1. No category

Sta$s$cal Designs For Master Protocols Mary W. Redman, PhD Lead Biosta+s+cian SWOG Lung Commi6ee/Lung-­‐MAP Master Protocol Goals • 
Improve screening
–  Screening large numbers of patients for multiple targets
–  Reduce screen failure rate
–  Provide a sufficient “hit rate” to engage patients &
physicians
•  Increase speed of drug evaluation and development:
–  Provide an infrastructure to open new sub-studies faster
–  Rapid drug/biomarker testing for detection of “large
effects”
–  Facilitate FDA approval of new drugs and bring safe &
effective drugs to patients faster
Confirmatory Master Protocol Design Tissue Submission
Biomarker Profiling*
Biomarker 1
Sub-study 1
Biomarker 2
Sub-study 2
1:1
Exp1
SoC1
Biomarker 3
Sub-study 3
1:1
Exp2
SoC2
…Biomarker n
Not Biomarker
1-n
…Sub-study n
Non-match Study
1:1
Exp3
SoC3
1:1
Expn
SoCn
1:1
NMT
SoC5
*Sub-studies assigned based on biomarker results, patients with multiple biomarkers
randomly assigned to sub-study.
Exp = Targeted therapy (TT) or TT combinations (TTC), Exp1-4 are different TT/TTC regimens
NMT = non-match study experimental therapy or combinations
SoC = docetaxel or erlotinib, SoC1-5 depends on biomarker and TT/TTC/NMT regimen
Confirmatory Design with Marker Priori8za8on: Focus 4 Confirmatory Master Protocol Design: Low Prevalence/High Benchmarks Tissue Submission
Biomarker Profiling*
Biomarker1
Sub-study 1
(Exp1 )
Biomarker 2
Sub-study 2
(Exp2 )
Biomarker 3
Sub-study 3
(Exp3 )
…Biomarker n
Sub-study n
(Expn )
Not Biomarker/
Assay
1-n
?
Common Design *Sub-study assignment could be randomized, prioritized, other? Depends on scientific
knowledge and objectives
Discover Master Protocol Design Tissue Submission
Biomarker Profiling*
Biomarker/
Assay 1
Biomarker/
Assay 2
Biomarker/
Assay 3
…Biomarker/
Assay n
Sub-study 1
(Exp1 )
Sub-study 2
(Exp2 )
Sub-study 3
(Exp3 )
Sub-study n
(Expn )
Design 1
Design 2
Design 3
Design n
Not Biomarker/
Assay
1-n
?
*Sub-study assignment could be randomized, prioritized, other? Depends on scientific
knowledge and objectives
Discovery Master Protocol Design Any of previous designs can be used to discover:
–  Best biomarker,
–  Best assay to assess a biomarker, or
–  Best drug within class to pursue
…in a future study
Choice underlying objectives determines statistical
design associated with trial design
Protocol and Logis8cal Considera8ons • 
• 
• 
• 
Disease setting
Confirmatory versus discovery goals
Common platform versus multiple assays
Sub-study assignment
–  Random, prioritization, patient/physician
choice
•  Inclusion/use of individual control arms
–  Marker prevalence and prognostic value
•  Single protocol with screening and sub-studies
included
•  Approvals process for adding new studies
Goals and Sta8s8cal Design •  Confirmatory Testing
–  Biomarker needs to be known
•  Target Large Effects
–  Scientifically motivated (want “home-runs”)
–  Results in smaller sample size (speed)
–  Could result in near misses
–  Facilitates studying low prevalence
biomarkers (we can go to 5% of Squamous)
•  Phase 2/3 design
–  No delay between phase 2 and 3 = speed
Study Design and Objec8ves Design:
Independently conducted and analyzed parallel Phase II/III studies
Primary Objectives within each sub-study:
Phase II Component:
1.  To evaluate if there is sufficient evidence to continue to the
Phase III component by comparing progression-free survival
(PFS) between patients randomized to experimental therapy
versus SoC.
Phase III Component:
1.  To determine if there is both a statistically and clinicallymeaningful difference in PFS between the treatment arms.
2.  To compare overall survival (OS) between treatment arms.
Study Design Within Each Sub-­‐study A
s
s
i
g
n
m
e
n
t
R
a
n
d
o
m
i
z
a
t
i
o
n
Phase II
Analysis
55 PFS
events
Phase III
Interim Analyses
OS for efficacy
PFS/OS for futility
Futility established
Stop
Complete
Accrual
Final Analysis
OS events
290 PFS events
12 months follow-up
Sta8s8cal Design: Phase II Interim Analysis Phase II Design Plan A
Primary Outcome
PFS
Sample Size
Target HR
(% improvement)
Plan B
55 progression events
HR = 0.5
2-fold increase
HR=0.4
2.5-fold increase
Power
90%
95%
Type I error
10%
4%
HR= 0.71
41% increase
HR = 0.61
63% increase
Approx. Threshold to continue:
HR
% improvement
Each sub-study can choose between Plan A or Plan B to determine “bar”
for continuation past Phase 2 interim analysis
Sta8s8cal Design: Phase III PFS and OS Co-primary PFS
OS
290
256
0.75*
(33% improvement)
1.0
(equivalence)
Alternative
Hypothesis
0.5
(2-fold increase)
0.67
(50% improvement)
Type I error
(1-sided)
0.014 against HR = 1.33
< 0.00001 against HR = 1
0.025
90%
90%
Events
Null Hypothesis (HR)
Power
* Non
HR = 1 null hypothesis encodes clinical significance
Sample size based on OS for all studies
Sample Size and Study Dura8on Prevalence Estimate Phase 21 Phase 32 500
/year
1,000
/year
Sample Size Decision Time
Sample Size
Estimate
Estimate
Study
Duration
5%
2.5%
76
36
282
147
10%
5%
92
22
295
83
15%
7.5%
107
17
307
61
20%
10%
119
14
318
50
30%
15%
146
12
337
39
40%
20%
169
10
349
33
50%
25%
189
9
360
29
Sample size to observe 55 progression events,5% ineligible, 2 months for
analysis and DSMB decision
2 Sample size and timing includes Phase 2 sample size/duration, final analysis
approx. 12 months after completion of accrual
1
Common Design with Flexibility S1400A • 
• 
• 
• 
Plan A Minimum 4 months FU for phase 2 analysis popula$on Minimum # PD-­‐L1 + pa$ents at analyses Phase 2 N = 170, Phase 2 N = 400 S1400B • 
• 
• 
• 
• 
Plan B Minimum 3 months FU for phase 2 analysis popula+on Study designed around PI3K GNE+ Enrolled based on FMI+ Phase 2 N = 78 (GNE+)/152, Phase 3 N = 288(GNE+)/400 S1400C •  Plan B •  Phase 2 N = 124, Phase 3 N = 312 S1400D •  Plan A •  Proposed amendment to cap # w/ FGFR by amplifica+on •  Phase 2 N = 112, Phase 3 N = 302 S1400E •  Plan A •  Evalua+on of c-­‐MET IHC in 1st 40 pa+ents •  Phase 2 N = 144, Phase 3 N = 344 Design Choice Considera8ons • 
Improve screening = single screening study protocol
• 
Improve development efficiency = single protocol
• 
Increase speed of drug evaluation and development
•  Comparison-based design
•  Target Large effects
•  Disease setting will determine the sub-study statistical
design (e.g. Lung-MAP vs ALCHEMIST vs. NCI-MATCH)
•  Uniformity and yet flexibility in design
•  Provide infrastructure for data generation for future
studies