Statistical Challenges in Immuno-Oncology · 2013. 11. 26. · MOA: Cytotoxic vs. Cytostatic Agents...
Transcript of Statistical Challenges in Immuno-Oncology · 2013. 11. 26. · MOA: Cytotoxic vs. Cytostatic Agents...
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Statistical Challenges in Immuno-Oncology
EFSPI, Brussels, Belgium
November 7, 2013
Veerle De Pril, MSc, Associate Director, Oncology Biostatistics, BMS
Tai-Tsang Chen, PhD, Director, Oncology Biostatistics, BMS
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Outline
Cytotoxic vs. cytostatic agents
Mechanism of action
Endpoints
Immunotherapies
Important issues to consider in study design and analysis
Efficacy
– Overall Survival
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MOA: Cytotoxic vs. Cytostatic Agents
Cytotoxic agents
Dose-dependent rapid cell kill or tumor shrinkage
Lack of selectivity leads to undesired toxicity or side effects
Cytostatic agents
Inhibit or suppress cellular growth or division which leads to delayed progression
Minimal or less severe toxicity, prolonged duration of treatment at lower dose
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Endpoints: Cytotoxic vs. Cytostatic
Cytotoxic
OS: Clinical benefit
BOR (WHO or RECIST): Direct cell kill action leads to tumor shrinkage
Cytostatic
OS: Clinical benefit
PFS/TTP: Stop or delay tumor growth
BOR: Some may shrink tumor
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Immunotherapies
Stimulate the patient’s own immune system to fight cancer
Immune cell activation; change in tumor burden
Toxicity or side effects caused by the modulation of immune activity
Endpoints remain similar
OS: clinical benefit
BOR: tumor shrinkage
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Important Issues in Design and Analysis in Immuno-Oncology
Sample size determination
Expected number of events
Timing of analysis
Efficacy analysis
Interim analysis strategy
Additional analysis considerations
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Typical Survival Curve – Advanced Breast Cancer
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Ipilimumab (Yervoy) in Metastatic Melanoma
G R O U P # D E A T H S / # R A N D O M IZ E D M E D IA N (95% C I)
IP I+ D T IC 198 /250 11 .17 (9 .40 - 13 .60 )D T IC 226 /252 9 .07 (7 .75 - 10 .51 )
S U B JE C T S A T R IS KIP I+ D T IC 2 50 1 82 1 15 8 6 6 9 5 7 5 0 4 6 4 4 1 4 1 0D T IC 2 52 1 60 9 0 6 4 4 4 3 7 3 0 2 6 2 2 8 1 0
IP I+ D T ICC E N S O R E D
D T ICC E N S O R E D
/w w b dm /c lin /p ro j/ca /184 /024 /va l/s ta ts /C S R /ta i/dm c/dm cs .sas R U N D A T E : 18 -Jan -2013 9 :11
C A 184-024 F irs t L ine M e lanom a S tudyD M C a n lays is based on da tabase
PR
OP
OR
TIO
N A
LIV
E
0 .0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1 .0
M on ths
0 6 1 2 1 8 2 4 3 0 3 6 4 2 4 8 5 4 6 0 6 6
Yervoy + DTIC
DTIC alone
1 2 3 4 Years
Yervoy + gp100
Yervoy alone
gp100 alone
* Hodi, S. et al. NEJM , 363 (8): 2010
Months * Maio et al. presented at 37th ESMO, 2012
Previously Treated
Previously Untreated
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Interferon alfa-2b (Intron-A) – Adjuvant Melanoma
Kirkwood et al., 2004, Clinical Cancer Research
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Pegylated Interferon alfa-2b (Sylatron): Relapse-Free Survival – Adjuvant Melanoma
Eggermont, AMM, et al., 2012, Journal of Clinical Oncology
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Study Design and Sample Size Determination
Standard study design
Assumes exponential distribution
Unconventional study design
Long-term survival (or “cure rate” or “functional cure”)
Delayed clinical effect
Does unconventional study design impact sample size / power calculation?
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Exponential OS Study Design
* Proportional hazards model (exponential)
___ CONTROL ___ EXPERIMENTAL
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Long-Term (LT) Survival
* Proportional hazards cure model
___ CONTROL ___ EXPERIMENTAL
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Delayed Clinical Effect
* Non-proportional hazards model
___ CONTROL ___ EXPERIMENTAL
HR1 HR2
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Delayed Clinical Effect with Long-Term Survival
* Non-proportional hazards cure model
___ CONTROL ___ EXPERIMENTAL
HR1 HR2
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Example of a Standard Study Design
Consider the following standard study design
Exponential distribution
Median OS: 12 vs. 16 months (HR=0.75)
Power: 90%
Two-sided type I error rate: 5%
Accrual rate: 20 pts/month
No interim analysis
Required number of events: 512 events
Sample size: 680 subjects
Accrual duration: 34 months
Study duration: 48 months
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Impact of LT Survival and Delayed Clinical Effect on Study Duration and Power
Standard
(exponential)
LT survival --
Delayed effect --
Sample size 680
# events 512
Hazard Ratio 0.75
Power 0.90
Study duration 48
* Based on 10000 simulations
LT Survival
0.10/0.18
--
680
512
0.75
0.90
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Delay
--
3 m
680
512
1/0.75
0.70
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LT Survival /
Delay
0.10/0.17
3 m
680
512
1/0.75
0.70
54
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Impact of LT Survival and Delayed Clinical Effect on Study Duration and Power
Long-term survival
Results in prolonged study duration
Higher LT survival results in longer study duration
Delayed clinical effect
Reduces statistical power
Longer delay results in more power loss
Expected number of events
Can the number of events be achieved?
Follow-up duration
Is the study designed to allow sufficient follow-up for all patients?
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Interim Analysis Strategy
Necessity of interim analysis
Interim analysis vs. final analysis only
Timing of interim analyses
Early vs. late interim analysis
Type of interim analysis
Superiority vs. futility
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Probabilities for Stopping at Interim Analysis
Standard
(exponential)
Interim sample size 520
# events 256
PETa (superiority) 0.25
PETa (futility) 0.01
* Based on 10000 simulations
PETa = Probability of Early Termination when agent is active
Using O’Brien-Fleming boundaries
LT Survival
540
256
0.25
0.01
Delay
480
256
0.06
0.08
LT Survival /
Delay
500
256
0.06
0.08
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Interim Analysis Strategy - Conclusion
Delayed clinical effect and LT survival
Careful consideration warranted:
– Necessity of interim analysis
– Timing of interim analysis
– Type of interim analysis
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Additional Analysis Considerations
Prediction of timing of analyses
Does the long-term survival alter projected study duration?
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Additional Analysis Considerations Summary Measures
G R O U P # D E A T H S / # R A N D O M IZ E D M E D IA N (95% C I)
IP I+ D T IC 198 /250 11 .17 (9 .40 - 13 .60 )D T IC 226 /252 9 .0 7 (7 .75 - 10 .51 )
S U B JE C T S A T R IS KIP I+ D T IC 2 50 1 82 1 15 8 6 6 9 5 7 5 0 4 6 4 4 1 4 1 0D T IC 2 52 1 60 9 0 6 4 4 4 3 7 3 0 2 6 2 2 8 1 0
IP I+ D T ICC E N S O R E D
D T ICC E N S O R E D
/w w bdm /c lin /p ro j/ca /184 /024 /va l/s ta ts /C S R /ta i/dm c/dm cs .sas R U N D A T E : 18 -Jan -2013 9 :11
C A 184-024 F irs t L ine M e lanom a S tudyD M C an lays is based on da tabase
PR
OP
OR
TIO
N A
LIV
E
0 .0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1 .0
M o n ths
0 6 1 2 1 8 2 4 3 0 3 6 4 2 4 8 5 4 6 0 6 6
Median
Hazard Ratio
Mean (Area Under the Curve)
Yearly OS rates
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Statistical Analysis Considerations
Primary analysis
Remains log-rank test and Cox model?
Long-term survival
Regulatory: Median vs. OS rates
Market access: Mean
Cure rate models
Delayed clinical effect
Fleming-Harrington weighted log-rank test
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Summary
Understand disease characteristics and MOA of therapy
– Delayed clinical effect
– Long-term survival
Implications on study design and analyses
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Reference
Statistical issues and challenges in immuno-oncology, Tai-Tsang Chen, Journal for ImmunoTherapy of Cancer 2013, 1:18