The role of statistical methodology in clinical research shaping and influencing decision making
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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

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Frank Bretz Global Head – Statistical Methodology, Novartis

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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

44eJournées 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]

| JDS | Frank Bretz | May 25, 2011


Drug development process

| JDS | Frank Bretz | May 25, 2011


Four clinical development phases

| 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.

| 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

| JDS | Frank Bretz | May 25, 2011


Examples

| JDS | Frank Bretz | May 25, 2011


Four clinical development phases

1 – Ph II dose finding study

2 – Ph III confirmatory study

| JDS | Frank Bretz | May 25, 2011


Example 1

Adaptive Dose Finding

| JDS | Frank Bretz | May 25, 2011


Notation and framework

| JDS | Frank Bretz | May 25, 2011


Notation and framework

| JDS | Frank Bretz | May 25, 2011


Optimal design for MED estimation

| JDS | Frank Bretz | May 25, 2011


Optimal design for MED estimation

| JDS | Frank Bretz | May 25, 2011


Adaptive Design for MED estimation

| JDS | Frank Bretz | May 25, 2011


Priors for parameters

| JDS | Frank Bretz | May 25, 2011


Procedure: 1) Before Trial Start

| JDS | Frank Bretz | May 25, 2011


Procedure: 2a) At Interim

| JDS | Frank Bretz | May 25, 2011


Procedure: 2b) At Interim

| JDS | Frank Bretz | May 25, 2011


Procedure: 3) At Trial End

| JDS | Frank Bretz | May 25, 2011


Example 2

Multiple testing problems

| 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 performingm independent hypotheses tests (= FWER, familywise error rate)

| 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)

| JDS | Frank Bretz | May 25, 2011


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


How to construct decision strategies that reflect complex clinical constraints?

| 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.

| JDS | Frank Bretz | May 25, 2011


Bonferroni-Holm test (k = 2)

| JDS | Frank Bretz | May 25, 2011


Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


General definition

| JDS | Frank Bretz | May 25, 2011


Graphical test procedure

| JDS | Frank Bretz | May 25, 2011


Main result

| 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

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure

| 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 !

| 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. 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


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