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

Definition of the treatment main effect RegionOverall treatment mean difference JAEU Mean (test) Mean (control) Difference6433 Equal weight1x61x41x3 16/4=4 Sample size5x65x445x3 320/100=3.2 Sample size45x645x45x3 480/100=4.8

Definition of the treatment main effect

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 RegionTreat ment diffe- rence Test of treatment main effectInter- action JAEUABCD Test6546 control0113 Case Case Case Case Case Case

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 RegionTreat ment diffe- rence Test of treatment main effectInter- action JAEUABCD Test6546 control0113 Case Case Case2’555* Case Case3’5*

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 RegionTreat ment diffe- rence Test of treatment main effectInter- action JAEUABCD Test control Case Case Case Case Case Case

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 regionTreat ment differ ence Treatment main effectIntera ction effect JAEUABCD Test20 control0000 Case Case Case Case Case Case

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

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

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