Sue-Jane Wang, Ph.D. Associate Director Adaptive Design and Pharmacogenomics Office of Biostatistics, Office of Translational Sciences Center for Drug

Sue-Jane Wang, Ph.D.

Associate Director Adaptive Design and Pharmacogenomics

Office of Biostatistics, Office of Translational SciencesCenter for Drug Evaluation and Research, U.S. FDA

Presented at “Graybill Conference VII”, Fort Collin, Colorado, June 12, 2008

Adaptive Designs that Prospectively Learn vs. Test Biomarker Sensitive Patients

Wang SJ, Graybill 06.12.2008 2

AcknowledgmentsH.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


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


Biomarker

• A characteristic recognized as an indicator• Regulatory impact

– Single Biomarker– Composite Biomarker


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 if patient’s risk score beyond threshold

= 0 otherwise

* Wang SJ (2007, Pharmaceutical Statistics)


Genomic Composite Biomarker

Developed from

Microarray,

Whole Genome Scan,

Other Technology Platforms


Tumor Staging Might not Predict Cancer Risk Level


GCB - Added Value to Clinical ?%

res

po

nd

ers

50%

70%

overall

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Placebo

Experimental Treatment

GCB, like baseline clinical covariate, might be associated with placebo alone, drug treatment alone, or interacting with disease & therapy simultaneously

Typical Prognostic Factor


Exploration from Prospective AD Trial

early endpoint

based on early endpoint


ACR: A Clinical Response

Prospective Learning of Patient Population


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 rateGH: 100 mm VAS

DAS ≤ 2.4 (LDA) DAS28 ≤ 3.2 2.4 < DAS ≤ 3.7 (MDA) DAS28 > 3.2 DAS > 3.7 (HDA)


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 well-controlled setting may be considered


Prognostic Biomarker



Predictive Biomarker



Prognostic-Predictive Biomarker



A Study Adequate to Support Effectiveness Claims Should

Reflect a Clear Prior HypothesisDocumented In The Protocol

*FDA Guidance on “providing clinical evidence of effectiveness for human drug and biological products” for Industry, 1998

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

Prospective Testing of Biomarker Sensitive Patient Subset


Adaptive: Split-, Hochberg, FS

Figure 4. Power Comparison for Dg+ Under Adaptive Design(Dg+ = 0.4, Dg- = 0)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6

f (sample size ratio)

sub

set

po

we

r (1

- b

g+

)

AD 0.0125 AD 0.005 FS 0.0125 FS 0.005 Hochberg


Is RF a GCB classifier for treatment?

Primary Endpoint PBO Treatment p-value

Ph2 n RF+ only

4038%

4073% < 0.005

Ph3 n RF+ (74%) RF– (26%) ITT

13128%53%31%

17654%47%51%

< 0.0001 (1O)ns<0.001

Ph3 n RF+ (79%) RF– (21%) ITT

20119%12%18%

29854%41%51% < 0.0001


Nested Subsets: Two Markers

Consider 2 indicators:

Subgroups formed: G0, G1, G2

G0: all patients randomized (ITT)

G1: patients w/ biomarker present

G2: patients w/ biomarkers present in

Prevalence: f1 | G0, f2 | G1

Prevalence relative to originally intended patient population

f0=1, f1’=f1 for G1, f2’ = f1*f2 for G2

*0D*1D*2D

GB GB-1 … G2 G1 G0 (ITT)

21, II

21 II 1I


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

Compute or 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 Bttott ZZZfD ,,1, BDt ,,2,1,0

jtjt

Gj ZZjCP ,| D jtjGj ZZjCP ,| D


Empirical Power Comparison – Some Pattern

Figure 2c. Empirical Powers Among 8 Strategies (Some Pattern) (f =.5, .5)

00.1

0.20.3

0.4

0.50.6

0.7

0.8

0.91

g0 g1 g2

Prospectively Specified Patient (Sub)sets

Ind

ivid

ual P

ow

ers

R A B C D E F G

D2=.300D1=.125D

0=.113

T=0.495

**

*

* *

*


Mechanics of Sample Size Formula Sample size planning based on , , , b

n/arm n formula – does not distinguish types of

patients

PainFree approved for (i) back pain, (ii) Nerve Pain

Adaptive Design – In/Exclusion ITT Patients

EOS I EOS II

Chronic Pain Interim Enrichment

Non-nested subset: Back pain or Nerve PainNested subset: Back pain & Nerve Pain

iid (n1) iid (n2)Br-CA CHF

Randomization stratify on Back pain, Nerve Pain


Concluding Remarks

Replication of the finding needed

Improvement from conventional null, sample size caveats

Exploratory biomarker development - flexible AD design

Two-stage adaptive design in A&WC setting provides flexibility for assessing sensitive patients prospectively and effectively

For A&WC trials, recommend stratified randomization based on biomarker status to avoid bias

Biomarker status for ITT patients should be available


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)

Some References

Documents

Sue-Jane Wang, Ph.D. Associate Director Adaptive Design and Pharmacogenomics Office of Biostatistics, Office of Translational Sciences Center for Drug