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 44 e Journées de Statistique – 21 au 25 mai 2012, Bruxelles

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 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] 2 | JDS | Frank Bretz | May 25, 2011

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

Four clinical development phases PhaseNumber of subjects per study Length per study Study population Aim I First in human 6 – 20Weeks – months Healthy Volunteers Pharmacokinetics & -dynamics; single & multiple ascending dose studies; bioavailability II First in patients 50 – 200MonthsPatients (narrow population) Proof-of-concept; dose and regimen finding; exploratory studies III Submission 200 – 10’000 YearsPatients (broad population) Confirmatory, pivotal studies IV Post marketing 1’000 – 1’000’000 DecadesMarketNew label claims & extensions; publication studies; health economics; pharmacovigilance 4 | JDS | Frank Bretz | May 25, 2011

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. 5 | JDS | Frank Bretz | May 25, 2011

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 6 | JDS | Frank Bretz | May 25, 2011

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

Four clinical development phases PhaseNumber of subjects per study Length per study Study population Aim I First in human 6 – 20Weeks – months Healthy Volunteers Pharmacokinetics & -dynamics; single & multiple ascending dose studies; bioavailability II First in patients 50 – 200MonthsPatients (narrow population) Proof-of-concept; dose and regimen finding; exploratory studies III Submission 200 – 10’000 YearsPatients (broad population) Confirmatory, pivotal studies IV Post marketing 1’000 – 1’000’000 YearsMarketNew label claims & extensions; publication studies; health economics; pharmacovigilance 8 | JDS | Frank Bretz | May 25, 2011

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Phase III development of a new diabetes drug  Structured family of hypotheses with two levels of multiplicity 1.Clinical study with three treatment groups placebo, low and high dose compare each of the two active doses with placebo 2.Two hierarchically ordered endpoints HbA1c (primary objective) and body weight (secondary objective)  Total of four structured hypotheses H i H 1 : comparison of low dose vs. placebo for HbA1c H 2 : comparison of high dose vs. placebo for HbA1c H 3 : comparison of low dose vs. placebo for body weight H 4 : comparison of high dose vs. placebo for body weight  In clinical practice often even more levels of multiplicity 24 | JDS | Frank Bretz | May 25, 2011

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

Basic idea  Hypotheses H 1,..., H k  Initial allocation of the significance level α = α α k  P-values p 1,..., p k α-propagation If a hypothesis H i can be rejected at level α i, i.e. p i ≤ α i, reallocate its level α i to other hypotheses (according to a prefixed rule) and repeat the testing with the updated significance levels. 26 | JDS | Frank Bretz | May 25, 2011

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

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

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

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

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

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

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

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

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

Example re-visited  Two primary hypotheses H 1 and H 2 Low and high dose compared with placebo for primary endpoint (HbA1c)  Two secondary hypotheses H 3 and H 4 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 36 | JDS | Frank Bretz | May 25, 2011

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

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

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

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

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

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

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

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

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 ! 45 | JDS | Frank Bretz | May 25, 2011

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. Biometrika 97,  Bretz, F., Dette, H., and Pinheiro, J. (2010) Practical considerations for optimal designs in clinical dose finding studies. Statistics in Medicine 29,  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 Research 2(4),  Bretz, F., Maurer, W., Brannath, W., and Posch, M. (2009) A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine 28(4),  Dette, H., Bretz, F., Pepelyshev, A., and Pinheiro, J.C. (2008) Optimal designs for dose finding studies. Journal of the American Statistical Association 103(483),  Bretz, F., Pinheiro, J.C., and Branson, M. (2005) Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics, 61(3), | JDS | Frank Bretz | May 25, 2011