A thousand numbers cannot say enough

1. A thousand numbers cannot say enough Communicating Bayesian statistics using examples, intuition and common sense Simon Wandel, Expert Statistical Methodologist, Novartis Oncology – Biometrics and Data Management Slough, June 5, 2014 – PSI 1-Day Scientific Meeting

2. Disclaimer The opinions expressed in this presentation and on the following slides are solely those of the presenter and not necessarily those of Novartis. Novartis does not guarantee the accuracy or reliability of the information provided herein. | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

3. Overview Introduction Single-agent dose-escalation in Oncology Combination dose-escalation in Oncology • Understanding the risk • Changing the mindset Conclusion Acknowledgments References | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

4. Introduction Communicating statistical models Communicating • The «value» of a model: can be complex even for simple statistical models • When deviating from historical standard: breaking with tradition • If no real cases at hand • If potential benefit long-term, but additional work short-term • The «model» itself • To what extent? • Study-specific or a general approach? Selected examples • Abandoning 3+3 escalation in Oncology Phase I • Combination escalation in Oncology | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

5. Introduction Communicating statistical models Communicating Know your customer! | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

6. Single-agent dose-escalation in Oncology Background Phase I Oncology • Untested drug in resistant patients • Escalating dose cohorts (1-6 patients) • Endpoint: dose-limiting toxicity (DLT) • Primary objective: determine maximum tolerable dose • High toxicity potential: safety first | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

7. Single-agent dose-escalation in Oncology Background Phase I Oncology • Untested drug in resistant patients • Escalating dose cohorts (1-6 patients) • Endpoint: dose-limiting toxicity (DLT) • Primary objective: determine maximum tolerable dose • High toxicity potential: safety first ... MTD Cohort 2 N = 3 Dose = 2 Cohort 1 N = 3 Dose = 1 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

8. Single-agent dose-escalation in Oncology Traditional approach Traditional approach: 3+3 escalation (Storer 1989) New cohort at a new dose level: enroll 3 patients DLT = 1/3 DLT = 0/3 DLT > 1/3 Enroll 3 additional pts at the same dose level Go to next lower dose level or declare MTD at next lower dose level if 6 pts already tested (never re-escalate) Go to next higher dose level or same dose if highest dose level DLT = 1/6 DLT > 1/6 Go to next higher untested dose level or declare MTD otherwise Go to next lower dose level or declare MTD at next lower dose level if 6 pts already tested (never re-escalate) | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

9. Single-agent dose-escalation in Oncology Traditional approach Statisticians know 3+3 cannot work well - obvious reasons • Ignores dosage history other than previous cohort • Same action under qualitatively different situations • Cannot re-escalate • Inflexible cohort sizes (either 3 or 6) • Undesirable properties (Goodman 1995; Thall and Lee 2003; Rogatko 2007; Chen et al 2009) • Outperformed by model-based approaches (Yin 2012) We also know • 2-parameter logistic model works well (Neuenschwander et al 2008) • Escalation with overdose control (EWOC) is an accepted principle (Babb et al 1998) | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

10. Single-agent dose-escalation in Oncology 2-parameter logistic regression Model the (true!) DLT rate Natural interpretation of parameters • α is the odds of DLT at the reference dose • β is the change in log-odds of DLT dependent on dose EWOC: Intervals of interest: [0-0.16), [0.16 – 0.33), [0.33 – 1.00] | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

11. Single-agent dose-escalation in Oncology Communicating: motivation Be aware of the differences | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

12. Single-agent dose-escalation in Oncology Communicating: motivation Start with the well-known • What’s the decision using a 3+3 for the following cases: | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

13. Single-agent dose-escalation in Oncology Communicating: motivation Start with the well-known • What’s the decision using a 3+3 for the following cases: • What’s the difference between | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

14. Single-agent dose-escalation in Oncology Communicating: motivation Start with the well-known • What’s the decision using a 3+3 for the following cases: • What’s the difference between | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

15. Single-agent dose-escalation in Oncology Communicating: statistical inference Put yourself in the other’s shoes • Clinicians tend to think from the patient perspective • «I have seen a response in one of my patients, the drug works.» • «I have seen a DLT in 3 patients, the rate is 1/3». | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

16. Single-agent dose-escalation in Oncology Communicating: statistical inference Put yourself in the other’s shoes • Clinicians tend to think from the patient perspective • «I have seen a response in one of my patients, the drug works.» • «I have seen a DLT in 3 patients, the rate is 1/3». Common sense • Had there been 4 patients, could you have seen 1/3? • If the true rate was 1/3, could you see 1 DLT in 4 patients? • 1/3 = 2/6 = 3/9 = ...? | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

17. Single-agent dose-escalation in Oncology Communicating: statistical inference Put yourself in the other’s shoes • Clinicians tend to think from the patient perspective • «I have seen a response in one of my patients, the drug works.» • «I have seen a DLT in 3 patients, the rate is 1/3». Common sense • Had there been 4 patients, could you have seen 1/3? • If the true rate was 1/3, could you see 1 DLT in 4 patients? • 1/3 = 2/6 = 3/9 = ...? Particularly useful: the «chocolate game» | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

18. Single-agent dose-escalation in Oncology Communicating: example Example • New compound • Anticipated doses 25 (starting), 50, 100, 150, 200 • d* = 100 • Prior: log(α), log(β) ~ BVN(µ, Σ) • µ = (log(0.5), 0) • Σ11 = 22, Σ12 = Σ21 = 0, Σ22 = 12 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

19. Single-agent dose-escalation in Oncology Communicating: example Prior | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

20. Single-agent dose-escalation in Oncology Communicating: example Posterior: 0/3 @ 25 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

21. Single-agent dose-escalation in Oncology Communicating: example Posterior: 0/3 @ 25, 1/3 @ 50 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

22. Single-agent dose-escalation in Oncology Communicating: example Posterior: 0/3 @ 25, 2/6 @ 50 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

23. Single-agent dose-escalation in Oncology Communicating: decision making Common sense | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

24. Single-agent dose-escalation in Oncology Communicating: re-assuring evidence Provide re-assuring evidence – data scenarios Model-based approach ensures patients’ safety • Case 1: 0/3 @ 25: possible to escalate • Case 2: 0/3 @ 25, 1/3 @ 50: possible to re-test • Case 3: 0/3 @ 25, 2/3 @ 50: decrease to 25 required • Etc. | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

25. Combination dose-escalation in Oncology Background Combinations are of particular interest in the era of targeted therapies • Multi-inhibition (e.g. MEK/RAF) • Resistance • Backbone + targeted therapy 3+3 even more problematic • Usually, non-neglectible DLT rate at starting dose combination • Considerable probability to observe >1/3 or >1/6 • Escalation path is not straightforward • What constitutes an MTD? | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

26. Combination dose-escalation in Oncology 5-parameter combination model Model the marginal probabilities | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

27. Combination dose-escalation in Oncology 5-parameter combination model Model formulation • Allows incorporation of single-agent information (relevant!) • Accounts for potential interaction Principle of dose-escalation remains the same: EWOC But... • Interaction plays a critical role | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

28. Combination dose-escalation in Oncology Prior information Concepts, not details! Relevant clinical data • For two compounds in early development often «straightforward» • Simpler for safety data than for efficacy (e.g. survival) data Common sense • If we would conduct another single-agent dose-escalation study in the same population • Would we see exactly the same results again? • Would the results of the already conducted study tell us something? • Prior = amount of information = «number of patients» | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

29. Combination dose-escalation in Oncology Example Start with the well-known • Historical single-agent DLT data of compound A • Historical single-agent DLT data of compound B | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

30. Combination dose-escalation in Oncology Example Start with the well-known • Historical single-agent DLT data of compound A • Historical single-agent DLT data of compound B ... but at which combination to start? | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

31. Combination dose-escalation in Oncology Example Prior | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

32. Combination dose-escalation in Oncology Example Predicted probability for number of DLTs (cohort size = 3) A = 6 A = 4.5 A = 3 B = 200 B = 400 B = 600 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

33. Combination dose-escalation in Oncology Example Predicted probability for number of DLTs (cohort size = 3) A = 6 A = 4.5 A = 3 B = 200 B = 400 B = 600 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

34. Combination dose-escalation in Oncology Example Predicted probability for number of DLTs (cohort size = 3) Probability? Call it Risk! • Acceptable risk (20%) to observe ≥ 1 DLT in the first cohort (3/400) • Would 30% (6/400) still be an acceptable risk? Again, selection of starting dose combination involves more than the statistical model • PK • PD • Clincial data • Etc. | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

35. Combination dose-escalation in Oncology Example Changing the mindset – after cohort 1 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

36. Combination dose-escalation in Oncology Example Changing the mindset – after cohort 1 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

37. Combination dose-escalation in Oncology Example Changing the mindset – after cohort 1 A = 6 A = 4.5 A = 3 B = 400 B = 600 B = 800 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

38. Combination dose-escalation in Oncology Example The first few cohorts... | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

39. Conclusion Communicating Complex Statistical Concepts • Communicating the «value» of a model • Communicating the «model» itself • Essential for changing standards (e.g., from 3+3 to model-based) Know your customer! • Not every clinician has the same background / experience • General concepts versus specific cases More a behaviour than an activity: ongoing dialogue | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

40. Acknowledgments • Beat Neuenschwander • Satrajit Roychoudhury • Stuart Bailey • Zhongwen Tang • Alessandro Matano | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

41. References • Babb J, Rogatko A, Zacks S. Cancer Phase I clinical trials: efficient dose escalation with overdose control. Statistics in Medicine 1998. • Goodman S, Zahurak M, Piantadosi S. Some practical improvements in the continual reassessment method for phase I studies. Statistics in Medicine 1995. • Neuenschwander B, Branson M, GsponerT. Critical aspects of the Bayesian approach to Phase I cancer trials. Statistics in Medicine, 2008 • Rogatko A, Schroeneck D, Jonas W, Tighioart M, Khuri F, Porter A. Translation of innovative designs into Phase I trials. Journal of Clinical Oncology 2007 • Storer B. Design and analysis of phase I clinical trials. Biometrics 1989 • Thall P, Lee S. Practical model-based dose-finding in phase I clinical trials: methods based on toxicity.Int J Gynecol Cancer 2003. • Yin G. Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. Wiley, 2012 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough

42. References Published clinical studies (selection) • Saura C, Bendell J, Jerusalem G. Phase Ib study of Buparlisib plus Trastuzumab in patients with HER2-positive advanced or metastatic breast cancer that has progressed on Trastuzumab-based therapy. Clin Cancer Res. 2014 • Rodon J, Tawbi H, Thomas A e tal. A phase I, multicenter, open-label, first-in-human, dose-escalation study of the oral smoothened inhibitor Sonidegib (LDE225) in patients with advanced solid tumors. Clin Cancer Res. 2014 • Shaw A, Kim D, Mehra R et al. Ceritinib in ALK-rearranged non-small-cell lung cancer.N Engl J Med. 2014 • Sessa C, Shapiro G, Bhalla K et al. First-in-human phase I dose-escalation study of the HSP90 inhibitor AUY922 in patients with advanced solid tumors. Clin Cancer Res. 2013 • Markman B, Tabernero J, Krop I et al. Phase I safety, pharmacokinetic, and pharmacodynamic study of the oral phosphatidylinositol-3-kinase and mTOR inhibitor BGT226 in patients with advanced solid tumors.Ann Oncol. 2012 | PSI 1-Day Scientific Meeting | Simon Wandel | June 5, 2014 | A thousand numbers cannot say enough