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Modeled Estimates of the Effects of Screening: Results from the CISNET
Breast Cancer Consortium
International Breast Cancer Screening Network Biennial Meeting
Kathleen Cronin
Statistical Research and Applications Branch
National Cancer Institute
May 12, 2006
What Is CISNET?
• NCI Sponsored Consortium of Modelers Focused on– Modeling of the Impact of Cancer Control
Interventions on Current and Future Population Trends in Incidence and Mortality
– Optimal Cancer Control Planning
• 15 funded grantees in Breast, Prostate, Colorectal, and Lung Cancer
• Comparative modeling approach – Base Cases are joint modeling exercises
Breast Cancer Investigators in CISNET Dana Farber - Marvin Zelen
Sandra J. Lee, Hui Huang, Rebecca Gelman
Erasmus University – Dik HabbemaSita Y.G.L. Tan, Gerrit J. van Oortmarssen, Harry J. de Koning, Rob Boer
Georgetown University – Jeanne MandelblattClyde B. Schechter, K. Robin Yabroff, William Lawrence, Bin Yi, Jennifer Cullen
MD Anderson – Donald BerryLurdes Inoue, Mark Munsell, John Venier, Yu Shen, Greg Ball, Emma Hoy, Richard L. Theriault, Melissa Bondy
Stanford University – Sylvia PlevritisBronislava Signal, Peter Salzman, Peter Glynn, Jarrett Rosenberg, Sanatan Rai
University of Rochester – Andrei YakovlevAlexander V. Zorin, Leonid G. Hanin
University of Wisconsin – Dennis FrybackMarjorie A. Rosenberg, Amy Trentham-Dietz, Patrick L. Remington, Natasha K. Stout,Vipat Kuruchittham
National Cancer InstituteEric J. Feuer, Kathleen A. Cronin, Angela Mariotto
Cornerstone Systems NorthwestLauren Clarke
Joint Analysis of the Seven CISNET Groups: Breast Cancer Base Case
What is the Impact of Adjuvant Therapy and Screening Mammography on US Breast
Cancer Mortality: 1975-2000 ?
Publications
Berry et al. N ENGL J MED 2005;353:1784-1792
JNCI Monograph due out summer 2006• Common inputs• Model descriptions• Comparisons of
– Modeling assumptions
– Intermediate outcomes
– Mortality outcomes
Population Models
7 Different Breast CancerModels
Backgroundtrends
Screeningbehavior
Diffusion ofnew treatments
BCIncidence
&mortality
Common InputsUnique Simulation or Analytical Model
Other Common Inputs
Common Inputs
Background Trends In Incidence
What would incidence have looked like without mammography screening?
Modeled incidence as a function of age, calendar period and birth cohort using historical Connecticut and SEER registry data.
• Assume that the “calendar period” effect reflects screening– Age-Period-Cohort represent that observed data points– Age-Cohort represents incidence without screening
• JNCI Monograph
Connecticut Breast Cancer Incidence By Age Group
Age 60-69
Age 30-39
Age 50-59
Age 40-49
Age 70-84
1940 1950 1960 1970 1980 1990 2000
Age 60-69
Age 30-39
Age 50-59
Age 40-49
Diagnosis year
Rat
e pe
r 10
0,00
0 w
omen
SEER Breast Cancer Incidence By Age Group Weighting for SEER
Age 70-84
1940 1950 1960 1970 1980 1990 2000
Screening Behaviors
How much screening is there between 1975 and 2000?
• Developed a simulation program that would generate screening histories over the course of a woman’s lifetime
• Modeled the age of first screen using survey data• Modeled repeat screening behaviors using longitudinal
data from the breast cancer surveillance consortium
Cronin et al. The Dissemination Of Mammography In The United States. Cancer Causes Control 2005;16:701-712.
Program posted on CISNET site under Input Parameter Generator Interfaces (http://cisnet.cancer.gov/)
Modeled Mammography Screening Over Time, Women age 40-79
0
25
50
75
100
1985 1990 1995 2000
Year
Biannual
Annual
Irregular
Never
Diffusion Of Adjuvant Chemotherapy and Tamoxifen
What is the usage of adjuvant chemotherapy and Tamoxifen by calendar year, age, stage and ER status?
• Modeled the use of adjuvant therapy using SEER patterns of care studies and SEER treatment information
– Mariotto et al. Trends in use of adjuvant multi-agent chemotherapy and Tamoxifen for breast cancer in the United States: 1975-1999. J Natl Cancer Inst 2002;94:1626-34.
– Updates in to include ER status in JNCI monograph
Dissemination of Adjuvant therapyWomen age 50-69 with node positive stage II or IIIA
0
10
20
30
40
50
60
1970 1980 1990 2000
Year
Multi-agent chemotherapy
only
Tamoxifen only
Both
Modeling Results
Model Runs From One GroupMortality Rates For Women 40-79 Under
Various Modeling Scenarios
0
10
20
30
40
50
60
70
1975 1980 1985 1990 1995 2000
year
mor
tali
ty r
ate
US actual
No Sc or Tr
Sc only
Tr only
Both Sc and Tr
Modeled Mortality For Women Age 40-70 Without Screening Or Adjuvant Treatment
0
10
20
30
40
50
60
70
80
1975 1980 1985 1990 1995 2000
year
mor
talit
y ra
te
DEGMSRW
Modeled Mortality For Women Age 40-70 With Screening and Adjuvant Treatment
0
10
20
30
40
50
60
1975 1980 1985 1990 1995 2000
Year
G M
WR
SE
USD
Mortality rate
Estimated percent decline in mortality due to screening and adjuvant therapy for the 7 models
Due to Screening
Due
to T
reat
men
t
0 5 10 15 20 25 30
0
5
10
15
20
25
30
W
G
D
S
EMR
Conclusions and Press Coverage
• Mammograms Validated as Key In Cancer Fight (New York Times)
Conclusions and Press Coverage
• Mammograms Validated as Key In Cancer Fight (New York Times)
• Mammography in question: Benefits of breast cancer screening may be small, researchers say (Chicago Tribune)
Conclusions and Press Coverage
• Mammograms Validated as Key In Cancer Fight (New York Times)
• Mammography in question: Benefits of breast cancer screening may be small, researchers say (Chicago Tribune)
• Statistical Blitz Helps Pin Down Mammography Benefits - “An unprecedented statistical assault” (CNN – medpage today)
Conclusions and Press Coverage
“What seems most important is that each team found at least some benefit from mammograms. The likelihood that they are beneficial seems a lot more solid today than it did four years ago, although the size of the benefit remains in dispute”
New York Times Editorial
Future Work
• Individual groups are working modeling risk factors and impact on cancer incidence.
• Optimal screening schedules for the population and for high risk groups.
• Factors influencing disparities.
• Several groups are participating in modeling progress toward HP2010 goals.
• A base case II – Modeling the impact of new treatments on population breast cancer mortality rates.
Age of First Mammography Screening By Birth Cohort
00.10.20.30.40.50.60.70.80.9
1
25 35 45 55 65 75 85 95
Age
% e
ver
scre
ened
Based on a series of NHIS surveys
1898-1912
1918-22
1928-321938-421948-52
1958-62
Time Between Subsequent Screening Exams For Women age 50-59
Based on data from the Breast Cancer Surveillance Consortium
0
20
40
60
80
100
0 5 10 15
years
% h
ad n
ext
ex
am
.
Annual
Biennial
Irregular
Classification Of Screening Type By Age
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
18-39 40-49 50-59 60-69 70-79 80+
Age Group
% o
f P
ou
lati
on
irregular
biennial
annual
Based on data from the Breast Cancer Surveillance Consortium
Next Steps
CISNET Reissuance
Discovery
Basic Mathematical and Statistical Relationships
Necessary for the Development of Multi-
Cohort Population Models
Development
Data Sources and Realistic Scenarios
to Evaluate Past Intervention Impact
in Population Settings and
Project Future Impact
Delivery
Synthesis of Relevant Scenarios
for Informing Policy Decisions
and Cancer Control Planning &
Implementation
CISNET Original Issuance