1. Cancer Survival Query System (CSQS): Making Survival Estimates from Population-Based Cancer Registries More Timely and Relevant for Recently Diagnosed Patients Sept. 20-21, 2010 Methods and Applications for Population-Based Survival Workshop Fascati, Italy Eric J. (Rocky) Feuer, Ph.D. Chief, Statistical Methodology and Applications Branch Division of Cancer Control and Population Sciences National Cancer Institute

2. Some Questions When someone calls 1-800-4CANCER and asks about the prognosis of a family member who was newly diagnosed, where should the information come from? How can physicians get a better understanding of the potential impact of competing risks for newly diagnosed cancer patients with significant comorbidities? Can population-based cancer registry data play a role in answering these questions?

3. Outline I.Statistical Methodology II.Application to Prostate Cancer III.Demonstration IV.Testing Usefulness in Real World Situations

4. I. Statistical Methodology

5. Competing Risks Analysis (Discrete Time)

6. Two Data Situations Competing Risks Analysis All of the relevant patient characteristics for both cancer and other causes are in the same data set All of the relevant patient characteristics for both cancer and other causes are in the same data set Cancer and other cause of death characteristics are in separate data sets Cancer and other cause of death characteristics are in separate data sets

7. I. Everything in A Single Data Set Example: co-morbidity added to SEER through SEER-Medicare linkage –Standard competing risks analysis methods can be used –No assumption of independence of competing risks is necessary –Some restrictions on the parameterization may be necessary (Example: complicated if the time scales for both causes of death are not the same – e.g. time since dx for cancer and age for other causes) –Minjung Lee will present

8. II. Cancer and Other Cause Mortality Derived from Separate Data Sets Examples: –Other cause mortality derived from combination of SEER-Medicare and 5% non-cancer matching patients (Angela’s talk) –Other-cause mortality derived from mortality follow-up of National Health Interview Surveys (NHIS) as a function of general health status, functional status, and self- reported conditions – (all ages available!) Conditional independence is required (conditional on covariates) Parameterization for each cause is flexible Covered in this talk!

9. Competing Risks Under Independence

10. Using Relative Survival* * Cronin and Feuer, “Cumulative Cause-Specific Mortality for Cancer Patients in the Presence of Other Causes – A Crude Analogue of Relative Survival”, Statistics in Medicine, 2000.

11. Moving from Cohort to Individual Up to now the equations apply to estimating competing risk survival for a cohort of individuals (e.g. age 60+, Stage II CRC, both genders, all races) We are interested in customizing the estimates for individual (j) with –Cancer characteristics (z j ) E.g. Gleason’s score, stage, age, race, comorbidity –Other cause characteristics ( w j ) E.g. age, race, co-morbidity

12. Customized for individual ( j ) with cancer characteristics ( ) and other cause characteristics ( )

13. Analogue When We Use Cause of Death Information

14. II. Application to Prostate Cancer* *Colorectal cancer also available

15. Basics

16. 3 Staging Groups Pre-Treatment Clinical –For patients who have not yet been treated –Estimable because for prostate cancer SEER maintains data on both clinical and pathologic staging Pure Clinical –For patients who elected not to have surgery Pathologic –For patients who had surgery

17. Prostate Cancer – Extent of Disease T1 (Clinical Staging only) –T1a: Tumor incidentally found in 5% or less of resected prostate tissue (TURP). –T1b: Tumor incidentally found in > 5% of resected prostate tissue (TURP). –T1c: Tumor found in a needle biopsy performed due to elevated PSA. T2: Tumor confined within prostate. T3: Tumor extends through prostatic capsule. T4: Tumor is fixed, or invades adjacent structures other than seminal vesicles, e.g., bladder neck, external sphincter, rectum, levator muscles, and/or pelvic wall.

18. Prostate Cancer Inclusion Criteria –Age 94 and under –First Cancer Staging –Localized (Inapparent) - T1a,T1b,T1c N0 M0 (Clinical only) –Localized (Apparent) - T2 N0 M0 –Locally Advanced I – T3 N0 M0 –Locally Advanced II - T4 N0 M0 –Nodal Disease I - T1-T3 N1 M0 –Nodal Disease II – T4 N1 M0 –Distant Mets – Any T, Any N, M1 (Clinical Only)

19. Strata and Sample Sizes Stage Pre-treatment ClinicalPure Clinical Co-morbidity (Age 66+) All Co-morbidity (Age 66+) All Localized (Inapparent) 348391090792551663222 Localized (Apparent) 497061375183571479418 Locally Adv and Nodal 3649945527576669 Distant Metastases 3997975634868592 Totals9219126580867473157901 Stage Path Co-morbidity (Age 66+) All Localized 1106360338 Locally Adv and Nodal 549027116 Totals 1655387454

20. Prostate Covariates Substages of Localized (Inapparent) Substages of Locally Advanced and Nodal Disease Gleason’s Score (2-7 and 8-10) Substages x Gleason's Score Age (cubic spline – flat under age 50 and after age 90) Race (white, black, other) Marital Status (married, other) Co-morbidity – age 66+ (linear – flat at high values ) Calendar year (linear) –Projected to most recent data year (2005) and then flat to (conservatively) represent prognosis of recently dx patient –Mariotto AB, Wesley MN, Cronin KA, Johnson KA, Feuer EJ. Estimates of long-term survival for newly diagnosed cancer patients: a projection approach. Cancer. 2006 May 1;106(9):2039-50.

21. III. Demonstration

22. Website http://www16.imsweb.com/ Username: imsdev Password: website

23. CSQS Home Page

24. Prostate, Pre-Trt Clinical

25. T3 N0 M0

26. Gleasons 8-10

27. 73 White Married

28. 73 Chronologic Age, 67 Health Adjusted Age

29. Show Diabetes, Congestive Heart Failure

30. Show Health Adjusted Age at 82, Then Add 3 Years Subjective 85

31. People Chart for 1, 5, 10 Years

33. Pie Chart for 1, 5, 10 Years

35. Summary Chart – Alive

36. Summary Chart – Death From Other Causes

37. Summary Chart – Death From Cancer

38. IV. Testing Usefulness in Real World Situations

39. Questions Should this system be public, or only for use by clinicians? How can the results of this system be best used to contribute to health care provider-patient communications? Can this system contribute to tumor board discussions? For what medical specialties is this system best suited? Oncologist, Surgical Oncologist, Primary Care Physician? Can modifiable risk factors (such as treatment) be added to the system?

40. No Additional Therapy Additional Slides With Selected Additional Therapy 32.3 alive in 5 years 55.5 die due to cancer 12.2 die of other causes 32.3 alive in 5 years 39.9 die due to cancer 13.8 alive due to chemotherapy 14.0 die of other causes Example of Adjuvant! Online Output (http://www.adjuvantonline.com/)

41. Future Directions Testing in clinical settings (tumor board and patient perceptions) –Supplemental grant to the Centers for Excellence in Communications (Kaiser HMO setting) Validation Potential new cancer sites –Head and neck cancers –Breast cancer Adding new comorbidity calculators (NHIS –based) Adding ecologic covariates

42. Collaborators NCI –Angela Mariotto, Minjung Lee, Kathy Cronin, Laurie Cynkin, Antoinette Percy-Laurry IMS –Ben Hankey, Steve Scoppa, Dave Campbell, Ginger Carter, Mark Hachey, Joe Zou Advisory –Dave Penson (Urologist, Vanderbilt) –Deborah Schrag (CRC Oncologist, Dana Farber) –(Consultants - User Interface) Scott Gilkeson, Bill Killiam

44. One Dataset Net probability of dying of Cancer Net probability of dying of Other Causes Cox Model 2 Cox Model 1 Dataset 1 Cancer Patients Net probability of dying of Cancer Net probability of dying of Other Causes Dataset 2 Non-cancer Cox Model 2 Cox Model 1 Crude probabilities dying of Cancer and Other Causes  No need for independence assumption  Minjung used a continuous time model where estimates are computed using counting process*  Estimates and SE’s of cumulative incidence are identical if independence is assumed or not (Nonidentifiability: Tsiatis,1975) * Cheng SC, Fine JP, Wei LJ, “Prediction of the Cumulative Incidence Function under the Proportional Hazards Model”, Biometrics, 54, 1998.  Needs independence assumption of competing risk and that populations are similar*  Can take advantage of the richness of alternative different data sources.  Use discrete time model – CI’s of cumulative incidence computed using delta method Equations are the same