Transcript Evaluation of a Multi-regional Trial for Global Simultaneous Drug

Evaluation of a Multi-regional Trial for
Global Simultaneous Drug Development
Norisuke Kawai
Clinical Statistics, Pfizer Japan Inc.
Agenda
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Background
3-layer approach
Statistical approaches for exploring regional
heterogeneity of the treatment effect
Points to consider for partitioning the overall sample size
into each region
Summary
Background
An Example of Japan-CTD
Apixaban versus Warfarin in Patients with Atrial Fibrillation: Efficacy Results
Overall Results
Japanese Results
Apixaban
(N=9120)
Warfarin
(N=9081)
Apixaban
(N=161)
Warfarin
(N=175)
Primary endpoint:
Stroke or systemic embolism
212 (2.32)
265 (2.92)
3 (1.86)
6 (3.43)
Ischemic or uncertain type of stroke
162 (1.78)
175 (1.93)
3 (1.86)
6 (3.43)
Hemorrhagic stroke
40 (0.44)
78 (0.86)
0 (0)
2 (1.14)
Systemic embolism
15 (0.09)
17 (0.10)
0 (0)
0 (0)
Number of events (%)
Sample size: 1.8% (336/18201)
Number of events: 1.9% (9/477)
Reproduced from the PMDA review report for Apixaban
Bridging Strategy
Phase 3
Data
Extrapolation to
the new region
Phase 2
(Dose-Response)
Data
Typically, a bridging study is
a confirmatory study
(almost the same sample
size as the foreign P2 study).
Phase 2
(Dose-Response)
Data
Comparison
Foreign region(s)
New region
(e.g. Japan)
“Basic Principles on Global Clinical Trials”
by MHLW in 2007
Question 6
 When conducting an exploratory trial like a dose-finding
study or a confirmatory trial as a global clinical trial, how
is it appropriate to determine a sample size and a
proportion of Japanese subjects?
Answers
 … A global trial should be designed so that consistency
can be obtained between results from the entire
population and the Japanese population, and by ensuring
consistency of each region, it could be possible to
appropriately extrapolate the result of full population to
each region….
A Case of a MRCT
MRCT
Overall Study Population
Total Sample Size:18201
(Number of Events: 477)
Japan portion
Sample Size: 336
(Number of Events:9 )
Comparison
(too much focused?)
Of course, we have no sufficient sample size to conduct
subgroup analyses within Japan portion.
How should we look at data from a MRCT?
compare
Overall results
Results from
“our nation
(ex. Japan)”
Results from
rest of the world
“Japan vs.” mentality?
Such a comparison may be reasonable
in the context of the “Bridging Strategy”
Should we focus on this so much in the new era of MRCTs ?
Objectives of MRCTs
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Primary objective
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Confirm efficacy and safety of the study drug in the overall
study population
A key secondary objective
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Evaluate influential ethnic factors on efficacy and safety of the
study drug, which includes investigating whether there is
regional heterogeneity
Investigation of heterogeneity
Homogeneous (consistent)
Heterogeneous (inconsistent)
A Framework to Evaluate Data from a MRCT
3-layer Approach
Layer 1
Overall results (efficacy, safety)
Findings from the
other studies
Knowledge of the
other drugs
in the same drug
class, etc.
Layer 2
Layer 3
Benefit:Risk
Assessment
Benefit:Risk
Assessment
Benefit:Risk
Assessment
For
Japan
For
Region A
For
Region B
Layer 2 and Layer 3 have
no prespecified hypothesis,
and insufficient power to
detect any inconsistency.
So, it is important to integrate
any available information.
3-layer Approach
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In Layer-1, we look at the overall results of
efficacy and safety.
In Layer-2, we conduct comprehensive and rigorous
analyses to explore influential factors on
efficacy or safety.
DO NOT jump
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Is there inconsistency in efficacy or safety in a particular
subgroup?
Is regional heterogeneity observed? etc.
In Layer-3, given the results from Layer-1 and Layer2, we consider Benefit:Risk for each region.
×
In Layer-2
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How do we explore regional heterogeneity of the
treatment effect?
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Graphical presentations
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Forest plot, funnel plot, etc.
Modeling approaches
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If we find a regional difference by looking at graphical
presentations, modeling approaches are useful to investigate
how much of the difference can be explained by covariates.
A Case Example of Forest Plot
We can visually look at inconsistency of the treatment effect
across regions. Japan is regarded as one region in Layer 2.
Change from Baseline in FEV1 for COPD patients
PMDA review report for Umeclidinium/vilanterol
A Case Example of Funnel Plot (1)
Outlier: -27 in Placebo
Malaysia
Taiwan
Philippine
China
Japan
Indonesia
Treatment better
Treatment difference
of CFB in YMRS
in bipolar disorder patients
Produced from the PMDA review report for Aripiprazole
(the treatment effects and SEs were derived from summary statistics)
A Case Example of Funnel Plot (2)
The PLATO trial: a MRCT that
compared ticagrelor and
clopidogrel for the prevention of
cardiovascular events in 18,624
patients admitted to the hospital
with an acute coronary
syndrome, with or without STsegment elevation
Treatment
better
FDA Briefing Information, BRILINTA™
(ticagrelor), for the July 28, 2010 Meeting
of the Cardiovascular and Renal Drugs
Advisory Committee
If we find regional heterogeneity
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A next question is “Are the observed regional differences
Real or Not?”
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Possible answers (they are mixed in practice)
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Imbalance across regions in distributions of intrinsic ethnic
factors who impact on the treatment effect
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We could explain it by available data in the MRCT
Extrinsic ethnic factors impact on the treatment effect
Play of chance
Others (outliers, treatment compliance, dropout rates, etc.)
Modeling Approaches
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If we find certain regions look “different” from others, as a next
step, we need to examine what may have caused the difference.
We can use statistical models to examine the difference.
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How much of the difference can be explained by covariates?
Are there observations that cannot be explained, such as outliers?
Imbalance across
regions in
distributions of
factors who impact
on the treatment
effect
Idealized
Schema
Outcome Variable
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Treatment effect
in Region A
Treatment effect in
Region B
Baseline distribution of
Region A
0.4
0.6
Baseline distribution
of Region B
0.8
BMD
Baseline
Variable
1.0
Placebo
Study drug
1.2
How much of the difference can be explained
by covariates?
Systematic residual errors
in a specific region still exist?
Actual
values
Line of “Predicted” = “Actual”
Residuals
= unexplained by the model
Predicted values by the model
e.g., Predicted value = f (treatment group, baseline value, body
weight)
Covariates
(influential factors)
An Illustrative Example
Line of “Predicted” = “Actual”
Predicted vs. Actual
Residual plots by regions
An Illustrative Example
Examination of residuals (gap between observed data and model)
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Points to consider for partitioning the overall
sample size into each region
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Consistency perspective: Minimize the chance for
observing apparent differences across regions when the
treatment effect is truly uniform across the regions.
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e.g. Method 1 or Method 2 in “Basic Principles on Global
Clinical Trials” by MHLW
Another perspective: Be able to evaluate influential
factor(s) on important efficacy/safety endpoints,
considering distributions of known influential factor(s) in
each region
Relationship between change in PANSS total
score and body weight in schizophrenia trials
A total 12585 patients from 33 clinical trials
Active Drug
Placebo
Chen YF, et al. (2010). Trial design issues and treatment effect modeling in multiregional schizophrenia trials. Pharm Stat. 9(3): 217-29.
Points to consider for partitioning the overall
sample size into each region
Distributions of
known influential factors
Total Sample Size
Region A
Impact
Weight
Region B
Region C
Baseline value of the
primary endpoint
Treatmen
t Effect
Points to consider for partitioning the overall
sample size into each region
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Need to check distributions of known (or potentially) influential
factors by simulating various scenarios for partitioning the total
sample size into individual regions at the design stage
Available CT or RWD data
at the design stage
Weight
Japanese Data
US Data
Bootstrap
Sampling
Baseline value
A proposal from Chen et al. (2012)* may be also useful during the study execution period.
*Chen J et al. (2012). An adaptive strategy for assessing regional
consistency in multiregional clinical trials. Clin Trials. 9(3):330-9.
An Illustrative Example
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Target Patients: hypercholesterolemic patients
Primary endpoint: Change from baseline (CFB) of
LDL-C in 12W
Known influential factors on the primary
endpoint: Weight (and baseline LDL-C value)
Regions: US and Japan
Sample size: Total 100 (50/group)
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An Illustrative Example
Median Distribution of Weight
90 kg
Median of Weight
80 kg
70 kg
60 kg
0
25
100
Japanese Sample Size
Summary
Layer 1
Overall results (efficacy, safety)
Worldwide
Collaborative
Works!
Layer 2
Layer 3
Benefit:Risk
Assessment
Benefit:Risk
Assessment
Benefit:Risk
Assessment
For
Japan
For
Region A
For
Region B