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Statistical Issues in Incorporating and Testing Biomarkers in Phase III Clinical Trials. FDA/Industry Workshop; September 29, 2006 Daniel Sargent, PhD Sumithra Mandrekar, PhD Division of Biostatistics, Mayo Clinic L Collette, EORTC. What are we testing.

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Statistical Issues in Incorporating and Testing Biomarkers in Phase III Clinical Trials

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Statistical issues in incorporating and testing biomarkers in phase iii clinical trials l.jpg

Statistical Issues in Incorporating and Testing Biomarkers in Phase III Clinical Trials

FDA/Industry Workshop; September 29, 2006

Daniel Sargent, PhD

Sumithra Mandrekar, PhD

Division of Biostatistics, Mayo Clinic

L Collette, EORTC


What are we testing l.jpg

What are we testing

  • A (novel) therapeutic whose efficacy is predicted by a marker?

  • A marker proposed to predict the efficacy of an (existing) therapeutic?


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Preliminary information

Methods & feasibility of

measurement of the marker

in the target population

Specificity to the cancer of interest

Cut point for classification

Prevalence of marker expression

in the target population

Properties as a prognostic marker

(in absence of treatment or

With non targeted std agent)

Expected marker predictive effect

Endpoint of interest


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Phase II/III Trials

Patient Selection for targeted therapies

  • Test the recommended dose on patients who are most likely to respond based on their molecular expression levels

  • May result in a large savings of patients (Simon & Maitournam, CCR 2004)


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Trials in targeted populations

  • Gains in efficiency depend on marker prevalence and relative efficacy in marker + and marker - patients

(Simon & Maitournam,

CCR 2004)


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Phase II/III Trials

Designs for Targeted Trials

May use standard approaches.

Possible Issues

  • Could lead to negative trials when the agent could have possible “clinical benefit”, since precise mechanism of action is unknown

  • Could miss efficacy in other patients

  • Inability to test association of the biologic endpoints with clinical outcomes in a Phase II setting


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Targeted Trials

Additional considerations

  • Not always obvious as to who is likely to respond - often identified only after testing on all patients

  • Slower accrual, and need to screen all patients anyway

  • Need real time method for assessing patients who are / are not likely to respond


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Example: C-225 in colon cancer

  • Early trials mandated EGRF expression

    • (Saltz, JCO 2004, Cunningham, NEJM 2004)

  • Response rate did not correlate with expression level (Cunningham, NEJM 2004)

    • Faint: RR 21%

    • Weak or Moderate: RR 25%

    • Strong: RR 23%

  • Case series demonstrates no correlation between expression and response

    • (Chung, JCO 2005)

  • Currently indicated only in patients with EGFR expressing tumors, but most current studies do not require EGFR expression


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Design of Tumor Marker Studies

  • Current staging and risk-stratification methods incompletely predict prognosis or treatment efficacy

  • New therapeutic options emerging

  • Optimizing and individualizing therapy is becoming increasingly desirable

  • Very few potential biological markers are developed to the point of allowing reliable use in clinical practice


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Prognostic Marker

Single trait or signature of traits that separates different populations with respect to the risk of an outcome of interest in absence of treatment or despite non targeted “standard” treatment

Prognostic

No treatment or

Standard, non targeted treatment

Marker +

Marker –


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Predictive Marker

Single trait or signature of traits that separates different populations with respect to the outcome of interest in response to a particular (targeted) treatment

Predictive

No treatment

or Standard

Targeted

Treatment

Marker +

Marker –


Validation l.jpg

Validation

Prognostic marker

Series of patients

with standard treatment

Designs?

Predictive Markers

Randomized

Clinical Trials


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Randomized Trials

  • Trials to assess clinical usefulness of predictive markers – i.e., does use of the marker result in a clinical benefit of a therapy

    • Upfront stratification for the marker status before randomization

    • Randomize and use a marker-based strategy to compare outcome between marker-based arm with non-marker based arm

      Sargent et al, JCO 2005


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Design I: upfront Stratification

Treatment A

Marker Level (-)

Randomize

Treatment B

Register

Test Marker

Treatment A

Marker Level (+)

Randomize

Power trial

separately within

marker groups

Treatment B

Sargent et al., JCO 2005


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Approach I: Separate Tests

Treatment A (Std)

Statistical test

With power

R

Marker -

Treatment B (New)

Test marker

Treatment A (Std)

Statistical test

With power

R

Marker +

Treatment B (New)


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Approach II: Interaction

Treatment A (Std)

Statistical test

With power

R

Marker -

Treatment B (New)

Test marker

Treatment A (Std)

R

Marker +

Treatment B (New)


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Marker-based strategy design

M -

Marker-

Based

Strategy

Statistical

Test with

Power

Treatment A

M +

Treatment B

Test

marker

R

Non

Marker

Based

Strategy

Treatment A


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Design II: Marker Based Strategy

Marker Level (-)

Treatment A

Marker Based Strategy

Marker Level (+)

Treatment B

Register

Randomize

Test Marker

Treatment A

Non Marker Based Strategy

Randomize

Treatment B

Sargent et al., JCO 2005


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Sample Size Interaction Design

HR: 1.25

844 †

1220 †

HR: 0.69

HR: 0.86

1705 †

2223†

2756†


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Sample size: Strategy Design

TS -

Marker-

Based

Strategy

IFL (20 mo)

16.5 mo

TS +

IO (14 mo)

HR

0.91

4629

R

Non

Marker

Based

Strategy

IFL (15 mo)

R

15 mo

IO (15 mo)


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Discussion

  • Sample Size

    • Typically large, especially if the marker effect size is modest

    • Depends on many factors such as

      • The marker prevalence in the target population

      • The baseline risk in the unselected population receiving standard treatment

      • The expected treatment difference in all marker groups


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Conclusions

  • The Marker Based Strategy design is preferable whenever more than one treatment are involved or when the treatment choice is based on a panel of markers

  • That design generally requires more patients than the Interaction design

    • The marker is also prognostic

    • Dilution (marker + patients receive the targeted therapy in the randomized non marker based group)


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Conclusions

  • In the case of a single marker and two treatments, Interaction Design preferable

  • Separate Tests versus Interaction ?

    • Depends on strength of evidence needed for the marker effect and sample size

  • Whenever the interaction HR is larger than any of the treatment HRs (generally qualitative interaction) the interaction approach demands less patients

  • A partial Separate Tests approach may be useful whenever no treatment difference is expected in one of the marker groups


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