Statistical considerations for a multi-regional trial

1. 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

2. 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.

3. 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

4. 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.

5. 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

6. 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

7. 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

8. 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 allowsinterpretation of the results • Identify controllable factors • Influencing baseline variables and patient characteristic • Subtype of a disease studies • Severity • Stratification by controllable factors

9. 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

10. 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.

11. 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

12. Mean response of each region

13. Definition of the treatment main effect

14. Definition of the treatment main effect

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. To get observed mean difference greater than MCSD assuming no interaction effect

22. 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＝/280％=> 1.06n, 90％=>2.262n, 95％=>3.62n • MCSD＝/380％=> 0.67n, 90％=>1.34n, 95％=>2.1n

23. 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

24. 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

25. Backup

26. 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

27. 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

28. 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

29. 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

30. 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