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. FDA/Industry Workshop; September 29, 2006 Daniel Sargent, PhD Sumithra Mandrekar, PhD Division of Biostatistics, Mayo Clinic L Collette, EORTC. What are we testing. - PowerPoint PPT PresentationTRANSCRIPT
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Statistical Issues in Incorporating and Testing
Biomarkers in Phase III Clinical Trials
FDA/Industry Workshop; September 29, 2006
Daniel Sargent, PhDSumithra Mandrekar, PhD
Division of Biostatistics, Mayo ClinicL Collette, EORTC
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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 ofmeasurement of the marker
in the target populationSpecificity to the cancer of interest
Cut point for classificationPrevalence of marker expression
in the target populationProperties as a prognostic marker
(in absence of treatment orWith 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
Prevalence Relative Efficacy
Efficiency Gain
25% 0% 16x
25% 50% 2.5x50% 0% 4x50% 50% 1.8x75% 0% 1.8x75% 50% 1.3x
(Simon & Maitournam, CCR 2004)
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Phase II/III TrialsDesigns 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 TrialsAdditional 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 MarkerSingle 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
PrognosticNo 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
PredictiveNo treatment or Standard
Marker +
Marker –
Targeted Treatment
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Prognostic marker Series of patients with standard treatment
Predictive Markers Randomized Clinical Trials
Validation
Designs?
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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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Register Test Marker
Marker Level (-)
Randomize
Treatment A
Marker Level (+)
Treatment B
Sargent et al., JCO 2005
Design I: upfront Stratification
Randomize
Treatment A
Treatment B
Power trial separately withinmarker groups
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Approach I: Separate Tests
Marker -
Marker +
R
R
Test marker
Treatment A (Std)
Treatment B (New)
Treatment A (Std)
Treatment B (New)
Statistical testWith power
Statistical testWith power
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Approach II: Interaction
Marker -
Marker +
R
R
Test marker
Treatment A (Std)
Treatment B (New)
Treatment A (Std)
Treatment B (New)
Statistical testWith power
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Marker-based strategy design
M -
M +
RTest marker
Treatment A
Treatment B
Marker-Based
Strategy
Non MarkerBased
Strategy
Treatment A
StatisticalTest with
Power
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Register
Marker Based Strategy
Non Marker Based Strategy
Randomize
Treatment A
Treatment B
Marker Level (-)
Treatment A
Marker Level (+) Treatment B
Test Marker
Sargent et al., JCO 2005
Design II: Marker Based Strategy
Randomize
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Median OS Irinotecan/Oxaliplatin
(IO)
Irinotecan/5-FU/L
TS low(50%) 16 months
20 months
TS high(50%) 14 months 12
months
HR: 1.25
Sample Size Interaction Design
HR: 0.86
844 †
1705 †
2223†2756†
HR: 0.691220 †
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Sample size: Strategy Design
TS -
TS +
IFL (20 mo)
IO (14 mo)
Marker-Based
Strategy
Non MarkerBased
Strategy
IFL (15 mo)
IO (15 mo)R 15 mo
16.5 mo
HR0.91R4629
†
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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