Norisuke Kawai Clinical Statistics, Pfizer Japan Inc. Evaluation of a Multi-regional Trial for Global Simultaneous Drug Development Norisuke Kawai Clinical Statistics, Pfizer Japan Inc.

Agenda 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) (N=161) (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) Hemorrhagic stroke 40 (0.44) 78 (0.86) 0 (0) 2 (1.14) Systemic embolism 15 (0.09) 17 (0.10) 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 Typically, a bridging study is a confirmatory study (almost the same sample size as the foreign P2 study). Phase 3 Data Extrapolation to the new region Phase 2 (Dose-Response) Data Phase 2 (Dose-Response) Data Comparison New region (e.g. Japan) Foreign region(s)

“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….

Overall Study Population A Case of a MRCT MRCT Overall Study Population Total Sample Size:18201 (Number of Events: 477) Comparison (too much focused?) Japan portion Sample Size: 336 (Number of Events:9 ) 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 rest of the world Results from “our nation (ex. Japan)” “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 ?

Homogeneous (consistent) Heterogeneous (inconsistent) Objectives of MRCTs Primary objective Confirm efficacy and safety of the study drug in the overall study population A key secondary objective 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 Overall results (efficacy, safety) Findings from the other studies Knowledge of the other drugs in the same drug class, etc. Layer 2 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. Benefit:Risk Assessment For Japan Benefit:Risk Assessment For Region A Benefit:Risk Assessment For Region B Layer 3

3-layer Approach 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. 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 Layer- 2, we consider Benefit:Risk for each region. × DO NOT jump

In Layer-2 How do we explore regional heterogeneity of the treatment effect? Graphical presentations Forest plot, funnel plot, etc. Modeling approaches 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 China Japan Philippine 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 ST-segment elevation FDA Briefing Information, BRILINTA™ (ticagrelor), for the July 28, 2010 Meeting of the Cardiovascular and Renal Drugs Advisory Committee Treatment better

If we find regional heterogeneity A next question is “Are the observed regional differences Real or Not?” Possible answers (they are mixed in practice) Imbalance across regions in distributions of intrinsic ethnic factors who impact on the treatment effect 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 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. How much of the difference can be explained by covariates? Are there observations that cannot be explained, such as outliers? Idealized Schema Imbalance across regions in distributions of factors who impact on the treatment effect Treatment effect in Region A Treatment effect in Region B Outcome Variable Placebo Baseline distribution of Region A Baseline distribution of Region B Study drug Baseline Variable

How much of the difference can be explained by covariates? Systematic residual errors in a specific region still exist? Predicted values by the model Actual values Line of “Predicted” = “Actual” e.g., Predicted value = f (treatment group, baseline value, body weight) Residuals = unexplained by the model Covariates (influential factors)

An Illustrative Example Line of “Predicted” = “Actual” Residual plots by regions Predicted vs. Actual

An Illustrative Example Examination of residuals (gap between observed data and model)

Points to consider for partitioning the overall sample size into each region Consistency perspective: Minimize the chance for observing apparent differences across regions when the treatment effect is truly uniform across the regions. 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 multi-regional schizophrenia trials. Pharm Stat. 9(3): 217-29.

Baseline value of the primary endpoint Points to consider for partitioning the overall sample size into each region Distributions of known influential factors Total Sample Size Region A Weight Impact Region B Treatment Effect Region C Baseline value of the primary endpoint

Available CT or RWD data Points to consider for partitioning the overall sample size into each region 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 Japanese Data Weight 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 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)

An Illustrative Example Median Distribution of Weight 90 kg 80 kg Median of Weight 70 kg 60 kg 100 Japanese Sample Size

Summary Worldwide Collaborative Works! Layer 1 Layer 2 Layer 3 For Overall results (efficacy, safety) Worldwide Collaborative Works! Layer 2 Benefit:Risk Assessment For Japan Benefit:Risk Assessment For Region A Benefit:Risk Assessment For Region B Layer 3