Frank Bretz Global Head – Statistical Methodology, Novartis
description
Transcript of Frank Bretz Global Head – Statistical Methodology, Novartis
The Role of Statistical Methodology in Clinical Research – Shaping and Influencing Decision Making
Frank Bretz
Global Head – Statistical Methodology, NovartisAdjunct 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
| JDS | Frank Bretz | May 25, 20112
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, 20113
Drug development process
| JDS | Frank Bretz | May 25, 20114
Four clinical development phasesPhase Number of
subjects per study
Length per study
Study population
Aim
IFirst in human
6 – 20 Weeks – months
Healthy Volunteers
Pharmacokinetics & -dynamics; single & multiple ascending dose studies; bioavailability
IIFirst in patients
50 – 200 Months Patients (narrow population)
Proof-of-concept; dose and regimen finding; exploratory studies
IIISubmission
200 – 10’000
Years Patients (broad population)
Confirmatory, pivotal studies
IVPost marketing
1’000 – 1’000’000
Decades Market New label claims & extensions; publication studies; health economics; pharmacovigilance
| JDS | Frank Bretz | May 25, 20115
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, 20116
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, 20117
Examples
| JDS | Frank Bretz | May 25, 20118
Four clinical development phasesPhase Number of
subjects per study
Length per study
Study population
Aim
IFirst in human
6 – 20 Weeks – months
Healthy Volunteers
Pharmacokinetics & -dynamics; single & multiple ascending dose studies; bioavailability
IIFirst in patients
50 – 200 Months Patients (narrow population)
Proof-of-concept; dose and regimen finding; exploratory studies
IIISubmission
200 – 10’000
Years Patients (broad population)
Confirmatory, pivotal studies
IVPost marketing
1’000 – 1’000’000
Years Market New label claims & extensions; publication studies; health economics; pharmacovigilance
1 – Ph II dose finding study
2 – Ph III confirmatory study
| JDS | Frank Bretz | May 25, 20119
Example 1
Adaptive Dose Finding
| JDS | Frank Bretz | May 25, 201110
Notation and framework
| JDS | Frank Bretz | May 25, 201111
Notation and framework
| JDS | Frank Bretz | May 25, 201112
Optimal design for MED estimation
| JDS | Frank Bretz | May 25, 201113
Optimal design for MED estimation
| JDS | Frank Bretz | May 25, 201114
Adaptive Design for MED estimation
| JDS | Frank Bretz | May 25, 201115
Priors for parameters
| JDS | Frank Bretz | May 25, 201116
Procedure: 1) Before Trial Start
| JDS | Frank Bretz | May 25, 201117
Procedure: 2a) At Interim
| JDS | Frank Bretz | May 25, 201118
Procedure: 2b) At Interim
| JDS | Frank Bretz | May 25, 201119
Procedure: 3) At Trial End
| JDS | Frank Bretz | May 25, 201120
Example 2
Multiple testing problems
| JDS | Frank Bretz | May 25, 201121
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, 201122
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)
| JDS | Frank Bretz | May 25, 201123
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, 201124
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
| JDS | Frank Bretz | May 25, 201125
How to construct decision strategies that reflect complex clinical constraints?
| JDS | Frank Bretz | May 25, 201126
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, 201127
Bonferroni-Holm test (k = 2)
| JDS | Frank Bretz | May 25, 201128
Bonferroni-Holm test (k = 2)
Example with α = 0.05
| JDS | Frank Bretz | May 25, 201129
Bonferroni-Holm test (k = 2)
Example with α = 0.05
| JDS | Frank Bretz | May 25, 201130
Bonferroni-Holm test (k = 2)
Example with α = 0.05
| JDS | Frank Bretz | May 25, 201131
Bonferroni-Holm test (k = 2)
Example with α = 0.05
| JDS | Frank Bretz | May 25, 201132
Bonferroni-Holm test (k = 2)
Example with α = 0.05
| JDS | Frank Bretz | May 25, 201133
General definition
| JDS | Frank Bretz | May 25, 201134
Graphical test procedure
| JDS | Frank Bretz | May 25, 201135
Main result
| JDS | Frank Bretz | May 25, 201136
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, 201137
Resulting test procedure
| JDS | Frank Bretz | May 25, 201138
Resulting test procedure
| JDS | Frank Bretz | May 25, 201139
Resulting test procedure
| JDS | Frank Bretz | May 25, 201140
Resulting test procedure
| JDS | Frank Bretz | May 25, 201141
Resulting test procedure
| JDS | Frank Bretz | May 25, 201142
Resulting test procedure
| JDS | Frank Bretz | May 25, 201143
Resulting test procedure
| JDS | Frank Bretz | May 25, 201144
Resulting test procedure
| JDS | Frank Bretz | May 25, 201145
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, 201146
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.