Transcript Breast Cancer Investigators in CISNET

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 Habbema
Sita Y.G.L. Tan, Gerrit J. van Oortmarssen, Harry J. de Koning, Rob Boer
Georgetown University – Jeanne Mandelblatt
Clyde B. Schechter, K. Robin Yabroff, William Lawrence, Bin Yi, Jennifer Cullen
MD Anderson – Donald Berry
Lurdes Inoue, Mark Munsell, John Venier, Yu Shen, Greg Ball, Emma Hoy,
Richard L. Theriault, Melissa Bondy
Stanford University – Sylvia Plevritis
Bronislava Signal, Peter Salzman, Peter Glynn, Jarrett Rosenberg, Sanatan Rai
University of Rochester – Andrei Yakovlev
Alexander V. Zorin, Leonid G. Hanin
University of Wisconsin – Dennis Fryback
Marjorie A. Rosenberg, Amy Trentham-Dietz, Patrick L. Remington,
Natasha K. Stout,Vipat Kuruchittham
National Cancer Institute
Eric J. Feuer, Kathleen A. Cronin, Angela Mariotto
Cornerstone Systems Northwest
Lauren 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
Common Inputs
Background
trends
Screening
behavior
Diffusion of
new treatments
Other Common
Inputs
Unique Simulation
or Analytical Model
7 Different
Breast
Cancer
Models
BC
Incidence
&
mortality
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 70-84
Age 60-69
Age 50-59
Age 40-49
Age 30-39
1940
1950
1960
1970
1980
1990
2000
SEER Breast Cancer Incidence By Age Group Weighting for SEER
Age 70-84
Age 60-69
Age 50-59
Age 40-49
Age 30-39
1940
1950
1960
1970
1980
Diagnosis year
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
100
Never
75
Irregular
50
Biannual
25
Annual
0
1985
1990
1995
Year
2000
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 therapy
Women age 50-69 with node positive stage II or IIIA
60
Multi-agent
chemotherapy
only
50
40
30
20
Both
10
Tamoxifen
only
0
1970
1980
1990
Year
2000
Modeling Results
Model Runs From One Group
Mortality Rates For Women 40-79 Under
Various Modeling Scenarios
70
No Sc or Tr
mortality rate
60
30
Sc only
Tr only
Both Sc and Tr
20
US actual
50
40
10
0
1975
1980
1985
1990
year
1995
2000
Modeled Mortality For Women Age 40-70
Without Screening Or Adjuvant Treatment
80
mortality rate
70
D
E
G
M
S
R
W
60
50
40
30
20
10
0
1975
1980
1985
1990
year
1995
2000
Modeled Mortality For Women Age 40-70
With Screening and Adjuvant Treatment
60
50
Mortality 40
rate
30
20
10
0
19 75
G
E
S
D
19 80
M
R
W
US
19 85
19 90
Year
19 95
20 00
Estimated percent decline in mortality due to
screening and adjuvant therapy for the 7 models
30
25
Due to Treatment
E
20
R
W
M
15
S
G
D
10
5
0
0
5
10
15
Due to Screening
20
25
30
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
% ever screened
1948-52
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1938-42
1928-32
1918-22
1898-1912
1958-62
25
35
45
55
65
75
Age
Based on a series of NHIS surveys
85
95
Time Between Subsequent Screening Exams
For Women age 50-59
Annual
100
% had next exam .
Biennial
80
Irregular
60
40
20
0
0
5
10
15
years
Based on data from the Breast Cancer Surveillance Consortium
Classification Of Screening Type By Age
100%
90%
% of Poulation
80%
70%
60%
irregular
50%
biennial
annual
40%
30%
20%
10%
0%
18-39
40-49
50-59
60-69
70-79
80+
Age Group
Based on data from the Breast Cancer Surveillance Consortium
Next Steps
Discovery
Development
Delivery
Basic Mathematical
and Statistical
Relationships
Necessary for the
Development of MultiCohort Population
Models
Data Sources and
Realistic Scenarios
to Evaluate Past
Intervention Impact
in Population
Settings and
Project Future
Impact
Synthesis of
Relevant Scenarios
for Informing
Policy Decisions
and Cancer Control
Planning &
Implementation
CISNET Original Issuance
CISNET Reissuance