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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 44e 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 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] 2 | JDS | Frank Bretz | May 25, 2011 Drug development process 3 | JDS | Frank Bretz | May 25, 2011 Four clinical development phases Phase Number of Length Study subjects per study population per study Aim I 6 – 20 Weeks – months Healthy Volunteers Pharmacokinetics & -dynamics; single & multiple ascending dose studies; bioavailability 50 – 200 Months Patients (narrow population) Proof-of-concept; dose and regimen finding; exploratory studies 200 – 10’000 Years Patients (broad population) Confirmatory, pivotal studies 1’000 – 1’000’000 Decades Market New label claims & extensions; publication studies; health economics; pharmacovigilance First in human II First in patients III Submission IV Post marketing 4 | JDS | Frank Bretz | May 25, 2011 Why do we need statisticians in the pharmaceutical industry? Remember, one way of defining Statistics is ... The science of quantifying uncertainty, Dealing with uncertainty, And making decisions in the face of uncertainty. ... and drug development is a series of decisions under huge 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 Phase Number of Length Study subjects per study population per study Aim I 6 – 20 Weeks – months Healthy Volunteers Pharmacokinetics & -dynamics; single & multiple ascending dose studies; bioavailability 50 – 200 Months Patients (narrow population) Proof-of-concept; dose and regimen finding; exploratory studies 200 – 10’000 Years Patients (broad population) Confirmatory, pivotal studies 1’000 – 1’000’000 Years Market New label claims & extensions; publication studies; health economics; pharmacovigilance First in human II First in patients III Submission IV Post marketing 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 • ... 21 | 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 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 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 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. 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 = 0.05 28 | JDS | Frank Bretz | May 25, 2011 Bonferroni-Holm test (k = 2) Example with α = 0.05 29 | JDS | Frank Bretz | May 25, 2011 Bonferroni-Holm test (k = 2) Example with α = 0.05 30 | JDS | Frank Bretz | May 25, 2011 Bonferroni-Holm test (k = 2) Example with α = 0.05 31 | JDS | Frank Bretz | May 25, 2011 Bonferroni-Holm test (k = 2) Example with α = 0.05 32 | 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 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 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, 513-518. Bretz, F., Dette, H., and Pinheiro, J. (2010) Practical considerations for optimal designs in clinical dose finding studies. Statistics in Medicine 29, 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 Research 2(4), 487-512. Bretz, F., Maurer, W., Brannath, W., and Posch, M. (2009) A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine 28(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 Association 103(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. 46 | JDS | Frank Bretz | May 25, 2011