Adaptive Designs that
Prospectively Learn vs. Test
Biomarker Sensitive Patients
Sue-Jane Wang, Ph.D.
Associate Director
Adaptive Design and Pharmacogenomics
Office of Biostatistics, Office of Translational Sciences
Center for Drug Evaluation and Research, U.S. FDA
Presented at “Graybill Conference VII”, Fort Collin, Colorado, June 12, 2008
Acknowledgments
H.M. James Hung
Robert T. O’Neill
Thanks are due to Dr. Robert Temple and Dr. Norman
Stockbridge of FDA for bringing the interesting problem to our
attention
The research work was supported by the RSR funds #02-06,
#04-06, #05-2, #05-14, #08-48 awarded by the Center for
Drug Evaluation and Research, U.S. Food and Drug
Administration
*The research view presented are those of the author’s
professional views and not necessarily those of the US FDA
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Outline
 (Genomic) Biomarker as a Classifier
 AD in Preliminary Biomarker Exploratory Studies
 AD in A&WC Setting
 Examples
 Mechanics of Sample Size Formula
 Concluding Remarks
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Biomarker
• A characteristic recognized as an indicator
• Regulatory impact
– Single Biomarker
– Composite Biomarker
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A Genomic Composite Biomarker*
(genomic classifier)
• Consists of a set of gene expressions or SNPs
• Defined by a prediction algorithm
• Used to classify patients as likely responsive
patients (efficacy or safety)
GCB = 1
=0
if patient’s risk score beyond threshold
otherwise
* Wang SJ (2007, Pharmaceutical Statistics)
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Genomic Composite Biomarker
Developed from
Microarray,
Whole Genome Scan,
Other Technology Platforms
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Tumor Staging Might not Predict Cancer Risk Level
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GCB - Added Value to Clinical ?
Typical Prognostic Factor
90%
80%
GCB, like baseline
clinical covariate,
might be associated
with placebo alone,
drug treatment alone,
or interacting with
disease & therapy
simultaneously
70%
70%
% responders
60%
50%
50%
40%
30%
20%
10%
0%
overall
Placebo
Experimental Treatment
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Exploration from Prospective AD Trial
early endpoint
based on early endpoint
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Prospective Learning of Patient Population
ACR: A Clinical Response
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ex: Baseline DAS4 (Fransen, 2005) (range 0-10)
DAS4 = 0.53938*(Ritchie) +
0.06465*(swollen joints) +
0.330* ln (ESR) +
0.00722* (General Health)
Ritchie: Ritchie articular index
Swollen joints: 44 swollen joint count
ESR: erythrocyte sedimentation rate
GH: 100 mm VAS
DAS ≤ 2.4 (LDA)
2.4 < DAS ≤ 3.7 (MDA)
DAS > 3.7 (HDA)
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DAS28 ≤ 3.2
DAS28 > 3.2
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Adaptive Designs in Adequate
and Well-Controlled Setting
 When a (composite) genomic biomarker is
developed (not a preliminary biomarker panel that
is continually refined), preliminary utility of
biomarker as a classifier needs analytic validation
and feasibility study
 To prospectively assess the biomarker’s clinical
utility, adaptive design in adequate and wellcontrolled setting may be considered
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Prognostic Biomarker
* Wang SJ (2007, Pharmaceutical Statistics)
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Predictive Biomarker
* Wang SJ (2007, Pharmaceutical Statistics)
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Prognostic-Predictive
Biomarker
* Wang SJ (2007, Pharmaceutical Statistics)
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Prospective Testing of Biomarker
Sensitive Patient Subset
A Study Adequate to Support
Effectiveness Claims Should
Reflect a Clear Prior Hypothesis
Documented In The Protocol
*FDA Guidance on “providing clinical evidence of effectiveness
for human drug and biological products” for Industry, 1998
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Prospective Testing of Biomarker
Sensitive Patient Subset
Strategy #1 (e.g., Freidlin, Simon 2005)
(1) Learn potential GCB+ responsive patients in stage 1
(2) Test T-effect in all comers from both stages at
0.02 level, allow test for GCB+ subset at 0.005 level
using only stage 2 GCB+ patients, if all comers failed
Strategy #2 (e.g., Wang, O’Neill, Hung, 2007)
(1) GCB+ is defined and not learned from current trial
(2) stage 1, assess if T futile or toxic in GCB- for accrual decision
(3) Test T-effect in all comers and in GCB+ subset from both
stages using, e.g., p-value combination, adaptive Hochberg
with strong control at 0.05 level
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Adaptive: Split-a, Hochberg, FS
Figure 4. Power Comparison for Dg+ Under Adaptive Design
(Dg+ = 0.4, Dg- = 0)
1
subset power (1 - bg+)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
f (sample size ratio)
AD 0.0125
AD 0.005
FS 0.0125
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FS 0.005
Hochberg
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Is RF a GCB classifier for treatment?
Primary Endpoint
PBO
Treatment
p-value
Ph2
n
RF+ only
40
38%
40
73%
< 0.005
n
RF+ (74%)
RF– (26%)
ITT
131
28%
53%
31%
176
54%
47%
51%
< 0.0001 (1O)
ns
<0.001
n
RF+ (79%)
RF– (21%)
ITT
201
19%
12%
18%
298
54%
41%
51%
< 0.0001
Ph3
Ph3
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Nested Subsets: Two Markers
I1 , I 2
Consider 2 indicators:
Subgroups formed: G0, G1, G2
G0: all patients randomized (ITT)
G1: patients w/ biomarker I1 present
G2: patients w/ biomarkers present in
I1  I 2
D0 *
D1 *
D2 *
Prevalence: f1 | G0, f2 | G1
Prevalence relative to originally intended patient population
f0=1,
f1’=f1 for G1,
f2’ = f1*f2 for G2
GB  GB-1  …  G2  G1  G0 (ITT)
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Rationale of Sensitive Patient Adaptation
At time t, based on interim data,

N or Nmax


Upper bound for CP & lower bound if futility or N
t
Compute CPGj Zj | D j , Z jt or CPGj Zj | D j , Z jt  Remaining
(1-t)N or (1-t)N+(Nmax-N) recruits only the
selected jth patient subset

Pre-specified weighting in weighted z-statistic


Let selection rule denoted by
Dt  f Z ot , Z1t , , Z Bt 
Dt  0, 1, 2, , B
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Empirical Power Comparison – Some Pattern
Figure 2c. Empirical Powers Among 8 Strategies
(Some Pattern) (f =.5, .5)
Individual Powers
R
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
*
*
A
B
C
D
E
F
G
T=0.495
*
*
* *
g0
D 0=.113
g1
g2
D 1 =.125
D 2=.300
Prospectively Specified Patient (Sub)sets
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Mechanics of Sample Size Formula
Sample size planning based on d, s, a, b  n/arm
n formula – does not distinguish types of patients
Adaptive Design – In/Exclusion ITT Patients
Br-CA
iid (n1)
CHF
iid (n2)
EOS I
EOS II
PainFree approved for (i) back pain, (ii) Nerve Pain
Chronic Pain
Interim Enrichment
Non-nested subset: Back pain or Nerve Pain
Nested subset: Back pain & Nerve Pain
Randomization stratify on Back pain, Nerve Pain
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Concluding Remarks
 Exploratory biomarker development - flexible AD design
 For A&WC trials, recommend stratified randomization
based on biomarker status to avoid bias
 Biomarker status for ITT patients should be available
 Two-stage adaptive design in A&WC setting provides
flexibility for assessing sensitive patients prospectively
and effectively
 Improvement from conventional null, sample size caveats
 Replication of the finding needed
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Some References
Cui, Hung, Wang. (1999, Biometrics)
Wang, Chen. (2004, Journal of Computational Biology)
Wang. (2005, Flexible Design Genomic Drug Trial, NCI-FDA Biomarker Wksp)
Wang. (2005, Special report in 1st Multi-track DIA WKSP, Japan)
Tsai, Wang, Chen, Chen. (2005, Bioinformatics)
Simon, Wang (2006, The Pharmacogenomics Journal (TPJ))
Trepicchio, Essayan, Hall, Schechter, Tezak, Wang, et al. (2006, TPJ)
Wang, Cohen, Katz, et al. (2006, TPJ)
Chen, Wang, Tsai, Lin (2006, TPJ)
Microarray Quality Control Project: (2006, Nature Biotechnology)
Wang. (2007, Taiwan Clinical Trials)
Wang, O’Neill, Hung. (2007, Pharmaceutical Statistics)
Wang. (2007, Pharmaceutical Statistics): Biomarker as a classifier in
pharmacugenomics clinical trials: a tribute to 30th anniversary of PSI
(Statistician in the Pharmaceutical Industry)
Wang et al. (2008, invited Biometrical J. in progress)
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