Showing papers by "Eric J. Feuer published in 2006"

Effect of Screening and Adjuvant Therapy on Mortality From Breast Cancer

Abstract: In several countries, a drop in mortality from breast cancer has been documented starting in 1975. Both early detection by mammographic screening and advances in management are plausible explanations. The National Institutes of Health have used a competitive peer review process to develop 7 independent statistical models of breast cancer incidence and mortality. A consortium of investigators used the same sources to obtain data on screening mammography, adjuvant treatment, and health benefits relating to the rate of death from breast cancer in the years 1975-2000. The use of mammographic screening in women age 40 and over increased markedly over the period 1985 to 2000. The use of adjuvant treatment depended on numerous factors beside the calendar year, including age, tumor stage, and estrogen-receptor status. The proportion of women given adjuvant treatment increased from virtually none in 1975 to approximately 80% in 2000. By 2000, half of all women were using tamoxifen. All 7 models predicted similar proportional reductions in mortality from a combination of screening and adjuvant therapy. The proportion of overall reduction in breast cancer deaths ascribed to screening ranged from 28% to 65% (median, 46%). The remaining decrease in mortality was associated with adjuvant treatment. Variation between models in the absolute contribution of screening was greater than for treatment. Combined screening and adjuvant therapy reduced breast cancer mortality by 25 to 38% (median, 30%). The proportion of decreased mortality ascribed to adjuvant treatment was 12 to 21% (median, 19%). For each of the 7 models, the combination of screening and adjuvant treatment lowered mortality slightly less than the sum of contributions from screening and adjuvant therapy alone. The investigators conclude from these findings that both mammographic screening and adjuvant treatment have helped to lower deaths from breast cancer in the United States.

Estimating average annual percent change for disease rates without assuming constant change.

Abstract: The annual percent change (APC) is often used to measure trends in disease and mortality rates, and a common estimator of this parameter uses a linear model on the log of the age-standardized rates Under the assumption of linearity on the log scale, which is equivalent to a constant change assumption, APC can be equivalently defined in three ways as transformations of either (1) the slope of the line that runs through the log of each rate, (2) the ratio of the last rate to the first rate in the series, or (3) the geometric mean of the proportional changes in the rates over the series When the constant change assumption fails then the first definition cannot be applied as is, while the second and third definitions unambiguously define the same parameter regardless of whether the assumption holds We call this parameter the percent change annualized (PCA) and propose two new estimators of it The first, the two-point estimator, uses only the first and last rates, assuming nothing about the rates in between This estimator requires fewer assumptions and is asymptotically unbiased as the size of the population gets large, but has more variability since it uses no information from the middle rates The second estimator is an adaptive one and equals the linear model estimator with a high probability when the rates are not significantly different from linear on the log scale, but includes fewer points if there are significant departures from that linearity For the two-point estimator we can use confidence intervals previously developed for ratios of directly standardized rates For the adaptive estimator, we show through simulation that the bootstrap confidence intervals give appropriate coverage

Changing patterns in breast cancer incidence trends.

Abstract: 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.

Impact of adjuvant therapy and mammography on U.S. mortality from 1975 to 2000: comparison of mortality results from the cisnet breast cancer base case analysis.

Abstract: 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.

Projecting the number of patients with colorectal carcinoma by phases of care in the US: 2000-2020

Abstract: This study provides projections of colorectal cancer prevalence by phases of care (initial, monitoring, and last year of life) to the year 2020 and describes the estimation method. Cancer prevalence by phase of care was estimated from colorectal cancer incidence and survival from the Surveillance, Epidemiology, and End Results (SEER) Program data, population estimates and projections from the US Census Bureau, and all cause mortality data from the Human Mortality Life Tables. Assumptions of constant incidence and survival were used for projections from 2000 to 2020. Modeled and directly observed patient months by phase of care were compared for 1996 −1998 to provide validation of estimates. Prevalence of colorectal cancer is estimated to increase from 1,002,786 (0.36%) patients to 1,522,348 (0.46%) patients between 2000 and 2020. The estimated number of person-months in the initial and last year of life phases of care will increase 43%, while the monitoring phase of care will increase 54%. Modeled person-months by phase of care were consistent with directly observed measures of person months by phase of care in 1996–1998. Under assumptions of current cancer control strategies we project that colorectal cancer prevalence will increase more rapidly than the US population, largely due to the aging of the US population. This suggests that considerable resources will be needed in the future for initial, continuing and last year of life treatment of colorectal cancer patients unless notable breakthroughs in primary prevention occur in the future years.

Dissemination of adjuvant multiagent chemotherapy and tamoxifen for breast cancer in the United States using estrogen receptor information: 1975-1999.

Abstract: 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 effi cacy of adjuvant systemic therapy reported in the overviews of early breast cancer clinical trials. These two inputs, dissemination and effi cacy, 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 verifi ed with treating physicians. Results: Use of multiagent chemotherapy is higher for younger 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 effi cacy in clinical trials have been adopted into practice. The dissemination and effi cacy 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

Additional common inputs for analyzing impact of adjuvant therapy and mammography on U.S. mortality.

Abstract: 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.

A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends

Abstract: 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.

Chapter 1: Modeling the Impact of Adjuvant Therapy and Screening Mammography on U.S. Breast Cancer Mortality Between 1975 and 2000: Introduction to the Problem

Abstract: The Cancer Intervention and Surveillance Modeling Network (CISNET) ( http://cisnet.cancer.gov ) is a consortium of National Cancer Institute (NCI) – sponsored investigators whose focus is modeling the impact of cancer control interventions on population trends in incidence and mortality for breast, prostate, colorectal, and lung cancer. These models are also used to project future trends and to help determine optimal cancer control strategies. Although each investigator has pursued research questions of individual interest, the breast group, consisting of seven principal investigators and their coinvestigators, agreed to collaborate to answer the following question: “ What is the Impact of Adjuvant Therapy and Screening Mammography on U.S. Breast Cancer Mortality, 1975 – 2000 ” ? The idea of this comparative model ing approach was to have each group synthesize existing, and sometimes confl icting, knowledge to build models of breast cancer treatment and screening, using certain specifi ed common population-level inputs. Because there was considerable room for variation in approaches, the desire was to use the palette of modeling to bring into better focus areas of consensus and disagreement. U.S. breast cancer mortality for women was rising slightly until 1990 but since declined 20% through 2000 and continued to decline a total of 23% (about 2.3% per year) through 2002 ( 1 ) . To better understand why these declines have occurred.(i.e., where we have been) and to target new cancer control strategies (i.e., where we are going), decomposing the population impact of interventions that began in the 1970s and 1980s is important. Population impact represents the fi nal phase of cancer research, because even if interventions have been evaluated in randomized controlled trials (RCTs), its impact in the population setting (effectiveness) may differ from that in a trial setting (effi cacy), and the dissemination of the intervention in the target population may be less than complete. The consortium used a common set of Chapter 1: Modeling the Impact of Adjuvant Therapy and Screening Mammography on U.S. Breast Cancer Mortality Between 1975 and 2000: Introduction to the Problem

Estimates of long-term survival for newly diagnosed cancer patients: a projection approach.

Abstract: BACKGROUND Patients with newly diagnosed cancer may request an estimate of their prospects for long-term survival. Unfortunately, standard estimates of survival may be outdated, because they do not reflect recent advances. The authors present a projection method that incorporates trends in survival and provides more up-to-date estimates of long-term survival for newly diagnosed patients. METHODS The projection method fits a regression model to interval relative survival and includes a parameter associated with a trend on diagnosis year. The cumulative relative survival rate (CRS) in a target year is calculated by multiplying the projected interval survival rates for that year. To investigate the predictive ability of the projection approach and to develop model-selection rules, data from the Surveillance, Epidemiology, and End Results Program and the Connecticut tumor registry were used to recreate data that were available at a particular time in the past, and those data were used to project survival for specified target years. RESULTS The projection method was better at predicting the survival of recently diagnosed patients than current methods, especially long-term survival for patients who had disease sites with an increasing and stable trend in survival. The authors predicted that the 15-year CRS for patients who were diagnosed in 2003 will be 61% for all cancer sites combined, 57% for colorectal cancer, 82% for female breast cancer, 53% for ovarian cancer, and 97% for prostate cancer. CONCLUSIONS Although the projection method was more speculative than other methods that are aligned more closely with current observed data, it offered the possibility of providing improved estimates of long-term survival for recently diagnosed patients. Caution should be used when applying these methods for cancer sites where there has been a dramatic uptake of screening, e.g., prostate cancer, for which the projected results may be overly optimistic. Cancer 2006. Published 2006 by the American Cancer Society.

Racial/ethnic patterns in lifetime and age-conditional risk estimates for selected cancers.

Abstract: BACKGROUND Estimates of the probability of developing or dying from cancer, either over a lifetime or over a specified number of years, are useful summary measures of the burden of cancer in a population. METHODS The authors used publicly available DevCan software and new, detailed, racial/ethnic data bases that were developed in the Surveillance Research Program of the National Cancer Institute to produce risk estimates for selected major cancers among American Indian/Aleut/Eskimo, black, Chinese, Filipino, native Hawaiian, Japanese, white (total, non-Hispanic), and Hispanic populations. RESULTS Japanese and non-Hispanic white men had the highest lifetime risk for developing cancer (47.94% and 47.41%, respectively), and the American Indian/Eskimo/Aleut population (excluding Alaska) had the lowest lifetime risk among men (24.30%). Among women, white and American Indian/Eskimo/Aleut (in Alaska) populations had higher lifetime risks than Japanese women, whereas American Indian/Eskimo/Aleut (excluding Alaska) women had the lowest risk. The age-conditional probabilities of developing cancer within the next 10 years among men and women age 60 years and the lifetime probabilities of dying from cancer also were reported by racial/ethnic group. CONCLUSIONS Racial/ethnic disparities in the lifetime risk of cancer may be because of differences in cancer incidence rates, but they also may reflect differential mortality rates from causes other than the cancer of interest. Furthermore, because cross-sectional incidence and mortality rates are used in calculating the DevCan lifetime risk estimates, results must be interpreted with caution when events, such as the widespread and rapid implementation of a new screening test, are known to have influenced disease rates. Cancer 2006. © 2005 American Cancer Society.