Simulation Modeling and Cancer Control Planning

1. Simulation Modeling and Cancer Control Planning CISNET: NCI’s Consortium for Population Modeling to Guide Public Health Research and Priorities ITCR May 4, 2018 2 - 3 pm Eric J “Rocky” Feuer. Ph.D. Chief, Statistical Research and Applications Branch Program Director, CISNET 1

2. Cancer Intervention and Surveillance Modeling Network (CISNET) NCI Sponsored Collaborative Consortium (U01) of Modelers in Breast, Prostate, Colorectal, Lung, Esophagus, and Cervical cancers formed in 2000 Synthesize trial, epidemiologic & surveillance data using simulation modeling to guide public health research and priorities Help address the formidable and growing gap between the rapid pace of innovation in cancer research and our ability to efficiently harness it to improve population health https://cisnet.cancer.gov/ 2

3. What is a simulation model in this context? • Simulates individual life histories with respect to: • Year of birth • Development of risk factors for cancer or background risk for those born in specific years • Development of pre-cancerous lesions (e.g. polyps, Barrett’s esophagus), initiation of cancer • Rate of growth of cancer • Diagnosis of cancer through symptoms • Basic treatment (in some base year prior to treatment advances that will be modeled) • Death from cancer or other causes All calibrated to as many data sources as possible • We then intervene on these individuals (advances in prevention, screening, and treatment) which changes their life history • Parallel lives with and without interventions • Summing together these individuals the modelers can represent what happens under real or hypothetical scenarios and the impact of each intervention (by comparing runs) Natural History 3

4. Outline • Four Tenets of CISNET Modeling • Two Examples of CISNET Work • CISNET Organization and Collaborative Opportunities 4

5. I. Four Tenets of CISNET Modeling 5

6. Screening Risk Factors Treatment preclinical stage I preclinical stage II preclinical stage III preclinical stage IV I. Flexible Broad-Based Disease ModelsExemplar Colorectal Cancer Model Preclinical CANCER screen-detectable cancer phase ADENOMA Preclinical screen-detectable adenoma phase Clinical CANCER phase clinical stage I clinical stage II clinical stage III clinical stage IV adenoma <=5 mm death colorectal cancer No lesion adenoma 6-9 mm adenoma >=10 mm Adenoma Autopsy studies Colonoscopy studies Preclinical Cancer Clinical Cancer SEER Incidence Death US Mortality 6 Datasources:

7. Risk factor trends Examples of outputs: • Mortality • Quality-adjusted life years • Overdiagnosis • Direct medical costs Screeningbehavior Diffusion ofnew treatments Schema for CISNET Modeling Individual Cancer Models: Simulation or Analytic Intervention Modeling (Common Inputs) Common Outputs: Costs & Benefits of Interventions Pre-cancerous Lesions Initiation of Cancer Rate of Growth of Cancer Calendar Time 2020 1970 1980 1990 2000 2010 2030 7

8. ExtensionsUpstream Modeling Examples of outputs: Risk factors • Mortality • Quality-adjusted life years • Overdiagnosis • Direct medical costs Screeningbehavior Diffusion ofnew treatments Common Outputs: Costs & Benefits of Interventions Upstream Modeling Intervention Modeling Cancer Modeling Social, economic, and other determinants of usage of screening, treatment & risk behavior Pre-cancerous Lesions Initiation of Cancer Rate of Growth of Cancer 8

9. ExtensionsMulti-Scale Modeling Examples of outputs: Risk factors • Mortality • Quality-adjusted life years • Overdiagnosis • Direct medical costs Screeningbehavior Diffusion ofnew treatments Common Outputs: Costs & Benefits of Interventions Upstream Modeling Intervention Modeling Cancer Modeling Social, economic, and other determinants of usage of screening, treatment & risk behavior Pre-cancerous Lesions Initiation of Cancer Rate of Growth of Cancer Multi-scale cancer model: molecular/cellular determinants of tumor behavior Integrative Cancer Biology Program Division of Cancer Biology 9 PAR 13-081Bridging the Gap Between Cancer Mechanism and Population Science

10. Extensions - Incorporating Genomic and Family History Risk Profiles Examples of outputs: Risk factors • Mortality • Quality-adjusted life years • Overdiagnosis • Direct medical costs Screeningbehavior Diffusion ofnew treatments Common Outputs: Costs & Benefits of Interventions Upstream Modeling Intervention Modeling Cancer Modeling Social, economic, and other determinants of usage of screening, treatment & risk behavior Pre-cancerous Lesions Initiation of Cancer Rate of Growth of Cancer Molecular and family history targeted medicine Multi-scale cancer model: molecular/cellular determinants of tumor behavior Integrative Cancer Biology Program Division of Cancer Biology 10

11. II. Multiple Birth Cohort Modeling Hypothetical Cohort – Starting at Age 30and Continuing to Age 79 Age 11 Years

12. CISNET Multi-Cohort Models Re-Create the Actual Population Dynamics of the US Population US experience 1975-2000 ages 30-79 Requires modeling birth cohorts from 1895 through 1970 Age 12

13. Smoking Prevalence by Birth Cohort: 1890 – 1995 Birth Cohorts Holford et al., 2014 Surgeon General’s Report on the Health Consequences of Smoking -- 50 Years of Progress Males Percent Current Smokers Females 13

14. III. Comparative Modeling Older Approach: 4 Independent Studies of the Cost-Effectiveness Of CT Screening for Lung Cancer • Central questions to be addressed by groups collaboratively with a common set of inputs and outputs • Reproducibility across models adds credibility to results • Differences points out areas for further study in a systematic way • Encourages cooperation instead of competition between modelers Approach Innovated by CISNET: Systematic Comparative Modeling Differences in target population, screening frequency, stage shift, assumptions about lead time and overdiagnosis, sensitivity 14

15. Cancer Site Organization Multiple-PI cooperative agreements (U01) each focused on a different cancer site with a coordinating center and multiple modeling groups Cancer-Site Specific Coordinating Center Modeling Group 1 Modeling Group 2 Modeling Group 3 Modeling Group 4 Modeling Group 5 Modeling Group 6 15

16. IV. Outreach and Collaborations United States Preventive ServicesTask Force,Agency for Health CareResearch and Quality (AHRQ)Support of Task Force Evidence Reviews Centers for Disease Control, Division of Cancer Prevention and Control Studies to guide CDC in designing new programs or enhancing existing ones American College of Radiology Imaging Network (ACRIN)Cost Effectiveness Study for NationalCT Colonography Trial Center for Medicare and Medicaid Services (CMS) Technology Assessments to support National Coverage Determinations 16

17. II. Two Examples 17

18. Cancer Site: Lung NCI Project Scientist Rocky Feuer Surveillance Research Program, DCCPS Coordinating Center PI University of Michigan Rafael Meza Model 1Erasmus University Medical Center Harry de Koning (PI) Model 2University of Michigan Rafael Meza (PI) Nancy Fleischer, Jihyoun Jeon (Co-I) Model 3Yale University Theodore Holford (PI) Model 4 Massachusetts General Hospital Joey Kong (PI) G. Scott Gazelle (Co-I) Model 5 Georgetown University David Levy (PI) Model 6Stanford University Sylvia Plevritis (PI) Summer Han (Co-I) Affiliate Member: Martin Tammemagi (Brock University, Cancer Care Ontario) 18

19. NLST Trial Applies to Only a Single Regimen of Screening “My opinion is that one can conclude from the NLST data that three annual low-dose helical CTs in individuals ages 55 to 74 with 30-pack-year smoking history can lower lung-cancer-specific mortality by 20 percent,” NCI’s Berg said in an interview. “Claims beyond that we are not addressing. We are saying that our data speak to what we did in the population in which we did it.” “The CISNET modelers may be able to look at different frequencies of screening, different ages at starting, different risk levels, such as 20 pack-year smokers or a 40 pack-year smokers.” 19

20. 2013 Decision Analysis for US Preventive Services Task Force (USPSTF) NLST - trial results published Nov. 2010 - represented only 1 specific screening regimen (start age 55, stopping age 75, 30 pack years, < 15 years since quit) Range of Screening Programs for Simulation NLST Criteria 20

21. Consensus-Efficient Scenarios: Scenarios Efficient in at Least 3 of 5 Models Average across models Efficiency Frontier: best you can do for each specified # of screens More efficient strategies Selected Scenario NLST Less efficient strategies de Koning et al, Ann Intern Med 2014 21

22. Helping the Task Force Choose Among Efficient Scenarios New LC screening recommendations released – Recommended Dec. 2013 Moyer et al., Ann Intern Med 2014 22

23. Cancer Site: Colorectal NCI Project Scientist Paul Doria-RoseHealthcare Delivery Research Program, DCCPS Coordinating Center PI Memorial Sloan Kettering Cancer Center Ann Zauber Model 1 University of Minnesota/ Massachusetts General Hospital Karen Kuntz (PI) Amy Knudsen (Co-I) Model 2 Erasmus Medical Center/Memorial Sloan Kettering Cancer Center Iris Lansdorp-Vogelaar (PI) Harry de Koning (Co-I) Model 3 RAND Corporation Carolyn Rutter (PI) Affiliate Member: Jonathan Ozik (Argonne National Laboratories/University of Chicago) 23

24. Polygenic Risk-Stratified Screening Cost-effective strategies* by relative risk as estimated by a polygenic test under different assumptions about the discriminatory ability of the polygenic score Threshold for lower intensity screening Threshold for higher intensity screening Percent of group 7 screening regimens 10 screening regimens 11 screening regimens Controls Cases Polygenic Risk Score Number of Risk Alleles Naber et al. (Ph.D thesis and submitted) 24 * Under a willingness-to-pay threshold of $50,000 per QALY

25. Estimated Cost for Polygenic Testing for Which Risk-Stratified Screening Would Be Equally Cost-Effective as Current Recommended Screening* Estimated Cost Per Test for Which Risk-Stratified Screening Would Be Equally Cost-Effective as Current Recommended Screening* As cost of testing falls and/or # of SNP’s increase polygenic risk-based screening could become cost effective current cost Current Cost CISNET collaboration with Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) to further validate SNP’s, monitor developing AUC, costs, and risk models including SNPs, family history & other risk factors * Colonoscopy at ages 50, 60, and 70 25

26. III. CISNET Organization and Collaborative Opportunties 26

27. Organizational Structure 27

28. CISNET Meetings • Annual meeting at NCI, mid-year meeting at participating institution • Meetings last 5 days • 1½ to 2 day meetings for each cancer site group, ½ day plenary session at annual meetings, occasional joint portions of meetings between cancer sites on special topics • Programmers Forum (extended lunch with talks, webinars) • Junior Investigators Forum (extended lunch with talks, webinars) • Most groups have monthly calls and additional calls for specific projects 28

29. Non-CISNET’ers Attending the Meetings • Because confidential unpublished research is discussed at the meetings, they are generally not open (except for the plenary session) • However: • Guests speakers are regularly invited • Others who wish to attend can ask the appropriate coordinating center PI • CISNET affiliate membership is encouraged 29

30. CISNET Affiliate Investigator Policy • Intent that the CISNET collaboration should be as open as possible to those outside the consortium who are interested in joining with and benefitting from the ongoing collaborative relationships within CISNET. • Interested affiliate members should understand that to join the collaboration, there will be certain expectations in terms of participation, sharing, mutual benefits and respect of confidentiality. • Applicants for affiliate status are required to submit a written statement summarizing their prior related work and to work with the Coordinating Center PI from the appropriate cancer site to write a Statement of Work describing the intended collaborations with the CISNET group. • Cancer site group votes 30

31. Opportunities for Collaboration • Model applications • Bringing unique data sources and ideas about how they can be utilized • Improvements in methodology & computing, e.g., • Model calibration with multiple target data sets over a large parameter space • Probabilistic sensitivity analysis • Statistical models (e.g. risk models) than can be imbedded in population simulation models • Model documentation platforms 31

32. Thank You! CISNET on the Web http://cisnet.cancer.gov/ Contact Info Rocky Feuer - rf41u@nih.gov 32