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Planning and
Implementing Large
Cardiovascular TrialsCardiovascular Trials
Paul Strumph; Lexicon
Zoran Antonijevic; Cytel
This is a Solution Provider Webinar brought to you by DIA in cooperation
with Cytel, Inc and Lexicon Pharmaceuticals, Inc.
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• Background
• Medical Considerations
St t & D i
Outline
• Strategy & Design
– Strategy #1: Address Pre & Post-Marketing Objectives
Simultaneously
– Strategy #2: Address Pre & Post-Marketing Objectives
Separately
• Sample Size and Duration of ProgramSample Size and Duration of Program
Background

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• 2008: Diabetes Mellitus — Evaluating Cardiovascular Risk
in New Antidiabetic Therapies to Treat Type 2 Diabetes:
FDA Guidance
1. The upper bound of two-sided 95% confidence interval of the risk
ratio of treatment over control is less than 1.8 using integrated
Phase 2 and 3 data. Data from a separate controlled safety study
can be integrated with data from Phase 2 and 3 trials.
2 If pre marketing data result with the upper limit in the range2. If pre-marketing data result with the upper limit in the range
between 1.3 and 1.8 then a post-marketing study needs to be
conducted such that the upper limit based on integrated data that
includes this post-marketing study is less than 1.3.
• In response to this guidance sponsors developing
Cardiovascular Outcomes Trials (CVOT)
diabetes products are now conducting large
CVOTs.
– The cost of these trials is often measured in $100s M.
• The FDA now makes similar requirements for aq
number of other indications.

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• This presentation will begin with medical considerations to
put in into a broader context.
• Cardiovascular trials are event-based. Therefore, strategy
Presentation Flow
Cardiovascular trials are event based. Therefore, strategy
and design will be discussed in terms of numbers of events.
• We will then describe how selected strategies/designs
translate into numbers of patients and length of
development.
• In order to demonstrate these strategies we are using
numerous assumptions that are to our knowledge mostnumerous assumptions that are to our knowledge most
representative of the “real world” scenarios.
• Still, every development program will have its specifics, and
we recommend that your focus is more on concepts than on
assumptions.
Medical Considerations

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• Current Situation
Medical Considerations
• CV Outcome Landscape
• Acceleration of NDA filing
• Serving two masters
Current Situation
• Worldwide epidemic of T2DM (IDF)
– 2011 – 366 million; 2030 – expect 552 million
• Regulatory guidance for demonstration of CV safety in USRegulatory guidance for demonstration of CV safety in US
and EU (post-Avandia meta-analysis):
– Large scale cardiovascular outcomes trials (CVOTs)
– ~5,000-10,000 subjects, 3-5 years, @ cost: $200-400 million
CV Outcomes study is Largest and
Highest Cost Study
Must be designed correctly

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Cardiac Safety Research Consortium
(CSRC)
• https://www.cardiac-safety.org/papers
• Designs and statistical approaches to assess CV risk of
new type 2 diabetes therapies in development. Leader:
Mary Jane Geiger (In Process)
CV Outcomes Trial Landscape:
Current designs
MACE‐primary endpoint Start Drug Size Population NCT
ORIGIN –CV mort 9/2003 Insulin Glargine 12,500 “At risk CVD” prior MI,stroke, revasc 00069784
TECOS (M4) UA‐hosp 12/2008 Sitagliptin 14,000 Preexisting CV D 00790205
M3 = CV death, non fatal MI, non fatal stroke
p g p g
ACE (M3) 02/2009 Acarbose 7,500 Prev MI, ACS, UA, stable Angina 00829660
EXAMINE (M3) 09/2009 Alogliptin 5,400 ACS within 15 to 90 days 00968708
CANVAS (M3) 11/2009 Canagliflozin 4,300 History of or a high risk CVD 01032629
AleCardio (M3) 02/2010 Aleglitazar 7,000 ACS 2‐6 w prior to randomization 01042769
SAVOR TIMI‐53 (M3) 04/2010 Saxagliptin 16,500 EstablCVD and/or multiple RF 01107886
ELIXA (M4) UA‐hosp 06/2010 Lixisenatide 6,000 ACS within 180 days 01147250
EXSCEL (ND) 06/2010 Exenatide LAR 9,500 No CV Inclusion listed 01144338
C‐SCADE 8 (M3) 07/2010 Empagliflozin 7,000 Prior MI, UA, revasc 01131676
CAROLINA (M4) UA‐hosp 10/2010 Linagliptin 6,000 Pre‐existing CVD 01243424
LEADER (M3) 11/2010 Liraglutide 8,750 Concomitant CVD 01179048
REWIND (M3) 07/2011 Dulaglutide 9,600 Established CVD 01394952
ITCA 650 (M4) UA‐hosp 01/2012 Exenatide ITCA650 2,000 Hx of CVD, Stroke or PAD 01455896
Adapted from Gore O, McGuire D. Diabetes & Vascular Disease Research. 9(2) 85-888. 2012
Selected populations vary, as does MACE definition

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• Events = Annual Event rate × Subjects ×
Strategies to accelerate NDA filing
years
• Accelerate NDA filing date by:
– Increasing subjects
– Increasing annual event rate
Accelerate NDA filing – latent period considerations
Secondary Prevention Scandinavian Simvastatin Study Survival Group
Primary Prevention
AFCAPSPrevention of Coronary Heart Disease with Pravastatin
In Men with Hypercholesterolemia

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• Does the diabetic
population selected
bl th ti t
Accelerate NDA filing – Increasing Annual Event
Rate
resemble the patients
who would use the drug
– ACS within 15-90 days
– CKD stage 4
• NDA safety exposure filing requirements
Serving Two Masters
• NDA CV Safety requirements

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Strategy & Design
1. Address pre-marketing and post-marketing
objectives simultaneously.
One meta analysis that includes CVOT that starts in
Strategy
– One meta analysis that includes CVOT that starts in
parallel with Phase 3 trials
– Pre-marketing requirement addressed at interim
analysis
2. Address pre-marketing and post-marketing
objectives separately.j p y
1. Meta-Analysis + CVOT pre-marketing. Another, post-
marketing CVOT to address 1.3
2. Meta-Analysis of Phase 2/3 trials only for pre-
marketing objectives. CVOT post-marketing

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• Event-based trials.
• Sample size considerations.
Design Considerations
– 122 events to achieve the 1.8 requirement, assuming
HR=1.0, Power 90%, two-sided α=0.05
– 611 events to achieve the 1.3 requirement, assuming
HR=1.0, Power 90%, two-sided α=0.05
• Rare Events
There is a lot of ncertaint regarding the “tr e”• There is a lot of uncertainty regarding the “true”
HR, and de-risking is highly recommended
– Interim analyses and sample-size re-assessment
• Meta-analysis
Strategy #1
Address Objectives Simultaneously

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• Address pre-marketing (1.8) and post-marketing
(1.3) objectives simultaneously.
– Sequential testing, once 1.8 achieved test for 1.3.
Example Strategy #1
• Meta-analysis includes data from all controlled trials:
Phase 2b, Phase 3, CVOT.
– CVOT initiated in parallel with Phase 3.
• 1.8 requirement addressed with 2 (could have more)
interim analyses. Assume HR=1.0.
– First interim not to be scheduled before efficacy studies
fi li dare finalized
– Second interim is also serving as final analysis for 1.8.
• 1.3 to be assessed at two additional analyses;
another interim for 1.3 and final for 1.3. Assume
HR=1.0.
Expected Number of Events After
Incorporating Interim Analyses (1.8)
Sample Size (Events) and
Probability of Stopping at Interim
Design Look Expected # of
Events
Interim
(at 75% events)
Final
No Interims 122 122
O’Brien‐
Fleming
93 (69%) 124 103
Pocock 101 (82%) 135 107

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• Type I error for each analysis (1.8 or 1.3)
controlled by α-spending functions
The o erall t pe I error controlled b seq ential
Final Design Chart
• The overall type I error controlled by sequential
testing
– Proceed to testing 1.3 only if 1.8 achieved.
HR<1.8 (75%)
HR<1.3 (15%) HR<1.3 (100%)
HR<1.8 (100%)
HR<1.3 (20%) HR<1.3 (50%)
Begin
0
IA2
124
Final
613
IA1
93
IA3
307
• What if HR≠1.0?
Sensitivity Analysis:
Probability of Stopping for Efficacy
“True”
HR
NI
Margin
IA1
E=93
IA2
E=124
IA3
E=307
Final
E=613
Total
What if HR≠1.0?
HR Margin E 93 E 124 E 307 E 613
0.8 1.8 94% 5% >99%
1.3 0% 2% 88% 10% 100%
0.9 1.8 85% 12% 97%
1.3 0% 0% 60% 39% >99%
1 0 1 8 69% 21% 90%1.0 1.8 69% 21% 90%
1.3 0% 0% 25% 65% 90%
1.1 1.8 52% 25% 78%
1.3 0% 0% 7% 47% 54%

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• One can consider the uSSR to protect against HR > 1.
• Under this strategy an uSSR for 1.8 is of limited
Additional Considerations:
Unblinded Sample Size Re-Assessment
(uSSR)
gy
operational impact, because the enrolment is ongoing.
• Similarly, an uSSR can be implemented for 1.3.
HR<1.8 (75%)
HR<1.3 (15%) HR<1.3 (100%)
HR<1.8 (100%)
HR<1.3 (20%) HR<1.3 (50%)
Begin
0
IA2
124
Final
613
IA1
93
IA3
307
SSR
• Can be incorporated as an extension of
previously described design
Additional Considerations:
Superiority
• Proceed to testing once 1.8 & 1.3 achieved.
• When should superiority be considered?
HR<1.8
HR<1.3 HR<1.3
Sup Sup Sup……
Begin FinalIAx IAy

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Additional Considerations:
Superiority
Sample Size (Events) for Superiority,
Assuming no Interims
HR # Events
0.75 508
0.80 845
0 85 15920.85 1592
0.90 3787
• Population definition: ITT or treated
• Futility assessments: to be considered,
Additional Considerations:
General
particularly for superiority.
• Filing & preserving CVOT integrity.

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Strategy #2
Address Objectives Separately
• Meta-Analysis + CVOT pre-marketing.
• Another, post-marketing CVOT to address 1.3
Address Objectives Separately
• Accrual of events begins from the beginning for post-
marketing trial
HR<1.8
(75%)
HR<1.3
(100%)
HR<1.8
(100%)
HR<1.3
(50%)
Begin
0
Final
124
Final
613
IA1
93
IA1
307
Begin
0

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• Meta-Analysis + CVOT pre-marketing. Another,
post-marketing CVOT to address 1.3
Meta Analysis Including a CVOT trial
• Disadvantages:
– You lose events used to address pre-marketing
requirements
– White space, re-initiation of enrolment
– Overall: increased costs, delays in submission and
achieving post-marketing goalsachieving post marketing goals
• Advantages
– Easier to design and implement
• Meta-Analysis of Phase 2/3 trials only for pre-
marketing objectives. CVOT post-marketing
Ke Challenge ho to ass re s fficient po er?
Meta Analysis Only
• Key Challenge: how to assure sufficient power?
– Efficacy studies smaller in size
– Low expected event rate (<1%)
– To be illustrated in next section
• Could extend efficacy studies, however:
Still t h ffi i t– Still may not have sufficient power
– Delays in approval
– Studies must remain blinded

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Sample Size and Duration of Program
• So far everything has been presented in terms of events
• How does this translate into number of patients and
development times?
Event-Based Trials
development times?
• Projected times of interim and final analyses.
• Impact of combining with meta-analysis.
• To enrich, or not to enrich?
• Monitoring event rates: as planned or not? How to
manage?
• Tools for predicting enrolment and accrual of events
critical during both planning and implementation phase

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Simulation Overview
Trial Design parameters
• sample size
• events model/parameters
• patient visits schedule
• milestones
Enrolment parameters / data
• countries / sites data
• enrolment / dropout rates
• realized enrolment / events data
• Activation plan
Si l iSimulation
Input
SimulateSi l ti Simulate
Scenario
Simulation
Results
Simulate site activation
Simulate patient enrolment
Simulate patient events
Simulate patient dropouts
• Tables / plots
o Enrolment prediction
o Events prediction
o Dropout prediction
o Milestones prediction
Enrolment Simulation Model:
• Poisson model with Gamma Prior for rates
Priors based on initial estimates of site enrolments
Simulation Models
• Priors based on initial estimates of site enrolments
• Bayesian updates of site enrolment rates based on
realized enrolments so far
Events Simulation Model :
• Piecewise Exponential with Gamma Prior for hazard rates
P i b d i iti l ti t f h d t• Priors based on initial estimates of hazard rates as per
the protocol
• Bayesian updates of hazard rates based on known events
and withdrawals until latest follow-up of each patient

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• Number of countries: 28
• Number of sites: 557
Number of patients: 12000
Case Study: Assumptions
• Number of patients: 12000
• Enrolment period ~ 24 months
• Total trial duration ~ 36 months
• MACE rate 4% (high risk population)
• Interim looks: 3
• Events distribution model: exponentialEvents distribution model: exponential
• Milestones: 93, 124, 307, and 613 MACE events
• Trial start: October 2013
• Four studies in normal risk T2DM population.
– Assume annual MACE rate of 0.6%
Efficacy Studies Assumptions
Study Study Design N Enrolled
Total/arm
Arms ST
A NCE vs.
Placebo
240/120 ‐Placebo
‐NCE
24wk
B NCE vs.
Placebo
240/120 ‐Placebo
‐NCE
24wk
C NCE vs. 600/300 ‐NCE 52wk
Glimepiride
/
‐Glimeperide
D NCE vs. TZD 600/300 ‐NCE
‐TZD
52wk

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No. Milestone
Pr. of
Achieving
Pr. of
Achieving
Milestone
Milestones &
Probability of Achieving Them
Milestone
(CVOT only)
(CVOT + Phase
2/3 Efficacy)
1 6000 patients enrolled by 31 December 2014
0.128 0.37
2 12000 patients enrolled by 31 December 2015
0.688 0.858
3 93 MACE events by 31 December 2014 0.596 0.772
44 124 MACE events by 28 February 2015 0.714 0.814
5 307 MACE events by 30 September 2015 0.656 0.77
6 613 MACE events by 30 June 2016 0.666 0.752
N Mil
From Phase
2/3 Effi
Reduced
f
Contribution from Efficacy Trials
No. Milestone 2/3 Efficacy
trials
target for
CVOT
1 93 MACE events by 31 December 2014 6 87
2 124 MACE events by 28 February 2015 6 118
3 307 MACE events by 30 September 2015 12 295y p
4 613 MACE events by 30 June 2016 18 595

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Enrolment Milestones
Probability Distribution
Event Milestones
Probability Distribution

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Enrolment Prediction
• Given assumptions that we used it would be extremely
unlikely that pre-marketing requirement can be achieved
without a CVOT.
Simulations Discussion
without a CVOT.
• Since a small number of events that can be expected
from efficacy trials one could consider using events from
CVOT only (although there are other reasons that may
favor meta-analysis).
• Similar simulations can be implemented to assess the
impact of enrichment on timelinesimpact of enrichment on timelines.
• Such simulations are also necessary to predict timing of
interim and final analysis, and to re-assess timelines if
enrolment and/or event accrual are not as planned.

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• Requirement: 2500 of any length; 1300-1500 > 12 months;
300-500 > 18 months.
• These requirements can be accomplished within sample
Additional Considerations:
NDA Safety Exposure Filing Requirements
These requirements can be accomplished within sample
sizes required to meet the CV requirement.
• However, some planning is required. In our case 93, and
even 124 events would be reached before the 18 months
requirement is met.
• Clearly meta-analysis gives you best chance to minimize
time to filing One can also consider initiating one or severaltime to filing. One can also consider initiating one or several
efficacy studies prior to initiation of CVOT.
• Previously described simulations can be very useful here.
• Large CVOT studies are causing large increases in cost and
risk in drug development in diabetes, and in a number of other
indications.
Th fi l i i hi h d l
Summary
• The first planning step is to assess which development
strategies are reasonable, and/or most efficient in terms of
number of events.
• As the next step simulations should be used to determine what
this means in terms of number of patients, and length of
development.
• Usually a number of iterations are needed to determine best
strategies for number of trials, sample sizes, sequencing, timing
of analysis…
• Once the strategy and design are finalized, simulations are
necessary for predicting, and re-predicting the timing of
analyses