Why Bayesian approaches for CER?

1. How Bayesian approaches for CER? Why Bayesian approaches for CER? Donald A. Berry dberry@mdanderson.org

2. Outline • Bayesian Metaanalysis & CER (ICD) • Adaptive Clinical Trials (I-SPY2) • Modeling in CER using Multifarious Data Sources (CISNET) • Comparing Outcomes—Trials and Tribulations • Bayesian Metaanalysis & CER (ICDs) • Adaptive Clinical Trials (I-SPY2) • Modeling in CER using Multifarious Data Sources (CISNET) • Comparing Outcomes—Trials and Tribulations

3. Bayesian Meta-Analysis for Comparative Effectiveness and Informing Coverage Decisions: Application to Implantable Cardioverter Defibrillators* *Berry SM, Ishak J, Luce B, Berry DA. Medical Care (2010). Disclosure: Berry Consultants contract with Boston Scientific via UBC

4. What Bayes Adds • Model sources of variation • Mortality rates over time: changing hazards • Address possible time-dependent effect of ICD • Cumulative meta-analysis, illustrate effect of each new study: When was evidence conclusive? • Predictive probabilities for future trials

5. Studies Included

6. Bayesian hierarchical modeling of time to death • Model 1: Proportional hazards • Model 2: Time-dependent hazard ratios (modeled separately by year) • Model 3: Hierarchical treatment effects; allow for different treatment effects in different trials

7. Hazard Rates & Survival: Models 1 & 2 Hazard rates Survival probabilities Control ICD Control ICD Model 1 Model 2

8. Results Summary

9. Relative Risks over Time in Model 1

10. Predictive Probabilities over Time Predicted #3 Predicted #1 Observed RR

11. Some Conclusions • ICD Effective: 23% hazard reduction • Effect persistent, consistent • Effect clear early on • Possible to account for changing patient populations

12. Outline • Bayesian Metaanalysis & CER (ICD) • Adaptive Clinical Trials (I-SPY2) • Modeling in CER using Multifarious Data Sources (CISNET) • Comparing Outcomes—Trials and Tribulations

13. Current use of Bayesian adaptive designs • MDACC (> 300 trials) • Device companies (> 25 PMAs)* • Drug companies (Most of top 40)** • CER? Not yet. *http://www.fda.gov/MedicalDevicesDeviceRegulationandGuidance/GuidanceDocuments/ucm071072.htm **http://www.fda.gov/downloads/DrugsGuidanceCompliance RegulatoryInformation/Guidances/UCM201790.pdf

14. Two Recent Pubs

15. A Bayesian statistical design was used with a range in sample size from 600 to 1800 patients.

17. Bayesian adaptive trials • Stopping early (or late) • Efficacy • Futility • Dose finding (& dose dropping) • Seamless phases • Population finding • Treatment finding • Ramping up accrual

18. Why? • Smaller trials (usually!) • More accurate conclusions and hence better treatment for patients, at lower cost (?)

19. I-SPY 2 Slides from press conference … (Change “Phase 2” to CER; “experimental” to “approved”)

20. Outcome: Tumor shrinkage? Population of patients Standard Phase 2 Cancer Drug Trials Experimental arm RANDOMIZE Population of patients Experimental arm Outcome: Longer time disease free Standard therapy

21. Outcome: Tumor shrinkage? Population of patients Standard Phase 2 Cancer Drug Trials Experimental drug Consequence: 60-70% Failure of Phase 3 Trials RANDOMIZE Population of patients Experimental drug Outcome: Longer time disease free Standard + drug

22. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Experimental arm 2 Outcome: Complete response at surgery Population of patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy

23. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Experimental arm 2 Outcome: Complete response at surgery Population of patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy Arm 2 graduates to small focused Phase 3 trial

24. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Outcome: Complete response at surgery Population of patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy Arm 3 drops for futility

25. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Outcome: Complete response at surgery Population of patients Experimental arm 4 Experimental arm 5 Standard therapy Arm 5 graduates to small focused Phase 3 trial

26. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Experimental arm 6 Outcome: Complete response at surgery Population of patients Experimental arm 4 Standard therapy Arm 6 is added to the mix

27. Outline • Bayesian Metaanalysis & CER (ICD) • Adaptive Clinical Trials (I-SPY2) • Modeling in CER using Multifarious Data Sources (CISNET) • Comparing Outcomes—Trials and Tribulations

30. CNN: Statistical Blitz Helps Pin Down Mammography Benefits

31. Fig. 1, Berry JNCI 1998 Updates K G S C O E H M U

32. Fig. 2, Berry JNCI 1998 U

34. CISNET from NEJM Women 40-79 Node-positive BC

35. CISNET from NEJM

36. Percent reductions in BC mortality due to adjuvant Rx and screening

37. Model(s) M

38. Accepted simulations E W M R S G D

39. Model M: Prior to Posterior (2 of several parameters) “the posterior mean effect of tamoxifen is 0.37, corresponding to a 37% decrease in the hazard of breast cancer mortality due to the use of 5 years of tamoxifen for ER-positive tumors in actual clinical practice.” Prior Posterior Posterior Prior

40. Future BC mortality HP 2010 Target Year

41. Keeping track of costs (and their uncertainties) is straightforward with Bayesian simulations

42. Outline • Bayesian Metaanalysis & CER (ICD) • Adaptive Clinical Trials (I-SPY2) • Modeling in CER using Multifarious Data Sources (CISNET) • Comparing Outcomes—Trials and Tribulations

43. Newsweek: “What You Don’t Know Might Kill You” “The right doctors can make all the difference when it comes to treating cancer. So why don't we know who they are?”

44. Survival Outcomes, by Disease Stage Us: Them:

45. 33% longer “Will Rogers Effect” Artifact Comparing Outcomes Median survival (years) Truth is no difference 60% longer Median survival (years) Central Community 100% longer Central Comm Central Comm Local Regional Advanced Overall Stage

46. Using Central Staging Median survival (years) Community Central Comm Central Comm Central Local Regional Advanced Overall Stage

47. 25 20 Using Community Staging 15 Median survival (years) Central 10 Community Community Central 5 Central Comm Central Comm Comm Central 0 Local Regional Advanced Overall Stage

48. Back to Newsweek “A spokesperson for M.D. Anderson Cancer Center in Houston said, ‘We do not have outcomes data at this time,’ while a physician there explained that doctors don't want to release data ‘that's difficult for people to interpret.’”

49. What would Bayes do? Model disease stage, build experiments to bolster weak parts of the model.

50. Outline • Bayesian Metaanalysis & CER (ICD) • Adaptive Clinical Trials (I-SPY2) • Modeling in CER using Multifarious Data Sources (CISNET) • Comparing Outcomes—Trials and Tribulations