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Kathleen A Cronin

Publications and source records attributed to Kathleen A Cronin.

15 recordsLinked to original sources
PubMed2006

Validation of a model of lung cancer risk prediction among smokers.

The Bach model was developed to predict the absolute 10-year risk of developing lung cancer among smokers by use of participants in the Carotene and Retinol Efficacy Trial of lung cancer prevention. We assessed the validity of the Bach model among 6239 smokers from the placebo arm of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The expected numbers of lung cancer cases and deaths without lung cancer were calculated from the Bach model and compared with the observed numbers of corresponding events over 10 years. We found that the risk model slightly underestimated the observed lung cancer risk (number of lung cancers expected/number observed = 0.89, 95% confidence interval [CI] = 0.80 to 0.99) over 10 years. The competing risk portion of the model substantially underestimated risk of non-lung cancer mortality (number of non-lung cancer deaths expected/number observed = 0.61, 95% CI = 0.57 to 0.64) over 10 years. The age-specific concordance indices for 10-year predictions were 0.77 (95% CI = 0.70 to 0.84), 0.59 (95% CI = 0.53 to 0.65), 0.62 (95% CI = 0.57 to 0.67), and 0.57 (95% CI = 0.49 to 0.67) for the age groups 50-54, 55-59, 60-64, and 65-69 years, respectively. Periodic radiographic screening in the ATBC Study may explain why slightly more cancers were observed than expected from the Bach model.

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 Unfortunately, standard estimates of survival may be outdated, because they do not reflect recent advances.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

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 mammography on U.S. breast cancer mortality, 1975-2000: are intermediate outcome measures informative?

Seven models have estimated the contribution of screening to the decrease in U.S. breast cancer mortality between 1975 and 2000. We will investigate whether the model estimates of the mortality reduction due to screening are associated with intermediate outcome measures (IOMs). Detection rates at screening, 1- and 2-year sensitivity, program sensitivity, and incidence of advanced tumors are used as IOMs. Moreover, the model parameters preclinical duration and sensitivity are analyzed. The correlation of IOMs with mortality is assessed for actual U.S. screening and for an intensive screening scenario, with annual screening at ages 40-79 years with 100% participation. Also, 12 alternative screening scenarios are run for one of the models, and within-model correlation between IOMs and mortality reduction is described. Resulting correlations between IOMs and mortality reduction are mostly weak. For 2-year sensitivity and the incidence of advanced tumors, correlations are high in the intensive screening scenario. Within-model correlations are strong for incidence of advanced tumors and program sensitivity. Intermediate outcome measures have limited potential in predicting the impact of mammographic screening on mortality. Incidence of advanced tumors and program sensitivity are measures that merit further consideration as surrogates for mortality reduction.

Adult↗
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↗
PubMed2005

The frequency of ipsilateral second tumors after breast-conserving surgery for DCIS: a population based analysis.

BACKGROUND: The diagnosis of ductal carcinoma in situ (DCIS) is increasing, although to the authors' knowledge there is no consensus regarding optimal treatment. This analysis of women treated with breast-conserving surgery (BCS) evaluated the impact of radiation therapy (RT) in patient outcomes. METHODS: The current study included a population-based sample of 1103 women residing in selected Surveillance, Epidemiology, and End Results (SEER) registries who were diagnosed with DCIS between 1991-1992. Data were obtained from the registry, physician follow-up, and pathology reports. Physicians were contacted in 1999 to determine whether the patient had developed a second event in the ipsilateral breast. For second events, pathology reports were reviewed to determine the presence of in situ or invasive disease. Registry data through 2001 were used to assess death rates and cause of death. Cox proportional hazards and logistic regression models were used to evaluate the rates of second events and breast carcinoma deaths between women treated with and without RT. RESULTS: Over an average of 91 months, 13.2% of women developed a second event. Rates of second events were 11% for women treated with BCS and RT compared with 15% for women treated with BCS only (adjusted hazards ratio, 0.64; 95% confidence interval, 0.44-0.92). Women receiving RT were significantly less likely to develop invasive breast carcinoma in the ipsilateral breast (adjusted odds ratio, 0.40). By 2001, the rate of death from breast carcinoma was 2.7%; in the group of women treated with BCS only compared with 0.8% in the group of women treated with BCS with RT. CONCLUSIONS: Among a population-based cohort, RT was found to significantly reduce the risk of second events in the ipsilateral breast, particularly invasive tumors, although not to the extent reported in clinical trials.

Aged↗
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↗
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↗
PubMed2004

The use of modeling to understand the impact of screening on U.S. mortality: examples from mammography and PSA testing.

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.

Breast Neoplasms↗
PubMed2003

Age-conditional probabilities of developing cancer.

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.

Age of Onset↗