2. Multiregional Trials Main features and issues raised Byron Jones
Novartis
PSI Conference, May 13, 2014

3. Multiregional Clinical Trials
Definition and Motivation
A single clinical trial that is conducted simultaneously in multiple geographical regions under a common protocol
Increasingly, clinical trials are run using patients from various regions worldwide.
More patients needed to demonstrate treatment advantages, as new treatments may have only incremental benefits vs existing therapies.
Local health authorities would like to see representation / evidence within their domains.
Varied settings may enhance confidence in observed effects.
Expanded markets interest trial sponsors.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

4. Multiregional Clinical Trials
Advantages
Advantages (Ando, Y. , ICSA/ISBS Conference, 2013)
Prevents unnecessary duplication of clinical trials
Makes drug development more efficient and cost-effective
Enables simultaneous global drug submission and approval
Gets effective and safe drugs to patients faster.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

5. Example: multiregional trial
679 study centres
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

6. Example of an MRCT
7,216 patients were enrolled from 5 geographical regions, 39 countries and 679 study centers.
3,581patients were randomized to the drug group and 3,635 to the placebo group.
Region
Number of countries N
Drug
(n)
Placebo
Treatment difference
Standard
error
P-value
Asia 5
441
214
227
-6.97
1.675
<.0001
Europe 20
3819
1889
1930
-5.43
0.531
Latin America 9
1229
630
599
-3.96
0.991
North America 2
1525
750
775
-4.93
0.829
Other 3
202 98
104
-3.18
2.258
0.16
Global 39
7216
3581
3635
-5.10
0.391
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

7. Regulatory Guidance ICH E5 and ICH E5 Q&A
A11. A multi-regional trial ...
The objectives of such a study would be: (1) to show that the drug is effective in the region and
(2) to compare the results of the study between the regions with the intent of establishing that the drug is not sensitive to ethnic factors.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

8. What is a region?
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

9. What is a region? Is it based on geography?
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

10. What is a region?
Not necessarily defined by location
“... region should not be limited to geographic boundaries but should take into consideration relevant intrinsic genetic and physiological or pathological factors as well as extrinsic factors such as medical practice” ICH E5.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

11. Inconsistency in the definition of regions
Review of 60 FDA Advisory Committee Meetings
90% of submissions were multiregional.
“Region was most often defined based on geography, and specifically continent ...”
“No trends or consistency was observed in how regions were defined within or across therapeutic areas nor any rationale for the definition of region ...”
“We propose that adequate justification of the definition should take into consideration factors such as race or ethnicity, disease epidemiology, medical practice, and geographic proximity, among others.”
Tanaka et al. (2011) [The PhRMA MRCT Key Issue Team]
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

12. PhRMA MRCT KIT Perspective on Region
“... regions should be predefined in the design stage and properly documented.”
“... Geography alone may not be adequate when defining regions Intrinsic and extrinsic factors should be considered.”
“ Country and site selection should be considered at the design stage as part of predefining regions...”
Analytical approach to defining regions (e.g., factor analysis, principal components).
The number of regions should not be large.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

13. Analysis models for multiregional clinical trials
Fixed or Random effects for regions/centres
Recall: Multicentre Trials
Fixed-effects Model [centre is a fixed factor]
The centers have been specifically chosen. Conclusions reached here only apply to the centers considered and can not be extended to other centers that are not in the trial
Random-effects Model [centre is a random factor]
The centers are a random sample from a large population of centers. Conclusions reached here can be extended to all the centers in the population
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

14. For MRCTs: Are regional estimates fixed or random?
Surely “region” is a fixed-effect – cannot think of a random sample of regions?
Possible model might assume centres are randomly nested within the levels of a fixed regional factor.
However, this are differing opinions in the literature.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

15. MTCT: fixed or random effects?
Random: Chen, Hung and Hsiao (2012)
Chen, Hung and Hsiao (2012) define a random effects model for the true treatment difference that applies to region i, i=1,2, ..., M.
They derive the global estimate of the treatment difference by applying well-known results for the random-effects estimator obtained from a meta-analysis, using the DeSimonian and Laird (1986) estimator of the between region variance.
Give sample size formula based on global estimate.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

16. Shrinkage estimates of regional treatment effects
Qui et al. (2013). Statistics in Medicine
Recommend :
fixed-effects model to estimate global effect and
estimates of individual region treatment differences using an empirical shrinkage estimator based on a random effects model
Individual region estimates borrow strength from other regions’ estimates.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

17. Consistency
Are individual region estimates similar to the global estimate?
It is (or should be) a basic premise of an MRCT that that there is no, or at most only a small amount of, regional variation
Regional variation can be reduced by good design and by inclusion of region-specific covariates in models for the response.
Should testing for such consistency be part of the analysis plan?
Sponsor more interested in global estimate
Regulator more interested in local estimate for their region
Ideally, global estimate of treatment difference is significantly different from zero and all regional estimates are significantly different from zero.
Sample size implications are different for the two situations.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

18. Two well-known examples of possible inconsistency
PLATO trial
MERIT trial
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

19. PLATO Trial Compare ticagrelor (novel) vs clopidogrel (standard)
PLATlet inhibition and patient Outcomes trial (Wallentin, et. al., 2009)
Compare ticagrelor (novel) vs clopidogrel (standard)
Patients with ACS (acute coronary syndomes)
Primary endpoint: CV death, MI, stroke
18624 patients, followed for a year.
Very strong evidence that ticagrelor is superior.
BUT...
Endpoint
ticagrelor
copidogrel HR
P-value
Primary
9.8%
11.7%
0.84
<0.001
Death
4.5%
5.9%
0.78
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

20. PLATO trial
“Ticagrelor works, except if you’re an American” – Stuart Pocock (LSHTM)
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

21. PLATO trial
“Ticagrelor works, except if your an American” – Stuart Pocock (LSHTM)
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

22. PLATO: A chance result?
Given 31 subgroup analyses were done, can this significant interaction be due to chance alone?
The chance of a “reversal” in sign of estimated treatment difference is not negligible.
But “region” is a special subgroup and will be of interest to US regulators (FDA).
Can this “chance” finding be explained?
Is it caused by the Aspirin (ASA) loading dose and long- term maintenance dose that patients received on day of randomization to treatment?
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

23. Does Aspirin use explain the interaction (?)
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

24. MERIT-HF trial
Metoprolol Controlled –Release Randomised InterventionTrial in Heart Failure
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

25. MERIT-HF trial in heart failure
Overall results
Endpoint
Death
Metoprolol
Placebo HR
P-value
95% CL
Lower limit
Upper limit
Sample size
1990
2001
Total deaths
145
217
0.66
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

26. MERIT-HF trial in heart failure
Make USA a subgroup
Endpoint
Death
Metoprolol
Placebo HR
P-value
95% CL
Lower limit
Upper limit
Sample size
1990
2001
Total deaths
145
217
USA 51 49
1.05
0.71
1.56
Other countries 94
168
0.55
0.43
0.70
Interaction test: P = 0.003
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

27. Break out deaths by country and treatment
metoprolol
placebo
Hungary 16 29
Germany 19 31
Netherlands 14 25
Belgium 3 13
Czech Republic 9 17
Sweden 2
Norway 6 11 UK 4
Finland
Switzerland 1
Iceland
Poland 8
Denmark
USA 51 49
Why concentrate
Interaction test
on USA?
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

28. Break out deaths by country and treatment
metoprolol
placebo
Hungary 16 29
Germany 19 31
Netherlands 14 25
Belgium 3 13
Czech Republic 9 17
Sweden 2
Norway 6 11 UK 4
Finland
Switzerland 1
Iceland
Poland 8
Denmark
USA 51 49
Why focus on USA?
Unlike the PLATO
Trial, there seem no
reason to believe
Interaction is real
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

29. Are the other/better methods to test for consistency?
Quan et al. (2010b) proposed 5 alternative methods.
These are of two types:
Methods that tend to conclude consistency until there is sufficient evidence to the contrary, e.g., interaction tests
Methods requiring a certain strength of signal of similarity in order to conclude consistency, e.g., Japanese MHLW proposals
Which type is appropriate for a given situation?
Where should the burden of evidence lie?
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

30. Five methods to test for consistency
Quan et al. (2005)
Each region should achieve a proportion, π, of the observed overall effect.
Each region should achieve a common pre-specified constant value (b ≥ 0).
Demonstrate through hypothesis testing that each region achieves a proportion, π, of the overall effect.
A test for treatment-by-region interaction must not yield a significant result.
Tests for individual regions having effects lower than the overall effect must all not yield significant results.
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

31. Difficulties in implementation of methods: Method 1
Quan et al. (2010a)
Trial planned to have 90% power, to detect a one-sided difference between two treatments with significance level
Consider a single region (e.g., Japan) out of the set of regions.
Let 𝐷 𝐽 be the estimated treatment effect in Japan and 𝐷 𝐴𝑙𝑙 be the estimated effect over all regions
Require Pr( D 𝐽 𝐷 𝐴𝑙𝑙 >0.5)≥0.8
If all treatment effects truly equal in all regions
Sample size fraction for Japan = 22.4%
Too high for a country with only 2% of the world population
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

32. Conclusions Statistical methodology for MRCTs is still evolving
Experience over time will determine acceptable methods
Issues relate to conflict in the desire to estimate a global effect versus a local (single region) effect.
Regulatory agency involvement can focus attention on a single region with unwanted consequences (e.g., for Type I error rate control, effect reversal, etc. ) familiar to users of subgroup analysis.
Definition of “a region” needs to be clarified
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

33. References
Ministry of Health, Labour and Welfare of Japan (2007). Basic Concepts for Joint Clinical Trials.
Chen, C-T, Hung, H.M.J and Hsiao, C-F, (2012). Design and evaluation of multiregional trials with heterogeneous treatment effects across regions. Journal of Biopharmaceutical Statistics, 22,
Chen, J., et al. (2011). Consistency of treatment effects across regions in multiregional clinical trials, Part 1: design considerations. Drug Information Journal, 45,
Gallo, P., et al. (2011). Consistency of treatment effects across regions in multiregional clinical trials, Part 2: monitoring, reporting and interpretation. Drug Information Journal, 45,
Kawai,N., et al. (2008). An approach to rationalize partitioning sample size into individual regions in a multiregional trial. Drug Information Journal, 42,
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

34. References
Quan, H., et al. (2010a). Sample size considerations for Japanese patients in a multiregional trial based on MHLW guidance. Pharmaceutical Statistics, 9,
Quan, H., et al. (2010b). Assessment of consistency of treatment effects in multiregional clinical trials. Drug Information Journal, 44,
Tanaka, Y. (2011). Points to consider in defining a region for a multiregional clinical trial: defining region workstream in PhRMA MRCT Key Issue Team. Drug Information Journal, 45,
Wendel, H., et al. (2001). Challenges of subgroup analyses in multinational clinical trials: experiences from MERIT-HF trial. American Heart Journal, 142,
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

35. END OF PRESENTATION
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

36. Models for MRCT data
Extensions of models for multicentre clinical trials (MCTs)
Suppose we have two treatment groups, j=1 denotes the active treatment arm, and j=2 denotes the placebo arm.
Let 𝑦 𝑖𝑗𝑘 denotes the response on patient k in treatment 𝑗 and center 𝑖. Where j=1, 2; 𝑖=1,2,⋯,𝑐 and 𝑘=1,2,⋯, 𝑛 𝑖𝑗
The full model is
𝑦 𝑖𝑗𝑘 =𝜇+ 𝜏 𝑖 + 𝛽 𝑗 + 𝛾 𝑖𝑗 + 𝜀 𝑖𝑗𝑘
𝜏 𝑖 is the effect of center 𝑖
𝛽 𝑗 is the effect of treatment 𝑗
𝛾 𝑖𝑗 is the interaction effect of center 𝑖 and treatment 𝑗
𝜀 𝑖𝑗𝑘 ~𝑁(0, 𝜎 2 )
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

37. Model II: 𝑦 𝑖𝑗𝑘 =𝜇+ 𝜏 𝑖 + 𝛽 𝑗 + 𝜀 𝑖𝑗𝑘
Assumption: With center effect, no treatment-by-center interaction
Overall treatment effect: 𝛿=( 𝛽 1 − 𝛽 2 )
Estimator (weighted): ∆ 𝐼𝐼 = 𝑖=1 𝑐 𝑤 𝑖 𝛿 𝑖 where
𝑤 𝑖 = (𝑉𝑎𝑟( 𝛿 𝑖 )) −1 𝑘=1 𝑐 (𝑉𝑎𝑟( 𝛿 𝑘 )) −1 = ( 1 𝑛 𝑖 𝑛 𝑖2 ) −1 𝑘=1 𝑐 ( 1 𝑛 𝑘 𝑛 𝑘2 ) −1
𝐸 ∆ 𝐼𝐼 =𝛿, 𝑉𝑎𝑟 ∆ 𝐼𝐼 = 𝜎 2 [ 𝑖=1 𝑐 ( 1 𝑛 𝑖 𝑛 𝑖2 ) −1 ] −1
Weights for each center are chosen according to the precision of the within-center estimate
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

38. Model III: 𝑦 𝑖𝑗𝑘 =𝜇+ 𝜏 𝑖 + 𝛽 𝑗 + 𝛾 𝑖𝑗 + 𝜀 𝑖𝑗𝑘
Assumption: With center effect and treatment-by-center interaction
Overall treatment effect: 𝛿 𝑖 𝑐 = (𝛽 1 − 𝛽 2 ) (𝛾 𝑖1 − 𝛾 𝑖2 ) 𝑐
Estimator (equally weighted): ∆ 𝐼𝐼𝐼 = 1 𝑐 𝑖=1 𝑐 𝛿 𝑖
𝐸 ∆ 𝐼𝐼𝐼 = 𝛿 𝑖 𝑐 , 𝑉𝑎𝑟 ∆ 𝐼𝐼𝐼 = 𝜎 2 𝑐 2 𝑖=1 𝑐 ( 1 𝑛 𝑖 𝑛 𝑖2 )
All centers are considered having the same importance thus receive the same weights
Model III is to be used when the treatment differences vary substantially from center to center
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

39. Framework for Random Effects Models for MCTs
Fedorov and Jones (2005)
Suppose we have two treatment groups, j=1 denotes the active treatment arm, and j=2 denotes the placebo arm.
Let 𝑦 𝑖𝑗𝑘 denotes the response on patient k in treatment 𝑗 and center 𝑖. Where j=1, 2. 𝑖=1,2,⋯,𝑐 and 𝑘=1,2,⋯, 𝑛 𝑖𝑗
The full model is
𝑦 𝑖𝑗𝑘 = 𝜇 𝑖𝑗 + 𝜀 𝑖𝑗𝑘 where
𝜇 𝑖𝑗 are random and for 𝜇 𝑖 = [ 𝜇 𝑖1 , 𝜇 𝑖2 ] 𝑇 : 𝐸 𝜇 𝑖 = [ 𝜇 1 , 𝜇 2 ] 𝑇 , 𝐶𝑜𝑣 𝜇 𝑖 = 𝜎 2 Ʌ
𝜀 𝑖𝑗𝑘 ~𝑁(0, 𝜎 2 )
Overall treatment effect 𝛿= 𝜇 1 − 𝜇 2
| PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones

41. Joachim Röhmel University of Bremen
Heterogeneity in multiregional studies and a new proposal for exact tests on interaction
Joachim Röhmel
University of Bremen

42. Reasons for regional differences can be manyfold
Genetic sensitivity
Culture
Dose regimen
Application scheme
Disease epidemiology
Disease definition
Economic standing
Health care system
Medical practice
Regulatory environment
Quality of trial conduct
Availability of concomitant medicines
Evaluation of outcomes (in particular in composite endpoints)
Insufficient standardisation and validation of scores (East Europe)
Patient compliance

44. PLATO TRIAL 2011 From Pocock et al.
Estimated treatment effects by geographic region for the primary endpoint (CV death,
MI, or stroke) of the PLATO trial (hazard ratios with 95% CIs, interaction P-value <0.05).

45. Conclusions of the FDA statistical review (Sep 2010)
From the additional analyses, we continue to be troubled by the qualitative interaction between the region (US versus non-US) and treatment.
In our view, neither play of chance nor concurrent use of ASA provides a satisfactory explanation for the US versus non-US disparity observed in this trial.
Even though multiple factors have been screened for potential causes, the question remains unsolved.

46. Conclusions of the FDA statistical review (Sep 2010)
The disparity can still be caused by the difference in standard medical practice between US and the rest of the world, which is hard to quantify and has not been quantified.
We ought to seek further data to either confirm or dismiss this disturbing finding.
Without the data, we would recommend that this drug not be approved.
Another study should be required if this drug is to be approved for use in US.

47. Pockock‘s conclusions
In the PLATO trial, the between-region comparison was one of 32 pre-planned subgroup analyses, and hence purely by chance one could expect one or two such analyses to have interaction P0.05.
Furthermore, post hoc emphasis on the most striking subgroup finding (geography, in this case) means that even if the finding is not entirely due to chance, the observed data are prone to exaggerate any true disparities (between regions).
Alternatively, one can assess all 43 countries separately, and the global interaction test for heterogeneity among the 43 hazard ratios yields P =0.95.

48. FDA APPLICATION NUMBER:022560Orig1s000
The study center effect was statistically significant in the main effect ANCOVA model. This indicates potential heterogeneity of efficacy responses across the 6 centers. …
The mean percent change from baseline BMD in lumbar spine was ranging from
2.5% in the US/Canada ( 139 subjects),
3.1% in Hungary ( 90 subjects),
3.2% in Argentina (222 subjects),
3.2% in France and Belgium ( 64 subjects),
3.8% in Poland (147 subjects), and
3.9% in Estonia ( 140 subjects) .
Results of subgroups analyses are not powered to draw any meaningful statistical conclusion, mainly due to small number of subjects in subgroups.

49. R.T.O‘Neill, May 28, 2009

50. R.T.O‘Neill, May 28, 2009

51. R.T.O‘Neill, May 28, 2009

53. Social Court of the Berlin-Brandenburg Reference number: L 1 KR 140/11 KL Dec 6, 2011
Company complains against Escitalopram being merged with all other (generic) SSRIs, which means low reimbursement
Company wins first stage battle in court
Health Insurance replies (actually based on IQWiG arguments) :
The results of the Yevtushenko study (2007) (conducted solely in Russia) lie extraordinarily above the estimates of the other studies. Comparability is therefore critical.
Furthermore, the applicability of study results may not be given in the context of German patient care. Generally, it is necessary to take stronger regard to cultural aspects in depression.

54. How do we define region? How do we define consistency?
K. J. Caroll, AstraZeneca, 2011

55. What constitutes a region?
America
North
Latin
South
Europe
East
Asia
China
India
Japan
South-East
By Country?
Significance of Interaction often disappears when 3 or more regions are included (Caroll, 2011)

57. Common criteria (Quan et al. DIJ, 2010)
Achieving in each region a proportion of the observed overall effect
Observing in each region an effect above a certain threshold
Tighten 1. by subsituting the lower limit of CIs instead of the observed values
Absense of statistical significance in interaction tests, usually at significance levels >>0.05
Lack of clinically significant differences from the overall

59. Strong Interaction

60. Strong Interaction may look less impressive when splitting one category into two

61. Digging fast and deep is possible

62. For exploratory subgroup analyses including regional subgroups
Baysian methods may be useful

64. (Penello , BASS Conference 2013)

65. (Penello , BASS Conference 2013)

66. An alternative conditional (permutation) approach to interaction

67. … full population decomposes into k 2x2 tables aR1 aT1 s aR0 aT0 f nR
Treat R
Treat T
Totals
Events
aR1
aT1 s
~Events
aR0
aT0 f nR nT n
stratum/subgroup1
stratum/subgroup k
Treat R
Treat T
Totals
Events
a1R1
a1T1 s1
~Events
a1R0
a1T0 f1
n1R
n1T
n1
Treat R
Treat T
Totals
Events
akR1
akT1 sk
~Events
akR0
akT0 fk
nkR
NkT
nk …

68. Search for all configuations such that summation over
n1R
F10
n1T
a1R1
a1R0
a1T1
b1T0
ZELEN:
Search for all configuations
such that summation over
Columns (successes, failures),
Rows (numbers randomised to R or T) and strata (e.g. regions)
gives identical results.
S21
n2R
F20
a2R1
n2T
a2R0
a2T1
a2T0
  
Sk1
nkR
F10
akR1
nkT
akR0
akT1
akT0
VR1
VR0
VT1
VT0

69. New proposal: Search for all configuations such that summation over
n1R
n1T
a1R1
a1R0
a1T1
New proposal:
Search for all configuations
such that summation over
Columns (successes, failures),
Rows (numbers randomised to R or T) per each Stratum (e.g. regions)
and the total numbers of successes (and failures) gives identical results
a1T0
n2R
a2R1
n2T
a2R0
a2T1
a2T0
  
nkR
akR1
nkT
akR0
akT1
akT0
VR1
VR0
VT1
VT0

70. “Zelen/BreslowDay test (Z/BD)“ versus “less restricted permutations (LRP)“
Z/BD | LRP
Column totals per each stratum constant 
Total no of Events in Treat R (sum over all strata) constant
Total no of Events in Treat T (sum over all strata) constant
Row totals per stratum constant
---

71. Zelen‘s exact conditional approach
all margins fixed }

72. Breslow/Day conditional extention
(StatXact 10)
Zelen‘s set  of configurations 
!!IMPORTANT!!
„Independently ordering the sample space“ is the principle that allows developing more exact tests for discrepancy based on other effect measures
such as RR or  or …

73. Extention allows for a variety for measuring effects in the full population
Difference  = (a1  m1) - (a2  m2)
Relative risk RR = (a1  m1)  (a2  m2)
Odds ratio OR = (a1 ( m1-a1))  (a2  (m2-a2))

74. and further measures of discrepency
Difference Difference of Differences
CI for interaction (Newcombe 1998)
k=2: discepency = subgroup 1 - subgroup 2
k>2: discepency = ij (subgroup i - subgroup j)2
Relative risk Ratio of relative risks
RRdiscepency =log [RRsubgroup 1/RRsubgroup 2]
RRdiscepency = ij (log [RRsubgroup i /RRsubgroup j])2
Odds ratio Zelen‘s test for homogeneity of odds ratios
Breslow-Day test

75. Situation investigated – typical for Biomarker + /-
Binomial trials with two strata
Success
probs
Treatm1
Treatm2
Biomarker - p1
Biomarker + p2

78. More details for three or more regions
This is an issue for the next lesson Thank you very much for your attention
( I propose to study this in the next lesson)
Thank you very much for your attention

80. Tomokazu Inomata Novartis, Japan PSI, May 13th, 2014
Multi-Regional Clinical Trials from the Japanese viewpoint - Current practice and future challenges -
Tomokazu Inomata
Novartis, Japan
PSI, May 13th, 2014
“Disclaimer : The views and opinions expressed in the following slides are those of individual presenter and should not be attributed to Novartis”

81. Motivation
MRCT has become most popular approach for efficient drug development in Japan
Simultaneous global drug development / submission is our (=Novartis Japan) standard approach
The Japanese regulatory guidance, “Basic Principles on Global Clinical Trials (GCTs)” was issued in 2007 (Note : GCT = MRCT)
Many discussions/conferences regarding the guidance (Japanese sample size, consistency, ..) were held ・・・
A good opportunity to share our experiences of MRCTs
Statistical approach vs. Practical / Feasible approach
|Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only

82. Outline Concluding remarks Trend in Japan drug development
Relevant guidance for MRCTs
Characteristics of MRCT
Current practice
An example
Possible considerations and future challenges
Concluding remarks
|Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only

83. Trend in Japan drug development
Relevant Guidance for MRCTs
1998: ICH-E5 “ETHNIC FACTORS IN THE ACCEPTABILITY OF FOREIGN CLINICAL DATA”
“Bridging study” was accepted for “resolving Drug Lag”
Intended to extrapolate foreign data (PIII) to the Japanese population and is generally conducted as a dose-finding study in Japanese subjects
Many discussions on how to evaluate “Similarity” of dose-finding profile between Japan population and Non-Japan population.
2003: ICH-E5 Q&A’s 　 2006: ICH-E5 Q&A’s (R1) - Points to Consider on MRCT was added –
2007: Basic Principles on Global Clinical Trials
“MRCT” was encouraged for “efficient and rapid drug development”
Many discussions on how to evaluate “Consistency” between Japan population and Non-Japan population.
2012: Basic Principles on GCTs (Ref. Cases / 17 Q&A’s)
2013: Guideline on Data Monitoring Committees
Japan Local guidance
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84. Number of Approvals in Japan
1998
ICH-E5
2006
ICH-E5 Q&A(R1)
2007
Basic Principles on Global Clinical Trials
2012
“Basic Principles on Global Clinical Trials (Reference Cases)
Number of Approvals in Japan
- Bridging studies vs. MRCTs -
In 2010, more than 20% of the total clinical trials were MRCTs (Source: Ando,Y., et al, 2012)
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85. “Basic Principles on Global Clinical Trials”
Basic principles for design and conduct of GCTs are provided in the 12 Q&As
Q 1: Basic requirements
Q 2: Timing for Japan to participate
Q 3: Phase I trial or PK information
Q 4: Dose-finding study in Japanese
Q 5: Basic points to consider in designing
Q 6: Sample size determination
Q 7: Primary endpoint / evaluation index
Q 8: Position of separate domestic study
Q 9: Control arm
Q10: Concomitant medication / therapy
Q11: Recommended disease area
Q12: Decision chart for joining MRCTs
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86. Basic Principles on Global Clinical Trials
- Some of Key features -
“Region”
No clear definition in the Guidance,
But consider Japan as one region
Need a reasonable definition of “region” in each study considering medical practice, guideline or drugs for the disease, etc.
“Consistency” between Japan and the entire study population
No clear definition in the Guidance
Need a justification as to why the entire population can be deemed as one population.
“Sample Size & Proportion of Japanese subjects”
No established recommendable method....
But two specific methods for calculating sample size are introduced
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87. Two Methods for calculating sample size
As an example a placebo-controlled study using quantitative endpoints
Method 1
Method 2
However,
Specific sample size setting for individual cases can be discussed with PMDA on the clinical trial consultation
It is necessary that designs and analytical methods for the global clinical trial should be acceptable to Japan
Probability of “DJapan / Dall > 0.5“ is 80% or higher
Condition
D: Difference between the placebo group and test drug group
Dall: Difference in the entire population
DJapan: Difference in the Japanese population
Minimizing the Japanese sample size increases the total sample size
Minimizing the total sample size increases the Japanese sample size.
Probability of “Each of Di exceed 0” is 80% or higher
Condition
Di : Difference between the placebo group and test drug group in each region i
The probability tends to increase if equal number of subjects is enrolled from each region.
The Japanese sample size can be set without changing the total sample size
“It is recommended to consult with PMDA reviewers to
discuss the study design / Japanese proportion prior to
fixing the protocol globally “
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88. Characteristics of MRCTs in Japan
Based on 31 approved drugs between 2006 and 2013 (excluding Oncology)
Study Phase
PIII: 29 (94%)
PII: 2 (6%)
Not many dose response studies were conducted as MRCTs
But, number of MRCTs in earlier stage has been increased
Region
Asian trials: 13 (42%)
Global trials*: 18 (58%)
No harmonized definition of “Asian region”
* Global trials include Asian countries
Source : PMDA review reports
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89. Characteristics of MRCTs in Japan
Based on 31 approved drugs between 2006 and 2013 (excluding Oncology)
Sample Size determination
Use Method 1 or/and 2 described in the Japanese guidance
No statistical calculation but feasible numbers
Japanese proportion
All MRCTs : 32% ( %)
Asian Trials: 48% ( %),
Global Trials: 21% ( %)
* Larger Proportion of Japanese population in the Asian Trial
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90. Characteristics of MRCTs in Japan
Proportions of the Japanese sample size to the total sample size based on the Clinical Trial Notifications of MRCTs
Many cases the proportion of the Japanese sample size is <0.20
For larger sample size, the proportion of Japanese subjects is <0.10
(Source :Ando and Uyama 2012)
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91. Current practice - An example - Possible considerations and future challenges
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92. An example - Seebri (COPD medication) –
Submitted in Nov 2011 Approved in Sept 2012
Phase III MRCT
Study Design :
A 26-week treatment, randomized, double-blind, placebo-controlled, parallel group study to assess the efficacy, safety and tolerability of Seebri in patients with Chronic Obstructive Pulmonary Disease (COPD)
Total Sample Size
755 (Seebri : 512, Placebo: 243)
Japanese Sample Size : 92 (62, 30)
12% of the entire population
Calculated based on Method 1
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93. An example - Seebri (COPD medication) –
Submitted in Nov 2011 Approved in Sept 2012
Study Design
Region :
13 countries grouped by 6 regions :
Japan ⇒ Japan
Korea, Singapore, India ⇒ Asia
US, Canada ⇒ North America
Argentina ⇒ South America
Australia, Netherlands, Spain ⇒ European Union
Turkey, Romania, Russia ⇒ Eastern Europe
No specific definition of the region documented
Randomization was stratified by region
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94. An example - Seebri (COPD medication) –
Objectives
Primary objective
To demonstrate that Seebri vs. placebo significantly increases trough FEV1 (Forced expiratory volume in one second) following 12 weeks of treatment in patients with moderate to severe COPD
Secondary objectives
To evaluate the effect of Seebri vs. placebo on the health status by measuring the total score of the St George’s Respiratory Questionnaire (SGRQ) after 26 weeks treatment.
To evaluate the effect of Seebri vs. placebo on breathlessness measured using the Transition Dyspnea Index (TDI) after 26 weeks treatment.
Many more efficacy variables were evaluated...
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95. An example - Seebri (COPD medication) –
Results : Patient background
ALL
Japan
Seebri
Pbo
total n
550
267
817 64 32 96
Age【<65 years】n (%)
278 (50.5%)
134 (50.2%)
412 (50.4%)
14 (21.9%)
9 (28.1%)
23 (24.0%)
Age【>=65years】n (%)
272 (49.5%)
133 (49.8%)
405 (49.6%)
50 (78.1%)
23 (71.9%)
73 (76.0%)
Age【Mean（SD）】yr
63.8 (9.47)
64.0 (8.96)
63.9 (9.30)
69.1 (8.00)
67.4 (9.75)
68.5 (8.61)
Weight【<60 kg】n (%)
139 (27.0%)
67 (27.6%)
206 (27.2%)
33 (51.6%)
13 (43.3%)
46 (48.9%)
Weight【>=60 kg】n (%)
375 (73.0%)
176 (72.4%)
551 (72.8%)
31 (48.4%)
17 (56.7%)
48 (51.1%)
Severity of【Moderate】n (%)
331 (60.2%)
166 (62.2%)
497 (60.8%)
42 (65.6%)
29 (90.6%)
71 (74.0%)
COPD【Severe】n (%)
217 (39.5%)
99 (37.1%)
316 (38.7%)
21 (32.8%)
3 (9.4%)
24 (25.0%)
　　　　【very severe】n (%)
2 (0.4%)
2 (0.7%)
4 (0.5%)
1 (1.6%)
0 (0.0%)
1 (1.0%)
Duration of COPD【Mean（SD）】 yr
5.87 (5.798)
6.49 (6.790)
6.07 (6.143)
2.83 (3.067)
2.95 (3.328)
2.87 (3.139)
Smoking history【Ex-smoker】n (%)
370 (67.3%)
176 (65.9%)
546 (66.8%)
47 (73.4%)
19 (59.4%)
66 (68.8%)
Smoking history【Current smoker】n (%)
180 (32.7%)
91 (34.1%)
271 (33.2%)
17 (26.6%)
13 (40.6%)
30 (31.3%)
Japanese Patients are older & lighter & ...
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96. An example - Seebri (COPD medication) –
Efficacy-primary endpoint- Trough FEV1 after 12 weeks treatment
ALL
Japan
Seebri (n=512)
Pbo (n=243)
Seebri (n=64)
Pbo (n=30)
Baseline
1.321
1.274
1.253
1.340
Trough FEV1 at Week 12【LS mean/(SE)】
1.408
(0.0105)
1.301
(0.0137)
1.404
(0.0332)
1.296
(0.0432)
Treatment difference【LS mean/(SE)】
[95%CI]
0.108
(0.0148)
(0.0466)
[ to ]
p<0.001
[ to ]
p=0.022
Consistent treatment difference, but
larger std error in Japan due to small sample size
ANCOVA model: Trough FEV1 = treatment + baseline FEV1 + baseline ICS use (Yes/No) + FEV1 reversibility components + baseline smoking status + region + center(region). region as fixed effects with center nested within region as a random effect.
Source: CTD
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97. Analysis of St George’s Respiratory Questionnaire (SGRQ) total score at Week 26 (FAS)
ALL
Japan
Seebri (n=502)
Pbo
(246)
Seebri (n=63)
Pbo (n=31)
Baseline
46.11
46.34
38.92
42.28
Week 26
LS Mean (SE)
39.50
(0.813)
42.31
(0.992)
34.70
(1.790)
37.20
(2.502)
Treatment difference
LS mean/(SE)】
[95%CI]
-2.81
(0.961)
-2.31
(2.613)
[ to ]
P=0.004
[ to 2.683]
p=0.340
Close similarity in treatment difference larger std error in Japan & quite different p-values
ANCOVA model: SGRQ total score = treatment + baseline SGRQ score + baseline ICS use (Yes/No) + FEV1 reversibility components + baseline smoking status + region + center(region). Center is included as a random effect nested within region.
Source: CTD
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98. Efficacy-secondary endpoint- Analysis of Transition Dyspnea Index (TDI) focal score at Week 26 (FAS)
ALL
Japan
Seebri (n=493)
Pbo
(240)
Seebri (n=62)
Pbo (n=31)
Baseline
6.18
6.30
7.27
7.35
Week 26
LS Mean (SE)
1.84
(0.257)
0.80
(0.294)
0.98
(0.558)
1.02
(0.704)
Treatment difference
LS mean/(SE)】
[95%CI]
1.04
(0.235)
-0.04
(0.697)
[0.583 to 1.504]
P<0.001
[ to 1.347]
p=0.955
The treatment differences are quite different
Inconsistent result ?
ANCOVA model: TDI focal score = treatment + BDI + baseline ICS use (Yes/No) + FEV1 reversibility components + baseline smoking status + region + center(region). Center is included as a random effect nested within region.
Source: CTD
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99. PMDA questions related to regional difference
Explain reason for the different treatment effects between Japanese subpopulation and the entire population
Why the TDI score got to worse for Japanese population etc.?
Explain whether any demographic difference caused treatment effect or not
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100. Key Considerations for MRCT
- Opportunities and Challenges -
Assessment of Consistency
Not only the Primary endpoint but also the secondary endpoints would play an important role for helping the assessment of “consistency”
Potential effect of the difference need to be evaluated in advance
Regions should be pre-defined based on intrinsic / extrinsic factors
<Seebri’s case >
Explain any influence of the following factors for each country, then explain if there are no big difference in condition of COPD between Japan and Non-Japan patients
Cause of COPD, Onset of COPD
Standard Treatment, medical practice
Environment (climate etc.) ,
Disease definition, diagnostic
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101. Key Considerations for MRCT
- Opportunities and Challenges -
Assessment of Consistency
Not only hypothesis testing (DJapan / DAll > 0.5, treatment by region, etc.) but also point estimate, CIs, graphical presentation would be helpful
Not only comparing between Japan sub population vs. entire study population but also displaying other regional populations could be helpful
Sample Size for Japanese sub-population
A single MRCT could provide limited information
Important for Japanese patients to enroll in MRCTs from earlier phase and dose response study, PK/PD
But no established method for sample size determination for DF study
Limited number of Japanese sample size for each dose
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102. Other Considerations for MRCT -
- Opportunities and Challenges -
Consistency assessment on Safety data
No well established statistical method available
More comprehensive approach to analyzing safety data is necessary
Quality of Clinical Trial
Need a good Quality Management on Operational aspects
Operational errors in clinical trials detected in the era of complex design clinical trials and global drug development (Ando (2012) at Bios Workshop )
Decision making at Interim analyses
Japanese sub-population assessment / Consistency evaluation necessary for early termination??
Speed of the subjects enrollment need to be well managed
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103. Guideline on Data Monitoring Committees
Issued on April 4th, 2013
3. Setup and implementation of DMC
3.1 Composition of DMC
When DMC is established in a large-scale global clinical trial, it is considered appropriate essentially as possible to select a representative for each participating region or some of regions as committees.
When Japanese subjects participate in such a global clinical trial, it may be desirable that specialists in Japan participate as DMC committee members considering on medical environment in Japan and existing safety information. In case that it is difficult, it should be considered how to evaluate safety of Japanese subjects beforehand.
Of note, when the special safety monitoring for Japanese subjects is required by reason that experience of study treatment and safe information is poor as compared with other regions, it is more significant that Japanese specialists participate as DMC members.
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104. Statistical approach vs. Practical/Feasible approach
Concluding Remarks
for successful simultaneous global drug development
Knowledge and experience of MRCT can be shared among academia, industry and regulatory agencies,
Looks similar, Not markedly different Not surprisingly different
How much we know/share information prior to the study is the key to success
Improvement of current practice, “operationally and methodologically” is required
Statistical approach vs. Practical/Feasible approach ??
Japan vs. Non-Japan ??
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105. References
ICH International Conference on Harmonization Tripartite Guidance E5. Ethnic Factor in the Acceptability of Foreign Data, (1998).
ICH International Conference on Harmonization Tripartite Guidance E5. Ethnic Factor in the Acceptability of Foreign Data, Questions & Answers (2006).
Ministry of Health, Labour and Welfare of Japan (2007). Basic Concepts for Joint Clinical Trials
Ministry of Health, Labour and Welfare of Japan (2012). Basic Concepts for Joint Clinical Trials. (Reference Cases)
Ministry of Health, Labour and Welfare of Japan (2013). DMC Guidance
Kawai, N., et al. (2008). An approach to rationalize partitioning sample size into individual regions in a multiregional trial. Drug Information Journal, 42,
Ando, Y. and Uyama, Y. (2012). Multiregional Clinical Trials : Japanese perspective on Drug Development Strategy and Sample Size for Japanese Subjects. Pharmaceutical Statistics, 22,
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106. References
Ando, Y. and Hamasaki, T. (2010). Practical issues and lessons learnted from multi-regional clinical trials via case eamples : a Japanese perspective. Pharmaceutical Statistics, 9,
Tsou, H-H., et al. (2010). Proposals of Statistical consideration to evaluation of results for a specific region in multi-regional trials – Asian perspective
Ikeda, K., (2013) Overview of multi-regional trials conducted in Japan. Novartis China Biostatistics Workshop
Ando, Y., (2012) Looking beyond ICH-E9 in the Era of Global Drug Development. Bios Summer Workshop 2012 in Osaka
Pharmaceuticals and Medical Devices Agency. Review Report of Seebri.
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107. End
Thank you very much !
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