statistical considerations for a multi-regional trial
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Statistical considerations for a multi-regional trial. Hiroyuki Uesaka, Ph. D October 28, 2003 Kitasato University-Harvard School of Public Health Symposium ANA Hotel Tokyo. Acknowledgement. The multi-regional/national trials were extensively discussed among - PowerPoint PPT PresentationTRANSCRIPT
Statistical considerations for a multi-regional trial
Hiroyuki Uesaka, Ph. D
October 28, 2003
Kitasato University-Harvard School of Public Health Symposium
ANA Hotel Tokyo
Acknowledgement
• The multi-regional/national trials were extensively discussed among – Mr. Thoru Uwoi (Chairman of JPMA ICH project committee;
Yamanouchi Pharmaceutical Co.,Ltd)
– Dr. Kihito Takahashi (Cordinator of JPMA ICH project committee efficacy part; Banyu Pharmaceutical Co.,Ltd)
– Mr. Toshinobu Iwasaki (member of JPMA ICH-E5 IWG; Shionogi Pharmaceutical Co.,Ltd.)
– Dr. Toshimitsu Hamasaki (member of JPMA ICH-E5 IWG; Pfizer Japan Inc.)
• The speaker would like to thank all of them.
Today’s talk
• General consideration for a multi-regional trial• Primary hypothesis of a multi-regional trial• Testing treatment difference• Sample size allocation and power• Conclusion
Introduction• There is still considerable gap in the NDA filing time
among regions• Simultaneous development would be most efficient.• Multinational study is one possibility in this situation.• There is an increasing interest in simultaneous
development among regions including Japan as well as the USA and EU countries.
• However, there is no discussion among regulators, academia and industries about design and statistical analysis of a multinational trial.
• This presentation is to give a chance to discuss these topics.
Purpose of a multi regional/national study
• To establish the efficacy of a drug on a disease where it is difficult to enroll sufficient number of subjects within a reasonable time period.– Rare disease
– A trial whose primary variable is survival or event rate
• To establish the efficacy of a drug among countries where ethnic differences are assumed negligible– Multinational trial conducted in EU and USA
– Multinational trial conducted in Asian countries
• To investigate the effect of ethnic differences on response to a drug– Bridging study
Multi-regional/national trial to be discussed here
• Type of a trial– To establish the efficacy of a drug among countries
where ethnic differences are assumed negligible
• Study design– Placebo controlled parallel group randomized
study
• Study objective– To establish efficacy of an investigational
medicinal product against placebo
Prerequisite of a trial
• Assessment of regional differences which may affect the drug effect– Factors to be investigated
• Lifestyle, cultural or socioeconomic factors, geographic environment
• Medical practice, study environment• Epidemiological characteristics of a disease studied• Intrinsic factor to produce inter-individual differences
– Actual status of regional difference– Possible differences in the response and adverse events
• Appropriateness of dose and dose regimen to be studied
Objective of a trial with a single protocol
• To apply the result of treatment main effect to all participating regions/countries
But• It is reasonable to assume some regional
difference in treatment effect– A design which allows interpretation of the results– Identify controllable factors
• Influencing baseline variables and patient characteristic• Subtype of a disease studies• Severity
– Stratification by controllable factors
Primary hypothesis and its validity
• Primary hypothesis to be confirmed– The test drug is superior to placebo in an overall mean difference
• Expected result– Statistically significant difference in the overall mean response
• Applicability and generalizability of the result– In principle, the primary result is applied to all participating
countries/regions
• Validity of the hypothesis– Is it possible to assume a priori that the interaction between treatment-
by-region/country is negligible?• From the information on the existing drugs in the same class or prior
studies• From pharmacological characteristics of the drug, etiological or
epidemiological nature of the disease– Confirmation by the study results
Analysis of treatment main effect-ICH-E9 guideline-
• Multicenter study– The main treatment effect may be investigated first using a
statistical model which allows for the center difference but does not include the term treatment-by-center interaction.
– In the presence of true heterogeneity of treatment effect, the interpretation of treatment main effect is controversial.
– Alternative estimates of treatment effect may be required, giving different weights to centers, to substantiate the robustness of treatment effect.
• Covariate or subgroups– In most cases, subgroup or interaction analysis are exploratory,…,
they should explore the uniformity of any treatment effect found overall.
Definition of the treatment main effect
• Difference between the treatment’s overall means
– A simple average of the mean of each region – A weighted average of the mean of each region
• The precision of mean difference of each region: reciprocal of variance of the mean difference at each region
• Other region specific weight
Mean response of each region
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test drug
Placebo
Multi-regional/national trial
Population means Nations
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Definition of the treatment main effect
Region Overall treatment
mean difference
J A E U
Mean (test) 6 5 4 3.5
Mean (control) 0 1 1 0.5
Difference 6 4 3 3
Equal weight 1x6 1x4 1x3 1x3 16/4=4
Sample size 5x6 5x4 45x3 45x3 320/100=3.2
Sample size 45x6 45x4 5x3 5x3 480/100=4.8
Definition of the treatment main effect
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(equal weight)
(5,5,45,45)
(45,45,5,5)
Multi-regional/national trial
Population mean difference and overall mean difference Nations
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Treatment main effect and power• Weighted analysis(model without Interaction: A, with interaction B),
• Unweighted mean: C, Simple two sample: D
• Treatment difference: 4.0, error SD=10 (Effect size =40%)
• Significance level of test treatment effect ： One-sides 2.5%; Interaction test: 5%
• Sample size =100: 80% of the power of the detecting 40% effect size
Region Treatment diffe-rence
Test of treatment main effect Inter-actionJ A E U A B C D
Test 6 5 4 6
control 0 1 1 3
Case1 25 25 25 25 4.0 80.5 80.7 80.7 80.4 9.9
Case2 5 5 45 45 3.2 61.9 61.9 39.1 61.5 5.8
Case3 45 45 5 5 4.8 92.1 92.2 39.0 92.1 8.9
Case4 10 10 40 40 3.4 66.9 67.0 61.4 66.5 7.4
Case6 40 40 10 10 4.6 90.0 90.1 61.6 89.9 9.6
Case6 15 5 40 40 3.5 69.5 69.7 51.8 69.3 9.1
Effect of sample sizes imbalance on the power of test for treatment main effect
• Weighted analysis(model without Interaction: A, with interaction B),
• Unweighted mean: C, Simple two sample: D
• Treatment difference: 4.0, error SD=10 (Effect size =40%)
• Significance level of test treatment effect ： One-sides 2.5%; Interaction test: 5%
• Sample size =100: 80% of the power of the detecting 40% effect size
Region Treatment diffe-rence
Test of treatment main effect Inter-actionJ A E U A B C D
Test 6 5 4 6
control 0 1 1 3
Case1 25 25 25 25 4.0 80.5 80.7 80.7 80.4 9.9
Case2 5 5 45 45 3.2 61.9 61.9 39.1 61.5 5.8
Case2’ 5 5 5*9 5*9 3.2 61.3 61.4 61.4 61.2 5.4
Case3 45 45 5 5 4.8 92.1 92.2 39.0 92.1 8.9
Case3’ 5*9 5*9 5 5 4.8 92.3 92.7 92.3 92.2 6.2
Treatment main effect and power(A case of no interaction)
• Weighted analysis(model without Interaction: A, with interaction B),
• Unweighted mean: C, Simple two sample: D
• Treatment difference: 4.0, error SD=10 (Effect size =40%)
• Significance level of test treatment effect ： One-sides 2.5%; Interaction test: 5%
• Sample size =100: 80% of the power of the detecting 40% effect size
Region Treatment diffe-rence
Test of treatment main effect Inter-actionJ A E U A B C D
Test 6 5 4 4.5
control 2 1 0 0.5
Case1 25 25 25 25 4.0 80.1 80.1 80.1 79.9 4.6
Case2 5 5 45 45 4.0 79.9 79.9 39.5 79.8 5.1
Case3 45 45 5 5 4.0 80.2 80.2 39.0 80.0 5.0
Case4 10 10 40 40 4.0 80.5 80.4 61.9 80.3 4.9
Case6 40 40 10 10 4.0 80.5 80.4 61.2 80.3 4.3
Case6 15 5 40 40 4.0 80.4 80.3 51.2 80.3 5.0
Test of treatment by country/region (Null case)• Weighted analysis(model without Interaction: A, with interaction B), • Unweighted mean: C, Simple two sample: D• Treatment difference: 4.0, error SD=10 (Effect size =40%)• Significance level of test treatment effect ： One-sides 2.5%; Interaction test: 5%• Sample size =100: 80% of the power of the detecting 40% effect size• No treatment effect
region Treatment difference
Treatment main effect Interaction effect
J A E U A B C D
Test 2 0 -1 -1
control 0 0 0 0
Case1 25 25 25 25 0 2.44 2.52 2.52 2.43 10.04
Case2 5 5 45 45 -0.8 0.56 0.58 2.54 0.56 6.49
Case3 45 45 5 5 0.8 8.28 8.34 2.61 8.33 9.56
Case4 10 10 40 40 -0.6 0.78 0.80 2.44 0.77 8.00
Case5 40 40 10 10 0.6 6.07 6.17 2.59 6.08 9.48
Case6 15 5 40 40 -0.5 0.91 0.91 2.75 0.88 8.34
Summary of testing treatment main effect
• When there is no interaction effect– Weighted analysis is more powerful than unweighted
analysis• Not affected by imbalance in sample sizes among regions• Statistically more powerful
• When there is interaction effects– Sample size imbalance among regions may severely
inflate the type I error rate– To apply the significant result of the treatment main
effect, unweighted mean should be used
A trial to observe the treatment difference greater than MCSD
• If a region shows treatment difference close to zero?– Is it due to too small sample from that region?
– Is it due to too low power to detect regional/country difference
– Does it suggest regional/country difference
• Points to consider for study design– Assume that regional/country difference is negligible
– Enroll enough subjects to give a point estimate greater than MCSD
To get observed mean difference greater than MCSD assuming no interaction effect
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MCSD=delta/2
MCSD=delta/3
Multi-regional/national trial
Overall population mean and MCSD (half and one thirds of overall mean) Nations
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A trial to observe the treatment difference greater than MCSD
• Assumption– There is no regional difference in treatment means
• Sample sizes– 4 regions have common treatment difference: – Power of test for treatment main effect 90%at one-sided
2.5% significance level
– Equal sample size at all regions: n
– Probability of getting observed mean difference >MCSD is 80, 90 and 95%, respectively.
• MCSD ＝ /280 ％ => 1.06n, 90 ％ =>2.262n, 95 ％ =>3.62n
• MCSD ＝ /380 ％ => 0.67n, 90 ％ =>1.34n, 95 ％ =>2.1n
Sample size
• Determine the target number of subjects to be enrolled in each region/country– The method of testing treatment main effect should be
determined prior to sample size estimation
– Equal numbers among regions/countries is most desirable from statistical perspective
– The number enough to give point estimate which is greater than minimum clinically significant difference between treatments in every or a specific region/country
Conclusion
• Design and statistical method should be discussed• Method of analysis of treatment main effect
should be pre-defined• Result of treatment main effect may vary
depending on the definition of treatment main effect and regional sample sizes
• Equal sample size is important for controlling both type I and Type II errors
• To give sample size for assuring point estimate which is greater than MCSD
Backup
Ethnic factors to be considered for study design
• Definition of a disease and diagnosis• Epidemiological characteristics of patients and enrolled
subjects– Distributions of disease subtypes and severity
• Dose and dose regimen of the test drug and control• Treatment objective, primary variables, timing of
measurement and criteria of efficacy• Evaluation and reporting safety information• Medical practices
– Hospitalization/outpatient, patient care, practitioners/specialized hospital, etc.
• Available concomitant treatments and actual uses
Interpretation of the result
• Is the result applicable to all regions/countries• What is the significance of the result in the
regional culture, socioeconomic and geographical conditions, and medical practices and environment
Statistical analysis plan
• Definition of analysis set• Comparability among regions/countries
– Attrition of subjects and reasons for attrition– Protocol violations: reasons and frequency– Concomitant medication/treatments, dose and dose regimen– Demographic factors, disease type and severity
• Confirmation of efficacy– Definition of treatment main effect and statistical model for the
analysis of treatment main effect– Analysis of treatment by region/country interaction– Adjustment for covariates– Important interaction effect between covariate
• Analysis of safety
Evaluation of interaction effect
• Clinically significant size of the interaction effect– Relative to the size of the mean difference between
treatments– If there exists an cross-over interaction, evaluate
treatment difference by region/country
• Is non-cross over interaction of no importance?– The region where there is no significant difference
between treatments. – Is it necessary for the point estimate of the treatment
difference to be greater than minimum clinically significant difference
Assessment of the interaction effect
• In case that is no evidence of treatment by regional interaction effect– Evidence that there is no interaction effect
• If the test of treatment main effect is significant, testing treatment by region interaction is performed
• In case some data suggest appreciable interaction effect– Non-cross over interaction
• Sample size to show at least the point estimate is greater than minimum clinically significant difference