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Eric J Feuer

Publications and source records attributed to Eric J Feuer.

At least 19 recordsLinked to original sources
PubMed2007

Detection of spatial clusters: application to cancer survival as a continuous outcome.

In this article, we develop the first detailed illustration of the use of a cluster detection method using a spatial scan statistic based on an exponential survival model. We use this approach to study the spatial patterns of survival of patients with stage III or stage IV colorectal cancer or with stage I/II, stage III, or stage IV lung cancer in the State of California and the County of Los Angeles (LA) diagnosed during 1988 through 2002. We present the location of the detected clusters of short survival or long survival and compute nonparametric estimates of survival inside and outside of those detected clusters confirming the survival pattern detected by the spatial scan statistic in both areas. In LA County, we investigate the possible relationship between the cluster locations and race, sex, and histology using nonparametric methods, and we compare socioeconomic factors such as education, employment, income, and health insurance inside and outside of the detected clusters. Finally, we evaluate the effect of related covariates on statistically significant long and short survival clusters detected in LA County using logistic regression models. This article illustrates a new way to understand survival patterns that may point to health disparities in terms of diagnosis and treatment patterns.

California↗
PubMed2006

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

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 Unfortunately, standard estimates of survival may be outdated, because they do not reflect recent advances.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.

Female↗
PubMed2006

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

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.

Adolescent↗
PubMed2006

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Age Distribution↗
PubMed2006

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

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 overviews 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.

Aged↗
PubMed2006

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

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.

Adult↗
PubMed2006

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

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.

Breast Neoplasms↗
PubMed2006

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.

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.

Adult↗
PubMed2006

Changing patterns in breast cancer incidence trends.

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.

Adult↗
PubMed2006

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

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.

Biometry↗
PubMed2005

CANSURV: A Windows program for population-based cancer survival analysis.

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

Computer Simulation↗
PubMed2005

Effect of screening and adjuvant therapy on mortality from breast cancer.

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.

Adult↗
PubMed2005

Modeling the dissemination of mammography in the United States.

OBJECTIVE: This paper presents a methodology for piecing together disparate data sources to obtain a comprehensive model for the use of mammography screening in the US population for the years 1975-2000. METHODS: Two aspects of mammography usage, the age that a woman receives her first mammography and the interval between subsequent mammograms, are modeled separately. The initial dissemination of mammography is based on cross-sectional self report data from national surveys and the interval length between screening exams is fit using longitudinal mammography registry data. RESULTS: The two aspects of mammography usage are combined to simulate screening histories for individual women that are representative of the US population. Simulated mammography patterns for the years 1994-2000 were found to be similar to observed screening patterns from the state level mammography registry for Vermont. CONCLUSIONS: The model presented gives insight into screening practices over time and provides an alternative public health measure for screening usage in the US population. The comprehensive description of mammography use from its introduction represents an important first step to understanding the impact of mammography on breast cancer incidence and mortality.

Age Distribution↗
PubMed2005

Geographic association between mammography use and mortality reduction in the US.

BACKGROUND: Breast cancer mortality rates in women have been declining at the same time as breast cancer incidence rates, mammography rates and use of effective adjuvant therapy have been increasing. The objective of this study was to examine population data on breast cancer screening and breast cancer mortality to see if there is any geographic association between mammographic screening and breast cancer mortality reduction in the US, after adjusting for therapy use. METHODS: Regression analysis of the estimated annual percent reduction of breast cancer mortality was performed on mammography use from the Behavioral Risk Factors Surveillance System (BRFSS) at state level. A secondary regression analysis on the SEER-11 region, at an aggregated Health Services Area (HSA) level, was carried out to adjust for use of adjuvant therapy. The annual percent change in the incidence of early stage cancer, calculated from SEER cancer data was used as a surrogate for mammography use. Adjuvant therapy use was estimated from SEER data and adjusted using the Patterns of Care data. All the analyses showed a small but significant negative correlation between mammography usage and mortality reduction (correlation of -0.285, p-value 0.045) in breast cancer (state level) and change in "early" stage breast cancer and mortality reduction (at HSA level) unadjusted (correlation of -0.307, p-value 0.065) and adjusted (partial correlation of -0.337, p-value 0.044) for adjuvant therapy use. DISCUSSION: The results of the two analyses appear to show a moderate effect of mammography usage on decreasing breast cancer mortality in the US, which seems to support the conclusions of randomized mammographic screening trials. While randomized controlled trials are certainly the gold standard in appraising the efficacy of new screening or treatment modalities, such trials are conducted under standardized conditions and do not always reflect the effect of these interventions at population level. This paper attempts to examine population level effects through ecologic analyses. Results, however, need to be interpreted cautiously owing to the limitations and biases inherent in such analyses.

Adult↗
PubMed2004

An ecologic study of prostate-specific antigen screening and prostate cancer mortality in nine geographic areas of the United States.

Ecologic studies of cancer screening examine cancer mortality rates in relation to use of population screening. These studies can be confounded by treatment patterns or influenced by choice of outcome and time horizon. Interpretation can be complicated by uncertainty about when mortality differences might be expected. The authors examined these issues in an ecologic analysis of prostate-specific antigen (PSA) screening and prostate cancer mortality across nine cancer registries in the United States. Results suggested a weak trend for areas with greater PSA screening rates to have greater declines in prostate cancer mortality; however, the magnitude of this trend varied considerably with the time horizon and outcome measure. A computer model was used to determine whether divergence of mortality declines would be expected under an assumption of a clinically significant survival benefit due to screening. Given a mean lead time of 5 years, the model projected that differences in mortality between high- and low-use areas should be apparent by 1999 in the absence of other factors affecting mortality. The authors concluded that modest differences in PSA screening rates across areas, together with additional sources of variation, could have produced a negative ecologic result. Ecologic analyses of the effectiveness of PSA testing should be interpreted with caution.

Aged↗
PubMed2004

Cure fraction estimation from the mixture cure models for grouped survival data.

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.

Cohort Studies↗