1. The Application of Adaptive
Sample Size Re-estimation in
Event Outcome Confirmatory
Clinical Trials
Yili L. Pritchett, PhD
East User Group Meeting
October 12, 2012

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Outline
• “Learning” and “Confirmatory” clinical trials
• A case study: predictivity of change in albuminuria to mortality,
CV outcome, or renal outcome events
• Adaptive sample size re-estimation (SSR) design for an event
outcome study
• Comparisons between adaptive SSR and group sequential
design approach
• Concluding remarks

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Background
• Clinical trials testing a New Molecular Entity (NME) can be
classified as “learning” or “confirmatory” (Sheiner, 1997)
• The ideal case: parameters (mostly on the efficacy side) are
well learned during the learning phase, and they will guide the
design of the confirmatory studies
• E.g., development program for a new antidepressant
• For this type of indication, study endpoints can be and should
be consistent between learning and confirmatory trials

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However, there are exceptions ……
• Indications that need data to demonstrate test drug’s effect in reducing
of certain detrimental outcome events are likely to fall in this category
• Because sufficiently large sample size and long duration of follow-up
are needed to observe the occurrence of events, it is not viable to
design such study for just to learn
• Most event outcome studies are designed at Phase 3 level
• How to learn about the NME at early stage of development?
--- Use surrogates or biomarkers
• How to make decision of Go/No Go for Phase 3?
--- Based on hypothesis or evidence of predictivity that infers treatment
effect on event outcomes from its effect on biomarkers

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The Meaning of “Confirmatory” Has Been Modified
in Such Clinical Development Programs
• The Phase 3 is not to replicate and confirm a treatment effect that was
observed in early phase, but to observe the hypothesized treatment
effect that has not yet been seen
• As such, the NME might enter the confirmatory phase with a great
deal of uncertainty and high risk of failure
• The uncertainties associated with confirmatory event outcome trials
include but are not limited to:
- Treatment effect, quantified by hazard ratio (HR)
- Placebo group event rate

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Example: Change in Albuminuria and the Outcomes
of Cardiovascular and Renal Events
• Albuminuria is a medical condition often diagnosed by elevation of
urinary albuminuria/creatinine ratio (UACR)
• Data from two prospective trials on telmisartan (ONTARGE and
TRANSCEND) were combined to assess the predictivity of changes in
albuminuria on mortality, CV, and renal outcomes (Schmieder et al., 2011)
Subjects with UACR at
both baseline and 2-year visit
N=23,480
Subjects with 50%
decrease, N=4994
% change in UACR
-50% 0 100%
Subjects with 100%
increase, N=6518
Subjects with minor
changes, N=11,968
Albuminuria worsenedAlbuminuria improved

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UACR Value at Baseline was Associated with Mortality
Annual Motality Rate
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
< 10 [10, 30) [30, 100) [100, 300) 300
UACR Value at Baseline (mg/g)

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Definitions and Data Analysis Methods
• Events of interest: All cause mortality, CV death, Composite CV
endpoint, and combined renal endpoint.
• Composite CV endpoint: cardiovascular death, myocardial
infarction, stroke, and hospitalization for heart failure
• Combined renal endpoint: doubling of serum creatinine, needing for
dialysis or renal transplant.
• The group of subjects with minor changes in UACR was taken as the
reference group
• Hazard ratio were estimated from a Cox model adjusting for age, sex,
body mass index, smoking, alcohol consumption, eGFR, plasma glucose,
systolic and diastolic BP and HR at baseline, BP change and eGFR change
within 2-year, treatment, and diagnosis at entry.

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Change in UACR Predicted Event Outcomes
Figure 1. Schmieder et al. 2011

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Relationship between UACR Reduction and
Relative Risk of Renal Outcomes
Data source: Lambers Heerspink and De Zeeuw Nephron. Clin. Prac. 2010
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Figure 1. Lambers Heerspink and De Zeeuw Nephron. Clin. Prac. 2010

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Accumulated Data and Research Suggest ……
• Albuminuria is powerful predictor of outcome of those events of
interest
• More importantly, change in albuminuria predicted outcome of these
events
• Thus, if a treatment can reduce albuminuria, it should likely have the
effect on reducing the rates of these events

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A hypothetical clinical program where almuninuria is
used as a biomarker
POC study
(8-12 weeks)
Dose-finding study
(8-12 weeks)
Event outcome study (3-5 years)
Primary efficacy endpoint:
Change in UACR
Primary efficacy endpoint:
time-to-event
Time

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At the time of designing the event-driven confirmatory study,
there are knowns and unknowns
• It is known that the test drug has effect on UACR reduction
• It is predicted that the test drug can achieve a hazard rate reduction in
the range between 25% and 30% (~HR=0.75 – 0.70) in those who
achieved albuminuria reduction under the treatment
• It is also known that a HR of 0.80 is clinically meaningful and can make
the test drug a viable treatment
• If the predictive model is correct, one should power the study for an HR
in the range of 0.70-0.75
• Alternatively, one can take a conservative approach to power the study
to detect an effect of 20% event reduction
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Assumptions for Initial Sample Size Calculation
• Hazard Ratio = 0.75
• Annual placebo event rate = 0.07
• One-sided =0.025, power = 90%
• An interim analysis will be performed when 50% of the events have been
collected
• Futility stopping: Gamma (-6); no plan to stop for success
• O’Brien-Fleming alpha-spending function is applied
• Accrual: 2 years, follow-up: 4 years
Required number of events d = 509 required total sample size N=3123
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The Adequacy of Sample Size and Potential Study
Power Are Conditional on the True Effect Size
90%
94%
98%
80%
71%
40
50
60
70
80
90
100
0.7 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8
Assumed True HR
Power
If true HR is in this area,
sample size targeted on
HR=0.75 is more than
adequate If true HR is in this area,
original sample size are
running short
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4651
5191
3123
N
A solution: use adaptive,
sample size re-estimation design

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Adaptive Sample Size Re-estimation Using
“Promising Zone” (Mehta and Pocock, 2011)
• Conduct an interim analysis when 1/2 of the events (d=255) have been
collected
• If interim efficacy data show compelling treatment effect, no sample size
adjustment; if interim efficacy data do not support a viable treatment effect,
no sample size adjustment
• If interim efficacy data are somewhat less than the original target but
promising, increase the sample size to ensure adequate power for final
analysis
Favorable zonePromising ZoneUnfavorable Zone
0 CPmin CPmax 100 (%)
CP = Conditional power – the power of study calculated using observed HR at interim
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Operating Characteristics for Various Choices of CPmin
(original event number d=509 with N=3123 targeting for HR=0.75)

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Operating Characteristics for
CPmin=10% and CPmax= 90%
Assume true HR=0.80
CP = 10%
HR = 0.92
CP = 90%
HR = 0.77
3123
(d=509)
4651
(d=758)
45% in
Promising Zone
40% in
Favorable Zone
13% in
Unfavorable
Zone
Power 94%
Power
26%
CP/HR
CP=0%
HR=1.05
CP=100%
HR=0.67
Power 67%
5191
(d=846)
Power 90%
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Simulation by East Version 5.4.
New
sample size
(new # events)
Original
sample size
(# events)
Power 87%

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Choice of CPmin - Trade off between Area of Protection and
Conditional Power in “Promising Zone” After Adjustment
Assume true HR=0.80
CP = 10%
HR = 0.92
CP = 90%
HR = 0.77
3123
(d=509)
4651
(d=758)
45% in
Promising Zone
40% in
Favorable Zone13%
CP/HR
CP=0%
HR=1.05
CP=100%
HR=0.67
Power 87%
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Simulation by East Version 5.4.
New
sample size
(# events)
Original
sample size
(# events)
24%
Unfavorable
Zone
34% in Promising
Zone
CP = 30%
HR = 0.92
Power 90%
Power 35%
(26%)
Power 94%

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Choice of CPmin - Trade off between Area of Protection and
Conditional Power in “Promising Zone” (cont’d)
Assume true HR=0.80
CP = 90%
HR = 0.77
3123
(d=509)
4651
(d=758)
58% in
Promising Zone
40% in
Favorable Zone
CP/HR
CP=0%
HR=1.05
CP=100%
HR=0.67
Power 78%
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Simulation by East Version 5.4.
New
sample size
(# events)
Original
sample size
(# events)
Power 94%Power 57%

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Choice of CPmax - Trade off between Incremental Investment
and Conditional Power at Final Analysis
Assume true HR=0.80
CP = 10%
HR = 0.92
CP = 90%
HR = 0.77
3123
(d=509)
4651
(d=758)
45% in
Promising Zone
40% in
Favorable Zone
13% in
Unfavorable
Zone
Power 92%
(94%)
Power
26%
CP/HR
CP=0%
HR=1.05
CP=100%
HR=0.67
Power 87%
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Simulation by East Version 5.4.
New
sample size
(new # events)
Original
sample size
(# events)
CP=80%
HR=0.80
49% in
Favorable Zone
36% in
Promising Zone
Power 84%

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Simulations for Sensitivity of the Design (original event
number d=509, CPmin=0.10, CPmax=0.90, new event number dnew=758)

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Comments
• Design operating characteristics are sensitive to the definition of “Promising
zone”.
• Simulations are essential to identify the optimal choice of boundaries so that
design can
- allow for up-adjust sample size when treatment effect is promising;
- ensure adequate conditional power at end to detect the effect;
- not spend unnecessary extra sample size.
• The randomness of effect size and noise at interim could lead to a decision of
increasing or not increasing sample size by mistake.
• To avoid making wrong decision, the DMC needs to examine the totality of
interim data.

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Adaptive SSR Design vs. Group Sequential Design
(GSD) from Sample Size Perspective
Sample size
5191
Average sample size using GS design:
N=5191*0.74 + 4776*0.26=5083
Probability of stopping for success if interim HR=0.80
is 26%; sample size due to early stopping = 4776
Average SS using adaptive SSR (up-adjust
to have near 90% power for HR=0.80
N=4062
>
GS design: sample size is determined to detect HR=0.80 with 90% power;
perform an interim at 50% of the events and allow stopping for success; O’brien-
Fleming alpha-spending function is applied.
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End of Study

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Comparisons Between Adaptive SSR and GSD
Conditioning on True Hazard Ratio

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Methods for Final Analysis
• Final nominal level will be adjusted using O'Brien-Fleming method regardless
of whether the sample size is increased
• If no sample size adjustment, the final statistical inference will be made
based on the standard statistic Z, calculated using all the events collected in
the study
• If there is a sample size increase, CHW method (Cui, Hung, and Wang,
1999) will be used to calculate the combined statistic at final analysis
• E.g., if 50% of the events are used for interim analysis, final test statistic will be
where Z1 and Z2 are two score tests calculated using events observed before and after the
interim, respectively.
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21 2
1
2
1 ZZ

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Concluding Remarks
• For indications where confirmatory trial endpoint were not measured
at learning phase, great uncertainty is associated with the outcome of
confirmatory studies
• To mitigate costly failure at late stage, adaptive sample size re-
estimation can be a novel design choice along with futility stopping
• This approach allows the opportunity for the test drug to demonstrate
treatment effect using smaller sample size
• It also provides the opportunity to increase the probability of trial
success when the sample size is falling short while a promising
treatment effect is observed at interim
• Careful selection of “Promising Zone” is important for such design

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Acknowledgements
• Cyrus Mehta, Ph.D., Cytel Inc.
• Hui Tang, Ph.D., Abbott Laboratories

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References
1. Sheiner L.B., Learning versus confirming in clinical drug development,
Clinical Pharmacology and Therapeutics, 61:275-291, 1997.
2. Roland E. Schmieder, et at., Changes in albuminuria predict mortality and
morbidity in patients with vascular disease, J Am Soc Nephrol 22: 1353–
1364, 2011
3. Lambers and De Zeeuw, Dual RAS therapy not on target, but fully alive,
Clin. Prac. 2010
4. Brenner et at., Effects of losartan on renal and cardiovascular outcomes in
patients with Type 2 diabetes and nephropathy, Nephron. Clin. Prac. 2010
5. Mehta and Pocock, Adaptive increase in sample size when interim results are
promising: A practical guide with examples, Statistics in Medicine 2011
6. Lu Chi, H. M. James Hung, and Sue-Jane Wang, Modification of Sample
Size in Group Sequential Clinical Trials, Biometrics 5, 853-857, September
1999

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Financial Information Disclosure
Yili L. Pritchett is an employee of Abbott Laboratories. The
research presented was financially supported by Abbott
Laboratories.