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Executive Summary

Executive Summary Mortality from breast cancer has been declining in the U.S. population since 1990. Although many have attributed this decline to more women participating in mammography screening, some leaders in clinical medicine think that most of the mortality decline is due to improvements in adjuvant therapies. A consortium of researchers participating in the Cancer Intervention and Surveillance Modeling Network (CISNET) (Table 1) address this issue through the use of statistical and simulation modeling to quantify the relative impact of adjuvant therapy and screening mammography on the decline. The collaborative modeling process and subsequent joint results represent an unprecedented modeling effort in public health. Table 1.  Breast cancer investigators in the Cancer Intervention and Surveillance Modeling Network Consortium Institution  Investigators  Dana-Farber Cancer Center  Marvin Zelen (PI), Sandra J. Lee, Hui Huang, Rebecca Gelman  Erasmus MC, University Medical Center Rotterdam  J. Dik F. Habbema (PI), Sita Y. G. L. Tan, Gerrit J. van Oortmarssen, Harry J. de Koning, Rob Boer  Georgetown University  Jeanne Mandelblatt (PI), Clyde B. Schechter, K. Robin Yabroff, William Lawrence, Bin Yi, Jennifer Cullen  University of Texas M. D. Anderson Cancer Center  Donald A. Berry (PI), Lurdes Inoue, Mark Munsell, John Venier, Yu Shen, Greg Ball, Emma Hoy, Richard L. Theriault, Melissa Bondy  Stanford University  Sylvia K. Plevritis (PI), Bronislava Signal, Peter Salzman, Peter Glynn, Jarrett Rosenberg, Sanatan Rai  University of Rochester  Andrei Y. Yakovlev (PI), Alexander V. Zorin, Leonid G. Hanin  University of Wisconsin  Dennis G. Fryback (PI), Marjorie A. Rosenberg, Amy Trentham-Dietz, Patrick L. Remington, Natasha K. Stout, Vipat Kuruchittham  National Cancer Institute  Eric J. Feuer (Program Director, CISNET); Kathleen A. Cronin (Scientific Coordinator, CISNET Breast Cancer Group); Angela Mariotto  Cornerstone Systems Northwest  Lauren Clarke  Institution  Investigators  Dana-Farber Cancer Center  Marvin Zelen (PI), Sandra J. Lee, Hui Huang, Rebecca Gelman  Erasmus MC, University Medical Center Rotterdam  J. Dik F. Habbema (PI), Sita Y. G. L. Tan, Gerrit J. van Oortmarssen, Harry J. de Koning, Rob Boer  Georgetown University  Jeanne Mandelblatt (PI), Clyde B. Schechter, K. Robin Yabroff, William Lawrence, Bin Yi, Jennifer Cullen  University of Texas M. D. Anderson Cancer Center  Donald A. Berry (PI), Lurdes Inoue, Mark Munsell, John Venier, Yu Shen, Greg Ball, Emma Hoy, Richard L. Theriault, Melissa Bondy  Stanford University  Sylvia K. Plevritis (PI), Bronislava Signal, Peter Salzman, Peter Glynn, Jarrett Rosenberg, Sanatan Rai  University of Rochester  Andrei Y. Yakovlev (PI), Alexander V. Zorin, Leonid G. Hanin  University of Wisconsin  Dennis G. Fryback (PI), Marjorie A. Rosenberg, Amy Trentham-Dietz, Patrick L. Remington, Natasha K. Stout, Vipat Kuruchittham  National Cancer Institute  Eric J. Feuer (Program Director, CISNET); Kathleen A. Cronin (Scientific Coordinator, CISNET Breast Cancer Group); Angela Mariotto  Cornerstone Systems Northwest  Lauren Clarke  View Large Seven CISNET modeling groups estimate the impact of screening mammography and the adjuvant therapies of multiple-agent chemotherapy and tamoxifen on U.S. breast cancer mortality rates. Together they employ a comparative modeling approach using common population-level inputs (such as the dissemination and use of mammography and adjuvant therapy) and a common set of model runs and outputs. The models vary in their structure, assumptions, and synthesis and use of data sources and study results. Using a variety of approaches affords a broader view of the relationships between treatment, screening, and mortality while providing a representation of the uncertainty related to the modeling results. This monograph provides details of the inputs, models, and results of this joint modeling effort. Following an introductory chapter that motivates the problem, this monograph is divided into three general sections: section 1 summarizes the common model inputs, section 2 presents each of the seven models and their specific results, and section 3 provides comparative results. The development of common inputs led to a better understanding of major factors influencing trends in breast cancer incidence and survival from 1975 to 2000, since some factors are held constant across models, and others are allowed to vary. The portion of the increase in breast cancer incidence that resulted from changes in individual risk rather than screening practices was estimated by modeling incidence rates as a function of age, birth cohort, and calendar time. Dissemination and usage of mammography and adjuvant treatments over time involved piecing together data from available sources to develop comprehensive population models. These models proved to be crucial in linking medical practices with mortality outcomes. Together the common inputs provide the background needed to model the mortality impact of screening and adjuvant treatment. The primary result of the analysis was estimating the percent decline in mortality due to adjuvant treatment and the percent decline due to screening. The observed mortality decline between 1990 and 2000 was 23.5% for women aged 30–79 years; the consortium of investigators estimated the mortality reduction between 24.9% and 38.3% when comparing modeling mortality rates without screening and treatment to modeled rates with screening and treatment. The modeled estimates were larger than the observed decline since a rise in breast cancer mortality would have been expected without the introduction of screening and adjuvant treatments. Individual results for the modeled percent decline in mortality had a range of 12%–21% due to adjuvant therapies and 8%–23% due to screening (1). The intermodel variability for the benefits of screening was larger than it was for treatment. The larger variability reflects the greater complexity and uncertainty associated with estimating the impact of screening on mortality. A key result is that all groups found some benefit from both adjuvant therapy and mammography screening while concluding that the observed mortality data could not be explained by either adjuvant therapies or screening alone. When the individual results were considered collectively, about half the modeled decline in mortality was due to adjuvant therapies and half due to screening. These interventions have been analyzed in clinical trials, but the impact in a population setting may be different from that in a trial setting. One significant factor in a population setting is the dissemination of the intervention to the target population, and often this dissemination is incomplete. In estimating the benefits of adjuvant treatment, this analysis combined information from clinical trials with dissemination and usage of adjuvant treatment in the U.S. population to estimate the impact on mortality. The benefit of screening has remained controversial because of inconsistent results from clinical trials. The CISNET modeling effort provides evidence that screening is affecting breast cancer mortality rates, although the magnitude of the mortality benefit is uncertain. The value of the analyses reported in this monograph goes beyond the substantive results of investigating the reasons for declining breast cancer mortality. The seven models described represent an array of approaches to population modeling of cancer control that can be applied to many other public-health questions. The work comparing the seven models at their most rudimentary levels in the final section gives unique insight into the complicated process of modeling population dynamics. This comparative framework is used to link intermediate outcomes and modeling assumptions to mortality results. The monograph demonstrates a process for collaborative work and an example of how statistical modeling can play an expanded role in providing input to public health policy and decision making. References (1) Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med  2005; 353: 1784–92. Google Scholar © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JNCI Monographs Oxford University Press

Executive Summary

JNCI Monographs , Volume 2006 (36) – Oct 1, 2006

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Publisher
Oxford University Press
Copyright
© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org.
ISSN
1052-6773
eISSN
1745-6614
DOI
10.1093/jncimonographs/lgj001
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Abstract

Mortality from breast cancer has been declining in the U.S. population since 1990. Although many have attributed this decline to more women participating in mammography screening, some leaders in clinical medicine think that most of the mortality decline is due to improvements in adjuvant therapies. A consortium of researchers participating in the Cancer Intervention and Surveillance Modeling Network (CISNET) (Table 1) address this issue through the use of statistical and simulation modeling to quantify the relative impact of adjuvant therapy and screening mammography on the decline. The collaborative modeling process and subsequent joint results represent an unprecedented modeling effort in public health. Table 1.  Breast cancer investigators in the Cancer Intervention and Surveillance Modeling Network Consortium Institution  Investigators  Dana-Farber Cancer Center  Marvin Zelen (PI), Sandra J. Lee, Hui Huang, Rebecca Gelman  Erasmus MC, University Medical Center Rotterdam  J. Dik F. Habbema (PI), Sita Y. G. L. Tan, Gerrit J. van Oortmarssen, Harry J. de Koning, Rob Boer  Georgetown University  Jeanne Mandelblatt (PI), Clyde B. Schechter, K. Robin Yabroff, William Lawrence, Bin Yi, Jennifer Cullen  University of Texas M. D. Anderson Cancer Center  Donald A. Berry (PI), Lurdes Inoue, Mark Munsell, John Venier, Yu Shen, Greg Ball, Emma Hoy, Richard L. Theriault, Melissa Bondy  Stanford University  Sylvia K. Plevritis (PI), Bronislava Signal, Peter Salzman, Peter Glynn, Jarrett Rosenberg, Sanatan Rai  University of Rochester  Andrei Y. Yakovlev (PI), Alexander V. Zorin, Leonid G. Hanin  University of Wisconsin  Dennis G. Fryback (PI), Marjorie A. Rosenberg, Amy Trentham-Dietz, Patrick L. Remington, Natasha K. Stout, Vipat Kuruchittham  National Cancer Institute  Eric J. Feuer (Program Director, CISNET); Kathleen A. Cronin (Scientific Coordinator, CISNET Breast Cancer Group); Angela Mariotto  Cornerstone Systems Northwest  Lauren Clarke  Institution  Investigators  Dana-Farber Cancer Center  Marvin Zelen (PI), Sandra J. Lee, Hui Huang, Rebecca Gelman  Erasmus MC, University Medical Center Rotterdam  J. Dik F. Habbema (PI), Sita Y. G. L. Tan, Gerrit J. van Oortmarssen, Harry J. de Koning, Rob Boer  Georgetown University  Jeanne Mandelblatt (PI), Clyde B. Schechter, K. Robin Yabroff, William Lawrence, Bin Yi, Jennifer Cullen  University of Texas M. D. Anderson Cancer Center  Donald A. Berry (PI), Lurdes Inoue, Mark Munsell, John Venier, Yu Shen, Greg Ball, Emma Hoy, Richard L. Theriault, Melissa Bondy  Stanford University  Sylvia K. Plevritis (PI), Bronislava Signal, Peter Salzman, Peter Glynn, Jarrett Rosenberg, Sanatan Rai  University of Rochester  Andrei Y. Yakovlev (PI), Alexander V. Zorin, Leonid G. Hanin  University of Wisconsin  Dennis G. Fryback (PI), Marjorie A. Rosenberg, Amy Trentham-Dietz, Patrick L. Remington, Natasha K. Stout, Vipat Kuruchittham  National Cancer Institute  Eric J. Feuer (Program Director, CISNET); Kathleen A. Cronin (Scientific Coordinator, CISNET Breast Cancer Group); Angela Mariotto  Cornerstone Systems Northwest  Lauren Clarke  View Large Seven CISNET modeling groups estimate the impact of screening mammography and the adjuvant therapies of multiple-agent chemotherapy and tamoxifen on U.S. breast cancer mortality rates. Together they employ a comparative modeling approach using common population-level inputs (such as the dissemination and use of mammography and adjuvant therapy) and a common set of model runs and outputs. The models vary in their structure, assumptions, and synthesis and use of data sources and study results. Using a variety of approaches affords a broader view of the relationships between treatment, screening, and mortality while providing a representation of the uncertainty related to the modeling results. This monograph provides details of the inputs, models, and results of this joint modeling effort. Following an introductory chapter that motivates the problem, this monograph is divided into three general sections: section 1 summarizes the common model inputs, section 2 presents each of the seven models and their specific results, and section 3 provides comparative results. The development of common inputs led to a better understanding of major factors influencing trends in breast cancer incidence and survival from 1975 to 2000, since some factors are held constant across models, and others are allowed to vary. The portion of the increase in breast cancer incidence that resulted from changes in individual risk rather than screening practices was estimated by modeling incidence rates as a function of age, birth cohort, and calendar time. Dissemination and usage of mammography and adjuvant treatments over time involved piecing together data from available sources to develop comprehensive population models. These models proved to be crucial in linking medical practices with mortality outcomes. Together the common inputs provide the background needed to model the mortality impact of screening and adjuvant treatment. The primary result of the analysis was estimating the percent decline in mortality due to adjuvant treatment and the percent decline due to screening. The observed mortality decline between 1990 and 2000 was 23.5% for women aged 30–79 years; the consortium of investigators estimated the mortality reduction between 24.9% and 38.3% when comparing modeling mortality rates without screening and treatment to modeled rates with screening and treatment. The modeled estimates were larger than the observed decline since a rise in breast cancer mortality would have been expected without the introduction of screening and adjuvant treatments. Individual results for the modeled percent decline in mortality had a range of 12%–21% due to adjuvant therapies and 8%–23% due to screening (1). The intermodel variability for the benefits of screening was larger than it was for treatment. The larger variability reflects the greater complexity and uncertainty associated with estimating the impact of screening on mortality. A key result is that all groups found some benefit from both adjuvant therapy and mammography screening while concluding that the observed mortality data could not be explained by either adjuvant therapies or screening alone. When the individual results were considered collectively, about half the modeled decline in mortality was due to adjuvant therapies and half due to screening. These interventions have been analyzed in clinical trials, but the impact in a population setting may be different from that in a trial setting. One significant factor in a population setting is the dissemination of the intervention to the target population, and often this dissemination is incomplete. In estimating the benefits of adjuvant treatment, this analysis combined information from clinical trials with dissemination and usage of adjuvant treatment in the U.S. population to estimate the impact on mortality. The benefit of screening has remained controversial because of inconsistent results from clinical trials. The CISNET modeling effort provides evidence that screening is affecting breast cancer mortality rates, although the magnitude of the mortality benefit is uncertain. The value of the analyses reported in this monograph goes beyond the substantive results of investigating the reasons for declining breast cancer mortality. The seven models described represent an array of approaches to population modeling of cancer control that can be applied to many other public-health questions. The work comparing the seven models at their most rudimentary levels in the final section gives unique insight into the complicated process of modeling population dynamics. This comparative framework is used to link intermediate outcomes and modeling assumptions to mortality results. The monograph demonstrates a process for collaborative work and an example of how statistical modeling can play an expanded role in providing input to public health policy and decision making. References (1) Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med  2005; 353: 1784–92. Google Scholar © The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org.

Journal

JNCI MonographsOxford University Press

Published: Oct 1, 2006

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