or younger, whereas the trend for those older than 50 years have continued to increase. Estimates of cohort trends in rates are also provided by extrapolating what would have occurred had there been no period trend before or after 1982, thus providing an estimate of the magnitude of the upturn that occurred after the recent emphasis on screening. [ABSTRACT FROM AUTHOR]

The authors comment on a 2016 systematic review of the use of patient preferences in designing noninferiority trials. Topics discussed include the reduction of adverse effects in an early-stage breast cancer trial that forgo axillary lymph node dissection (ALND), noninferiority margin including the balance of the decrement in disease control and improvements in adverse effects and the authors' suggestion for the use of decision models in estimating noninferiority margins.

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]

Segmented line regression models, which are composed of continuous linear phases, have been applied to describe changes in rate trend patterns. In this article, we propose a procedure to compare two segmented line regression functions, specifically to test (i) whether the two segmented line regression functions are identical or (ii) whether the two mean functions are parallel allowing different intercepts. A general form of the test statistic is described and then the permutation procedure is proposed to estimate the p-value of the test. The permutation test is compared to an approximateF-test in terms of the p-value estimation and the performance of the permutation test is studied via simulations. The tests are applied to compare female lung cancer mortality rates between two registry areas and also to compare female breast cancer mortality rates between two states. [ABSTRACT FROM AUTHOR]

Summary: Objective: Optimal US screening strategies remain controversial. We use six simulation models to evaluate screening outcomes under varying strategies. Methods: The models incorporate common data on incidence, mammography characteristics, and treatment effects. We evaluate varying initiation and cessation ages applied annually or biennially and calculate mammograms, mortality reduction (vs. no screening), false-positives, unnecessary biopsies and over-diagnosis. Results: The lifetime risk of breast cancer death starting at age 40 is 3% and is reduced by screening. Screening biennially maintains 81% (range 67% to 99%) of annual screening benefits with fewer false-positives. Biennial screening from 50-74 reduces the probability of breast cancer death from 3% to 2.3%. Screening annually from 40 to 84 only lowers mortality an additional one-half of one percent to 1.8% but requires substantially more mammograms and yields more false-positives and over-diagnosed cases. Conclusion: Decisions about screening strategy depend on preferences for benefits vs. potential harms and resource considerations. [Copyright &y& Elsevier]

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]

Background: We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000. Methods: A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer. Results: The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening. Conclusions: Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States. N Engl J Med 2005;353:1784-92. [ABSTRACT FROM AUTHOR]