Transcript An Update on FDA`s Critical Path Initiative Statistical Contributions

An Update on FDA’s Critical Path
Initiative
Statistical Contributions
Robert T. O’Neill Ph.D.
Director , Office of Biostatistics
Center for Drug Evaluation and Research
Presented at the 2005 FDA/Industry Statistics Workshop: September 14-16, 2005
Marriott Wardman Park Hotel, Washington, DC
The Critical Path Initiative
 Refers to the product development path from
candidate selection to product launch
Covers drugs, biologics, and medical devices –
but today’s talk is mostly about drugs / biologics
Initiative was announced publicly by Dr.
McClellan Tuesday, March 16, 2004
What the “Critical Path” Is
 A serious attempt to bring attention & focus to the need for
more scientific effort and publicly-available information on
evaluative tools
 Evaluative tools: The techniques & methodologies needed to
evaluate the safety, efficacy & quality of pharmaceuticals as
they move down the path
Despite Advances in Science, Success
Rate of Product Development has
NOT Improved
New compounds entering Phase I
development today have 8% chance of
reaching market, vs. 14% chance 15 years ago.
Phase III failure rate now reported to be 50%,
vs. 20% in Phase III, 10 years ago.
Perceived Problem:
The development process itself is
becoming a serious bottleneck
 Current applied science and infrastructure date from last century
Funding and progress in Development science has not kept
pace with basic biomedical science.
 Science to evaluate safety and efficacy of potential new medical
products, and enable manufacture, is different from basic
discovery science.
 Need to fill gap in applied science-- to increase productivity and
efficiency --to reduce cost of development process.
Stakeholder Input:
Overwhelming Support
Overwhelming concurrence with:
recognition of science infrastructure
problem
CP Initiative focus on research and
collaboration,
We heard this from: drug industry, patient
groups, device companies and groups,
biotech companies, others
This is what we heard !
Demand Exceeds Supply
 Docket Demand for FDA Action Exceeds
FDA Capacity: Far more proposed than FDA
can undertake.
 Principles for setting priorities for FDA
actions are on Science Board agenda.
Overriding Concerns
 Clinical Trials
 Biomarkers and Endpoints
What is the problem
 Phase III trials are failing at a rate that is
higher than expected - root causes ?
 What is the typical planning process for
drug development / phase 3 trials
 What can we change; what new tools can
we use, and what can we do better in the
future to improve Phase III success and
efficiency of drug development
Possible solutions / strategies
Can statisticians help ?
 Are new study designs needed

Impetus for Adaptive designs, two stage designs, enriched target
population designs
 Are we planning correctly - Rethink how the study
planning process occurs

It’s the dose

It’s the scenario needing better planning - or analysis methods

Bring consensus / closure to most pressing statistical issues at the core of
decision making

Get involved in new emerging subject matter areas and impact them -genomics,
proteonomics, nanotechnology

Broaden the multi-disciplinary roles, in industry, academia and regulatory
bodies - internationally
Our Proposal for
the Critical Path

Conduct Research , Gain Consensus, and
Develop Guidance to Remove Obstacles to
Efficient Drug Development and Enhance
Success Rates of Clinical Trials

Improve the Processes and Approaches to
Quantitative Analysis of Clinical Safety Data
from Clinical Trials to Enhance Risk
Assessment and Management Initiatives

Improve the Statistical Understanding and
Application of Modern Statistical
Approaches to Product Testing and Process
Control
Clinical Trial Proposals for the Critical Path
 Missing data due to patient withdrawals and dropouts in
clinical trials
 Flexible / adaptive clinical trial designs to improve the
information and success rate of trials
 Non-inferiority active control studies when placebos can't
be used - getting to consensus on appropriate methods for
margin setting, data analysis and interpretation for various data
rich and data poor scenarios
 Development of consensus on the statistical handling of
multiple endpoints in clinical trials.
 Clinical trial modeling and simulation as a tool for better
design and interpretation of clinical trials
 Application of Bayesian Methods to Enhance the Success
Rate of Clinical Trials
Prioritize Efforts - Three
separate yet related approaches
 Guidance Development
 Multiple endpoints
 Non-inferiority
 Topics of high interest
 Adaptive / Flexible designs
 Modeling / simulation / planning/Phase 2a
 Other Critical Path needs: safety , product quality
Safety and Quantitative Risk
Assessment
Clinical Trials - Pre-Marketing
 Methods of application
 Planning, data collection, statistical analysis plan
 Process

Newly formed statistical safety team for
more concentrated and focused advice
 Earlier planning, modeling and simulation
FDA Risk Management Guidances
Life cycle of a drug
 Premarketing Risk Assessment
(Premarketing Guidance)
 Development and Use of Risk
Minimization Action Plans (RiskMAP
Guidance)
 Good Pharmacovigilance Practices and
Pharmacoepidemiology Assessment
(Pharmacovigilance Guidance)
Enhancing Product Quality
Modern in process testing raises the
possibility that alternatives to product
quality should be considered
There have also been advances in Process
Analytical Technology (PAT) which
depends on in process assessment of
product quality all along the drug
manufacturing process
The Non -Inferiority Problem
Current guidance is inadequate and the
issues are poorly understood - must be fixed
 Term introduced in ICH E9 ‘Statistical
Principles for Clinical Trials’
 Some issues described in ICH E10 ‘Choice
of Control Groups’
 A study design that provides an indirect
measure of evidence of efficacy / safety
What are the various objectives
of the non-inferiority design
 To prove efficacy of test treatment by indirect
inference from the active control treatment
 To establish a similarity of effect to a known
very effective therapy - e.g. anti-infectives
To infer that the test treatment would have
been superior to an ‘imputed placebo’ ; ie.
had a placebo group been included for
comparison in the current trial. - a new and
controversial area - choice of margin is the key
To preserve a specified % effect of the AC
How is the margin “  “ chosen based
upon prior study data
 For a large treatment effect, it is easier - a
clinical decision of how similar a response
rate is needed to justify efficacy of a test
treatment - e.g. anti-infectives is an example.
 For modest and variable effects, it is more
difficult ; and some approaches suggest
margin selection based upon several
objectives.
Complexities in choosing the margin
(how much of the control treatment
effect to give up)
 Margins can be chosen depending upon which of these
questions is addressed:
 how much of the treatment effect of the comparator can
be preserved in order to indirectly conclude the test
treatment is effective - a clinical decision for very large
effects; a statistical problem for small and modest
effects
 how much of a treatment effect would one require for
the test treatment to be superior to placebo, had a
placebo been used in the current active control study - a
lesser standard than the above
How convincing is the prior
evidence of a treatment effect ?
 Do clinical trials of the comparator treatment
consistently and reliably demonstrate a treatment
effect - when they do not, what is the reason ?
 Study is too small to detect the effect - under
powered for a modest effect size
 The treatment effect is variable, and the
estimate of the magnitude will vary from
study to study, sometimes with NO effect in
a given study - a BIG problem for active
controlled studies (Sensitivity to drug effect)
Importance of the assumption of
constancy of the active control treatment
effect derived from historical studies
 It is relevant to the design and sample size of the
current study, to the choice of the margin, to the
amount of bias built into the comparisons, to the
amount of effect size one can preserve (both of
these are likely confounded), and to the
statistical uncertainty of the conclusion.
 Before one can decide on how much of the effect
to preserve, one should estimate an effect size for
which there is evidence of a consistent
demonstration that effect size exists.
Four approaches to the problem
 The simple case: specify a delta - not estimated
 Indirect confidence interval comparisons (ICIC)
(CBER/FDA type method, etc.)
- thrombolytic agents in the treatment of acute MI
 Virtual method
(Hasselblad & Kong, Fisher, etc.)
- Clopidogrel, aspirin, placebo
 Bayesian approach (Gould, Simon, etc.)
- treatment of unstable angina and non-Q wave MI
Current Guidance on Multiple
Endpoints is inadequate
Multiple primary endpoints
Multiple secondary endpoints
Composite endpoints
Multiple composites
Hierarchies
Patient reported outcomes
Decision Criteria for success
A collaborative effort: PhRMA 2004 meeting on coprimary endpoints, manuscript
Emerging Interest in Adaptive /
Flexible Trial Designs
 Adaptive designs
 Enrichment / pharmacogenomics
 Sample size re-estimation
 Design modification
New study designs
Why a need for adaptive /
flexible designs ?
 Enriching trials with patients having
genomic profiles likely to respond or less
likely to experience toxicity
 Goal of an adaptive / flexible design
 Mid study changes that prospectively
plan for modifications that preserve
Type 1 errors and maximize chances
for success
Information adaptive designs /
flexible designs
Controversial
Statististical Methodology is Available
Why and where to use them?
Why the need for adaptation?
Design specifications often entail at least partial
knowledge of the values of many planning (primary
or nuisance) parameters that are unknown or at best
might be guessed crudely
Sample size planning entails “educated” guess of
effect size.
Selection of a composite endpoint requires
“educated” guess of where the potential effects lie
and what noises may be.
Others…..
Hung
Addressing a process issue:
Scenario Planning:
A Tool to Increase the Success Rate of Phase III trials
and to Enhance Drug Development Planning
Incorporates:
Several linked linked study phases - continuum
Multiple endpoints
Missing data
Use of all information in the process
Safety Planning
Modeling and simulation
Flexible designs / development sequence / international
What is Scenario Planning
 Modern approach to protocol planning and choice of
clinical study designs
 Utilizing models for disease progression and endpoint
selection
 Utilizing simulation strategies for what if scenarios
 Assumes input from other studies and planning
efforts - planned sequences of studies may matter
 An aid for prospectively planning integrated analyses
Disease Progression Modeling
 Endpoint selection and evaluation
 Trial Duration determination
 Frequency and number of subject
measurements
 Tradeoffs between clinical endpoints and
patient reported outcomes
 Evaluate impact of missing data, informative
treatment related censoring
 Evaluate multiplicity implications
What would be observed if subjects had stayed in trial ?
Impute values from subects staying in longer
Test
Control
Which path do
you choose ?
Higher
is bad
1
2
3
Visit
4
5
Disease Progression Models and
Clinical Outcomes
What model captures the functional
relationship of the disease progression and
the clinical outcome(s) to be used to
measure treatment effect
Can one function capture each of the clinical
outcomes adequately
If not are several disease progression
models used to express ‘response’
Modern Protocol /Development
Planning
Sensitivity / Scenario planning
 Different statistical tools and strategies
 Challenge and explore assumptions
 More multidisciplinary involvement
 It is more than sample size planning
 Structured planning meetings that are different
that current – formal Q & A’s not broad enough
 Links between phase planning and modeling
efforts – currently too limited and stove piped
Concluding remarks
Meeting the Challenges of the Critical Path will
require collaboration and resource allocation
 Multidisciplinary / collaborative planning and evaluation
is needed now more than ever because issues becoming
more complex - guidances can’t solve this - resources,
exposure, experience, training will
 Efforts to move available appropriate statistical methods
and concepts , possibly more complex, into the main
stream by emphasis on understanding by the audience
appropriate to the application
 Guidances don’t help here - need resources that can
understand and communicate
 Efforts to maximize contributions of industry, academic
and regulatory statisticians
Concluding remark
-Priority setting  Choosing the most pressing needs and the
chances for success - currently being
updated
 This is a national effort - not just FDA’s
initiative - it will take a major coordinated
effort to make progress