Optimizing the Design of
Clinical Programs
Adaptive Programs (AP)
workstream
Carl-Fredrik Burman, Ph.D., Assoc. Prof.
AstraZeneca R&D and Chalmers Univ. Tech.
Fredrik Öhrn, Ph.D.
AstraZeneca R&D
Thanks to the rest of the AP core team:
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•
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•
•
•
•
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Zoran Antonijevic, Quintiles
Alun Bedding, GSK
Christy Chuang-Stein, Pfizer
Chris Jennison, Bath Univ.
Martin Kimber, Tessella
Olga Marchenko, Quintiles
Nitin Patel, Cytel
José Pinheiro, J&J
• … and to the entire AP network with 30+ members
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Topics for today
• Background: Adaptive Program workstream
• Joint optimization of Phase II and Phase III
• Adaptive Program subteam work
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Background:
Adaptive Program (AP) workstream
Carl-Fredrik Burman
DIA’s Adaptive Design Scientific Working Group
(ADSWG)
• Ongoing workstreams
- KOL lectures
- Adaptive Programs (AP)
- Communication
- Simulations
- Survey
- Material supply (sunsetting)
• Upcoming workstreams
- DMC
- NIH
- Personalized medicine
- Portfolio
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Adaptive Programs (AP)
Background
 Past: Standard trials
 Recent: Optimization of single trials
• Increased interest in study design (optimal design theory,
cross-over, dose choices, etc)
• In particular, much work on Adaptive Designs (including
group-sequential, dose-finding, sample size re-estimation)
 Sometimes: Consider a study in its context
• Modeling – what can be learned from previous information?
• Hand-over – what questions need answers in this trial,
to provide a solid basis for the design of the next phase?
 AP: Optimize the whole program
• Optimal Phase IIB depends on design rule for Phase III
• Optimal Phase III depends on Phase IIB results
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
From study to program perspective
Focus on one
single trial
One trial in
program context
Program
focus
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Adaptive Programs (AP)
Key ideas
• See the whole picture
- Global optimization, not sub-optimization
- Efficacy & safety
• Be specific about values and costs
- Stakeholder analysis
- Clear science-based quantitative assumptions
- Bayesian in, frequentist out
• Focus on key decisions
- Go / No Go
- Sample size
- Dose
- Biomarkers
- Population
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Adaptive Programs (AP)
Ongoing activities
• Main model
• Specific applications
- Neuropathic pain
- Diabetes
- Oncology
• Supporting
- Algorithms & soft-ware
- Academic collaborations
• Communication
- Six scientific articles planned
- Some 20 conference presentations
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Potential model components
(have to prioritize)
Ph IIb
Go / No Go
Design
Prior
information
Project
Prioritization
Ph III
Go / No Go
Design
Benefit/risk
(enters everywhere)
Regulatory
evaluation
Phase IIb
Phase III
Dose
adaptation
Adaptation
Biomarker
in ph IIb?
Two doses?
Seamless?
Commercial
Depends on efficacy,
safety, timing
Uncertain response
(even given data)
Grand (mathematical) problem
Phase II model
• Parametric models (based in pharmacology):
- E(d) for efficacy
- S(d) for safety
• One safety variable may be sufficient for our purposes
- Typically Emax models
d
E0  Emax 

d  ED50
- Prior on all parameters
- Ethical constraints on doses d
• Cost c2(N2), e.g. linear in N2
• Time t2(N2), e.g. linear in N2
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Burman & Öhrn | 14 October 2011
Design parameters
• Go / No Go
• Doses d1,…,dk
• Sizes n1,…,nk (N2=ni)
• (Adaptation)
R & D | Global Medicines Development
Grand problem
Phase III model
• Parameters of efficacy & safety depends on Phase II data
- Posterior
- May allow different endpoints in Phase II and III, with
correlated parameters
• Cost c3(N3), e.g. linear in N3
• Time t3(N3), e.g. linear in N3
Design parameters
• Go / No Go
• Dose d*
• Size N3 (per trial)
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Grand problem
Regulatory / Commercial model
• Value depends on
- Efficacy E (true mean efficacy at selected dose, vs. placebo)
- Safety S
- Total time to market T
- E.g. probit function of clinical utility index (CUI), linear in time

T 

V ( E , S , T )  k1  (k 2 E  k3 S  k 4 )  max 1 
 T

patent 

• No value unless regulatory acceptance, e.g.
- Statistical significant efficacy in two phase III trials
- Pooled estimate of CUI is positive
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Grand problem
Goal function
• Expectation over net value minus total cost
• Optimization over both Phase II and Phase III design
parameters
- Optimal Phase III design depends on Phase II outcome
- Optimal Phase II design depends on optimal Phase III
designs in all possible scenarios, reflecting Phase II outcome
• Many potential extensions, e.g.
- Subpopulation / Personalized Health Care
- Several Phase III trials in different sub-indications
- >1 dose in Phase III
- Earlier phases
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Joint Optimization of Phase II and
Phase III
Fredrik Öhrn
(special thanks to Chris Jennison)
Topics
• Introduction
• Model
• Design of Phase III
• Design of Phase II with Phase III in mind
• Group Sequential Phase III
• Extensions with multiple doses
A Common Situation
• Different endpoints in Phase II and Phase III
• Differences in Study Length, Population etc.
- Perhaps a broader population in phase III
- Longer follow-up in Phase III
• Phase III often expensive
Phase III go/no go decision important
• Phase II trial can reduce uncertainty but comes at a price:
- Cost of running trial
- Limited patent life
Relevant Questions
• How to model Phase II and Phase III endpoints?
• Phase III sample size?
• Phase II sample size?
• Quantitative support for go/no go decision
• Further efficiencies could be achieved through
seamless Phase II/III trials but not focus here
• Focus on one active does and a control in Phases II and III
A Bayesian Framework
• Express prior belief and associated uncertainty
• Denote the mean treatment effect by  in Phase II and  in Phase III
• Suppose  and  follow bivariate normal distribution
• Correlation r is crucial for the properties of the model
• Express prior belief about  and  before start of Phase II
• Update for posterior distribution after Phase II
A Mixed Bayesian/Frequentist Approach
• Frequentist requirement at end of Phase III
- May not be as important in Phase II
• Bayesian approach to model endpoints
• Specifying prior distributions may be difficult
- Ideally supported by previous studies
• Cost of phase II trial a2 + c2 n2
• Cost of phase III trial a3 + c3 n3
• Gain function g(z3,,n2,n3)
- Can depend on sample sizes in Phase II and Phase III
- True and estimated treatment effect in Phase III
Expected Utility to be Maximised


a

c
n

I
(
z

z
2
2
2
2

*
2
)
  a3  c3n3  I ( z3  z ) g ( z3 , , n2 , n3 )
*
3
 f ( z2 ) f ( z3 |  , z2 ) ( | z2 ) dz2 dz3d
Probability of Success (PoS)
• Account for uncertainty about treatment effect
• Integrate over prior distribution for effect size, (|z2) :
- (|z2) is posterior distribution after Phase II
- Can be thought of as prior before Phase III
• PoS is in this context sometimes called average power or assurance
• PoS given progress to Phase III is an important metric:
- Crucial to avoid costly failures in Phase III
- Example shown on a subsequent slide
Sample Size of Phase III Trial
• Assume prior distributions for relevant parameters
• Define gain function and cost of sampling
• We let gain function depend on
- Time (sample sizes), true effect size and/or estimated effect size
- Fixed gain function for illustrative purposes on next slide
• Can find expected utility of running trial
• Use decision analysis to find phase III sample size n3
Optimisation of Phase III Sample Size
But…
• Many other factors to consider
• Phase III sample size often driven by safety
• Suppose Phase III sample size is either
- Fixed due to constraints not in our model
- Optimised based on prior after Phase II
• Move one step back
• Focus on design of Phase II
• With approach to choosing Phase III sample size in mind
Scope for Phase II Trial
• Today, focus on trial with two treatment arms
- Support go/no go decision
- Guide Phase III sample size
• Briefly mention approach for multidose Phase II
- Four parameter Emax model
- More computationally challenging
Phase II Sample Size
• Can reduce uncertainty
• A more informed investment decision
• Can derive decision rule for progression
• Similar approach possible with multiple doses
• Important to consider other factors
• Information collected for a certain cost
PoS vs Phase II Sample Size
Solid and dashed lines display biomarkers with high and low
correlation, respectively. We note a substantial increase in PoS
in phase III for the former, but little impact for the latter.
Phase II Investment Decision
• Increasing Phase II sample size can help to reduce
Phase III attrition but…
• More expensive Phase II
• Longer duration
delay to start of Phase III and potential launch
• Other differences compared to Phase III
- Endpoint is often not the same
- Shorter follow-up time
- Information may be cheaper
Choice of Biomarker
• Suppose different biomarkers are considered
• Different costs of sampling and correlation
• May represent different follow-up times in Phase II
• Natural to focus on decrease in posterior variance of 
• Adjust for Phase II cost
• Can be expressed analytically as function f(r,c2,parameters in prior distribution)
• Calculate for different biomarkers
• For a given cost, which biomarker decreases posterior variance the most?
Choice of Biomarker
More strongly correlated biomarkers dominate for large investments!
Group Sequential vs Fixed Sample Phase III
• Consider group sequential design (GSD) with K interim analyses (IA)
• Each IA provides a go/no go decision rule
• Role of Phase II becomes less important
• Can still be helpful if observations are very cheap
• Not obvious how to model cost of stopping at IA
- Savings may be small due to large start-up cost
- Survival setting may be different
- In what follows expected cost under prior is charged
Benefits with GSD
GSD Summary
• Modest gains by updating Phase III sample size based on Phase II
- Greater efficiency gains by adding more IA
- In keeping with results within trials
- Schmitz designs versus group sequential designs
• Now consider if this holds for multidose Phase II
Extending to Multiple Doses
• Chris Jennison studied 4-parameter Emax model
• Treatment effect increasing with dose
• Needs to model safety
• Calculate posterior distribution
• Right dose as trade-off between efficacy and safety
• Impact of GSD in Phase III similar to for two-arm Phase II trial
• Brief results on next slide, courtesy of Chris Jennison
Optimal Phase II Sample Size for 7-arm Phase II Trial
For GSD in Phase III, Similar as for Two-arm Trials
Optimal Phase II
sample size is
substantially
lower than for
fixed sample
Phase III trial!
Summary
• A mixed Bayesian and frequentist framework
• Can solve decision problem to optimise various quantities
• Phase II and Phase III sample sizes n2 and n3
• Choice of endpoint in Phase II
• Optimal Phase II sample sizes lower with GSD in Phase III
• Number of groups more important than updating sample size
• Holds also for multidose Phase II
• Latter problem more complex to solve
Work in AP subteams
Carl-Fredrik Burman
(thanks to Nitin Patel, Zoran Antonijevic,
Olga Marchenko and other subteam
members)
Diabetes subteam
Team members
Martin Kimber, Tessella (lead)
Zoran Antonijevic, Quintiles (ex-lead, driving ms #1)
Klas Bergenheim, AZ
José Pinheiro, J&J
David Manner, Lilly
Carl-Fredrik Burman, AZ
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Diabetes subteam
Framework
• Key endpoints
- Efficacy: HbA1c, at 24 weeks in phase III
- Safety: Hypoglycemic events
• Dose-limiting in phase IIB
• More a payer than regulatory concern.
- Safety: CV events
• Regulatory requirements both pre- and post-
• Trial program
- Phase IIB dose-finding
- Phase III, 1st line treatment
• X vs Placebo (superiority)
- Phase III, 2nd line treatment
• X vs Placebo vs Active control (superiority + non-inferiority)
- Phase III, 3rd line treatment
• X vs Placebo vs Active control (superiority + non-inferiority)
- CV safety (meta-analysis above trials + CV safety study)
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Diabetes subteam
Model
• Costs
- Linear in sample size
- Cost per patient depending on phase
• Value
- No value unless regulatory acceptance
• At least two significant trials
• CV fulfils regulatory requirements
- Value depends heavily on claims (superiority / noninferiority) in different market segments (1st, 2nd, 3rd line)
- Value decreases with hypoglycemic event (AE) rate
• Interaction between claim and AE rate
- Time factor
• Prior uncertainty in key parameters
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Diabetes subteam
Sketching the problem
• Design parameters to optimize
- Phase II
• Sample size
• Treatment duration, 12 vs 24 weeks
• Adaptive vs. fixed
- Phase III
• Go / No Go criteria
• Sample size
- (CV safety study)
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Diabetes subteam
Sketching the solution
• Lots of simulations (thanks Martin and David!)
• Check different (discrete) design scenarios
• MCMC to update prior for parameter vector
- Note: complicated dependence structure
• For results … see Zoran’s presentations
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Neuropathic Pain subteam
Team members
Nitin Patel, Cytel (lead)
Christy Chuang-Stein, Pfizer
Jim Bolognese, Cytel
José Pinheiro, J&J
David Hewitt, Merck
+ a number of associated members
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Neuropathic Pain subteam
Sketching the problem
More mature project than Diabetes – but I’m no expert
• Impact of Phase II
- Sample size
- Number of doses
- Adaptive designs
• on probability of success (PoS), expected net present value
(ENPV), financial risk
• Value depends on both efficacy & safety
• Time factor
• Discrete doses
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Oncology subteam
Team members
Olga Marchenko, Quintiles (lead)
Don Berry, MD Anderson Cancer Center
Joel Miller, ImClone
Tom Parke, Tessella
Inna Perevozskaya, Pfizer
Yanping Wang, Lilly
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Oncology subteam
Newly started work
Plan to finalize simulation plan in October
May cover other questions than other subteams
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
Discussion
• We’ve come a long way
… but the journey’s just begun
• In 10 years, people will hopefully think what we’ve done now
is very immature
• Future work
- Increased realism (Apply to real projects – Confidentiality)
- More continuous models
- Benefit / risk (connect with evolving regulatory B/R thinking)
- Subpopulations
- Etc. etc.
• Any volunteers?
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development
To be continued …
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T: +44(0)20 7604 8000, F: +44 (0)20 7604 8151, www.astrazeneca.com
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Burman & Öhrn | 14 October 2011
R & D | Global Medicines Development