Frank Bretz Global Head – Statistical Methodology, Novartis

1. The Role of Statistical Methodology in Clinical Research – Shaping and Influencing Decision Making Frank Bretz Global Head – Statistical Methodology, Novartis Adjunct Professor – Hannover Medical School, Germany Joint work with Holger Dette & Björn Bornkamp; Willi Maurer & Martin Posch 44eJournées de Statistique– 21 au 25 mai 2012, Bruxelles

2. Drug development ... • ... is the entire process of bringing a new drug to the market • ... costs between USD 500 million to 2 billion to bring a new drug to market, depending on the therapy • ... is performed at various stages taking 12-15 years, where out of 10’000 compounds only 1 makes it to the market • drug discovery [10’000 compounds] • pre-clinical research on animals [250] • clinical trials on humans [10] • market authorization [1] | JDS | Frank Bretz | May 25, 2011

3. Drug development process | JDS | Frank Bretz | May 25, 2011

4. Four clinical development phases | JDS | Frank Bretz | May 25, 2011

5. Why do we need statisticians in the pharmaceutical industry? Remember, one way of defining Statistics is ... ... and drug development is a series of decisions under huge uncertainty ! The science of quantifying uncertainty, Dealing with uncertainty, And making decisions in the face of uncertainty. | JDS | Frank Bretz | May 25, 2011

6. Strategic Role of Statisticians • Decision making in drug development • Integrated synthesized thinking, bringing together key information, internal and external to the drug, to influence program and study design • Optimal clinical study design • Specify probabilistic decision rules and provide operating characteristics to illustrate performance as parameters change • Exploratory Data Analysis • Take a strong supporting role in exploring and interpreting the data • Submission planning and preparation • Be integrally involved in the submission strategy, building the plans, interpreting and exploring accumulating data to provide input to a robust and well-thought through dossier | JDS | Frank Bretz | May 25, 2011

7. Examples | JDS | Frank Bretz | May 25, 2011

8. Four clinical development phases 1 – Ph II dose finding study 2 – Ph III confirmatory study | JDS | Frank Bretz | May 25, 2011

9. Example 1 Adaptive Dose Finding | JDS | Frank Bretz | May 25, 2011

10. Notation and framework | JDS | Frank Bretz | May 25, 2011

11. Notation and framework | JDS | Frank Bretz | May 25, 2011

12. Optimal design for MED estimation | JDS | Frank Bretz | May 25, 2011

13. Optimal design for MED estimation | JDS | Frank Bretz | May 25, 2011

14. Adaptive Design for MED estimation | JDS | Frank Bretz | May 25, 2011

15. Priors for parameters | JDS | Frank Bretz | May 25, 2011

16. Procedure: 1) Before Trial Start | JDS | Frank Bretz | May 25, 2011

17. Procedure: 2a) At Interim | JDS | Frank Bretz | May 25, 2011

18. Procedure: 2b) At Interim | JDS | Frank Bretz | May 25, 2011

19. Procedure: 3) At Trial End | JDS | Frank Bretz | May 25, 2011

20. Example 2 Multiple testing problems | JDS | Frank Bretz | May 25, 2011

21. Scope of multiplicity in clincial trials • Wealth of information assessed per patient • Background / medical history (including prognostic factors) • Outcome measures assessed repeatedly in time: efficacy, safety, QoL, ... • Concomitant factors: Concomitant medication and diseases, compliance, ... • Additional information and objectives, which further complicate the multiplicity problem • Multiple doses or modes of administration of a new treatment • Subgroup analyses looking for differential effects in various populations • Combined non-inferiority and superiority testing • Interim analyses and adaptive designs • ... | JDS | Frank Bretz | May 25, 2011

22. Impact of multiplicity on Type I error rate Probability to commit at least one Type I error when performingm independent hypotheses tests (= FWER, familywise error rate) | JDS | Frank Bretz | May 25, 2011

23. Impact of multiplicity on treatment effect estimation Distribution of the maximum of mean estimates from m independent treatment groups with mean 0 (normal distribution) | JDS | Frank Bretz | May 25, 2011

24. Phase III development of a new diabetes drug • Structured family of hypotheses with two levels of multiplicity • Clinical study with three treatment groups • placebo, low and high dose • compare each of the two active doses with placebo • Two hierarchically ordered endpoints • HbA1c (primary objective) and body weight (secondary objective) • Total of four structured hypotheses Hi H1: comparison of low dose vs. placebo for HbA1c H2: comparison of high dose vs. placebo for HbA1c H3: comparison of low dose vs. placebo for body weight H4: comparison of high dose vs. placebo for body weight • In clinical practice often even more levels of multiplicity | JDS | Frank Bretz | May 25, 2011

25. How to construct decision strategies that reflect complex clinical constraints? | JDS | Frank Bretz | May 25, 2011

26. Basic idea • Hypotheses H1, ..., Hk • Initial allocation of the significance level α = α1 + ... + αk • P-values p1, ..., pk • α-propagation • If a hypothesis Hi can be rejected at level αi, i.e. pi ≤ αi, reallocate its level αi to other hypotheses (according to a prefixed rule) and repeat the testing with the updated significance levels. | JDS | Frank Bretz | May 25, 2011

27. Bonferroni-Holm test (k = 2) | JDS | Frank Bretz | May 25, 2011

28. Bonferroni-Holm test (k = 2) Example with α = 0.05 | JDS | Frank Bretz | May 25, 2011

29. Bonferroni-Holm test (k = 2) Example with α = 0.05 | JDS | Frank Bretz | May 25, 2011

30. Bonferroni-Holm test (k = 2) Example with α = 0.05 | JDS | Frank Bretz | May 25, 2011

31. Bonferroni-Holm test (k = 2) Example with α = 0.05 | JDS | Frank Bretz | May 25, 2011

32. Bonferroni-Holm test (k = 2) Example with α = 0.05 | JDS | Frank Bretz | May 25, 2011

33. General definition | JDS | Frank Bretz | May 25, 2011

34. Graphical test procedure | JDS | Frank Bretz | May 25, 2011

35. Main result | JDS | Frank Bretz | May 25, 2011

36. Example re-visited • Two primary hypotheses H1 and H2 • Low and high dose compared with placebo for primary endpoint (HbA1c) • Two secondary hypotheses H3 and H4 • Low and high dose for secondary endpoint (body weight) • Proposed graph on next slide • reflects trial objectives, controls Type I error rate, and displays possible decision paths • can be finetuned to reflect additional clinical considerations or treatment effect assumptions | JDS | Frank Bretz | May 25, 2011

37. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

38. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

39. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

40. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

41. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

42. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

43. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

44. Resulting test procedure | JDS | Frank Bretz | May 25, 2011

45. Now and future • In addition to building and driving innovation internally, important to leverage strengths externally at the scientific interface between industry, academia, and regulatory agencies • At its best, cross-collaboration is greater than the sum of the individual contributions • Synergy on different perspectives and strengths • Provides opportunity to more deeply embed change throughout industry and to have greater acceptance by stakeholders An exciting time to be a statistician ! | JDS | Frank Bretz | May 25, 2011

46. Selected References • Bornkamp, B., Bretz, F., and Dette, H. (2011) Response-adaptive dose-finding under model uncertainty. Annals of Applied Statistics (in press) • Bretz, F., Maurer, W., and Hommel, G. (2011) Test and power considerations for multiple endpoint analyses using sequentially rejective graphical procedures. Statistics in Medicine (in press) • Maurer, W., Glimm, E., and Bretz, F. (2011) Multiple and repeated testing of primary, co-primary and secondary hypotheses. Statistics in Biopharmaceutical Research (in press) • Dette, H., Kiss, C., Bevanda, M., and Bretz, F. (2010) Optimal designs for the Emax, log-linear and exponential models. Biometrika97, 513-518. • Bretz, F., Dette, H., and Pinheiro, J. (2010) Practical considerations for optimal designs in clinical dose finding studies. Statistics in Medicine29, 731-742. • Dragalin, V., Bornkamp, B., Bretz, F., Miller, F., Padmanabhan, S.K., Patel, N., Perevozskaya, I., Pinheiro, J., and Smith, J.R. (2010) A simulation study to compare new adaptive dose-ranging designs. Statistics in Biopharmaceutical Research2(4), 487-512. • Bretz, F., Maurer, W., Brannath, W., and Posch, M. (2009) A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine28(4), 586-604. • Dette, H., Bretz, F., Pepelyshev, A., and Pinheiro, J.C. (2008) Optimal designs for dose finding studies. Journal of the American Statistical Association103(483), 1225-1237. • Bretz, F., Pinheiro, J.C., and Branson, M. (2005) Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics, 61(3), 738-748. | JDS | Frank Bretz | May 25, 2011