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

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

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 d evelopment process

Drug development process

| JDS | Frank Bretz | May 25, 2011


Four clinical development phases

Four clinical development phases

| JDS | Frank Bretz | May 25, 2011


Why do we need statisticians in the pharmaceutical industry

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

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


Frank bretz global head statistical methodology novartis

Examples

| JDS | Frank Bretz | May 25, 2011


Four clinical development phases1

Four clinical development phases

1 – Ph II dose finding study

2 – Ph III confirmatory study

| JDS | Frank Bretz | May 25, 2011


Frank bretz global head statistical methodology novartis

Example 1

Adaptive Dose Finding

| JDS | Frank Bretz | May 25, 2011


Notation and framework

Notation and framework

| JDS | Frank Bretz | May 25, 2011


Notation and framework1

Notation and framework

| JDS | Frank Bretz | May 25, 2011


Optimal design for med estimation

Optimal design for MED estimation

| JDS | Frank Bretz | May 25, 2011


Optimal design for med estimation1

Optimal design for MED estimation

| JDS | Frank Bretz | May 25, 2011


Adaptive design for med estimation

Adaptive Design for MED estimation

| JDS | Frank Bretz | May 25, 2011


Priors for parameters

Priors for parameters

| JDS | Frank Bretz | May 25, 2011


Procedure 1 before trial start

Procedure: 1) Before Trial Start

| JDS | Frank Bretz | May 25, 2011


Procedure 2a at interim

Procedure: 2a) At Interim

| JDS | Frank Bretz | May 25, 2011


Procedure 2b at interim

Procedure: 2b) At Interim

| JDS | Frank Bretz | May 25, 2011


Procedure 3 at trial end

Procedure: 3) At Trial End

| JDS | Frank Bretz | May 25, 2011


Frank bretz global head statistical methodology novartis

Example 2

Multiple testing problems

| JDS | Frank Bretz | May 25, 2011


Scope of multiplicity in clincial trials

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

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

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

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


Frank bretz global head statistical methodology novartis

How to construct decision strategies that reflect complex clinical constraints?

| JDS | Frank Bretz | May 25, 2011


Basic idea

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

Bonferroni-Holm test (k = 2)

| JDS | Frank Bretz | May 25, 2011


Bonferroni holm test k 21

Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni holm test k 22

Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni holm test k 23

Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni holm test k 24

Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


Bonferroni holm test k 25

Bonferroni-Holm test (k = 2)

Example with α = 0.05

| JDS | Frank Bretz | May 25, 2011


General definition

General definition

| JDS | Frank Bretz | May 25, 2011


Graphical test procedure

Graphical test procedure

| JDS | Frank Bretz | May 25, 2011


Main result

Main result

| JDS | Frank Bretz | May 25, 2011


Example re visited

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

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure1

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure2

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure3

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure4

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure5

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure6

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Resulting test procedure7

Resulting test procedure

| JDS | Frank Bretz | May 25, 2011


Now and future

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

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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