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Establishing consistency across all regions in a multi-regional clinical trial
Abstract
In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and shorten approval time, the design of multi-regional clinical trials (MRCTs) incorporates subjects from many countries/regions around the world under the same protocol. After showing the overall efficacy of a drug in a global trial, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each region. However, most of the recent approaches developed for the design and evaluation of MRCTs focus on establishing criteria to examine whether the overall results from the MRCT can be applied to a specific region. In this paper, we use the consistency criterion of Method 1 from the Japanese Ministry of Health, Labour and Welfare (MHLW) guidance to assess whether the overall results from the MRCT can be applied to all regions. Sample size determination for the MRCT is also provided to take all the consistency criteria from each individual region into account. Numerical examples are given to illustrate applications of the proposed approach.
... Forty-four frequentist methods were identified 15,[34][35][36] of which 11 methods 41-51 synthesised data from source and target populations in a joint model, three methods 52-54 combined data across populations through a weighted test statistic and 30 methods 15,[34][35][36][48][49][50][51][55][56][57][58][59][60][61][62][63][64][65][66] proposed criteria to assess the consistency of estimates of key parameters in different populations. ...
... 0:5 although Chen et al. comment that this may be too conservative when several trial regions are included. This literature review found 15 further methods 35,48,49,51,[56][57][58][59][60][61][62][63] proposing consistency criteria similar to the PMDA method. For example, letÁ,Á Snj ? ...
... In Section 1 it was noted that there may be differences between age groups of children. Twentyfive methods 2,15,18,55,61,66 identified by this review can accommodate a heterogeneous target population because key parameters are taken to be parameters of (semi-)parametric models capable of adjusting for baseline demographics. Several methods proposing a joint model for data from the source and target populations assume only that data from different populations are correlated. ...
Objective:
When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes.
Methods:
Web of Science was used to identify papers proposing methods relevant for using data from a 'source population' to support inferences for a 'target population'. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes.
Results:
Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic.
Conclusions:
Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.
... Traditionally, the treatment effect was assumed to have a fixed positive value over all regions in an MRCT. Numerous studies with proposals for the design and evaluation of MRCTs under the assumption of a common treatment effect across regions have been reported in many literatures, such as an approach to rationalize partitioning the total sample size among the regions (Kawai et al. [1]), consistency criteria approach (Ko et al. [2]), statistical consideration from an Asian perspective (Tsou et al. [3]), similarity assessment using Bayesian most plausible prediction (Tsou et al. [4]), and a consistency approach across all participating regions (Tsou et al. [5]). ...
... Both papers are based on method 2 of the MHLW. Other articles have attempted to describe approaches to assess the consistency of treatment effects based on method 1 of the MHLW, such as Chen et al. [9], Quan et al. [10], Uesaka [15], Chen et al. [16], Ko et al. [2], Tsou et al. [3][4][5], and Quan et al. [17,18]. In this paper, we adopt the assumption that treatment effect follows a discrete distribution, patient-level random effects model (DREM) proposed by Lan and Pinheiro [11] to combine treatment effect estimates from independent regions. ...
In recent years, developing pharmaceutical products via multiregional clinical trials (MRCTs) has become standard. Traditionally, an MRCT would assume that a treatment effect is uniform across regions. However, heterogeneity among regions may have impact upon the evaluation of a medicine's effect. In this study, we consider a random effects model using discrete distribution (DREM) to account for heterogeneous treatment effects across regions for the design and evaluation of MRCTs. We derive an power function for a treatment that is beneficial under DREM and illustrate determination of the overall sample size in an MRCT. We use the concept of consistency based on Method 2 of the Japanese Ministry of Health, Labour, and Welfare's guidance to evaluate the probability for treatment benefit and consistency under DREM. We further derive an optimal sample size allocation over regions to maximize the power for consistency. Moreover, we provide three algorithms for deriving sample size at the desired level of power for benefit and consistency. In practice, regional treatment effects are unknown. Thus, we provide some guidelines on the design of MRCTs with consistency when the regional treatment effect are assumed to fall into a specified interval. Numerical examples are given to illustrate applications of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.
... Both methods are based on probabilities to demonstrate consistency between the entire population and individual regions, using a predefined level for the definition of "consistency. " The methods have been examined by many researchers (Kawai et al. 2008;Uesaka 2009;Chen et al. 2010;Quan, Zhao, et al. 2010;Tsou et al. 2012;Quan et al. 2014;Quan and Mao 2016;Liao, Yu, and Li 2018;Teng, Lin, and Zhang 2018); however, there are no pronounced views on the sample size allocation for the dose-finding study. Furthermore, to our knowledge, research on sample size calculation under the MCP-Mod approach (e.g., Pinheiro, Bornkamp, and Bretz 2006;Pinheiro and Bornkamp 2017), has not been extended to address regional allocation for Japan or any other region. ...
Simultaneous global drug development with multiregional studies is becoming a common strategy for increasing efficiency of the development process. In particular, multiregional dose-finding studies can be informative to identify inter-ethnic difference in dose-response relationships early in development. An application of MCP-Mod to multiregional studies is discussed in this paper. We consider sample size allocation to one specific region and provide three methods for demonstrating consistency in the dose-response relationship between the entire population and one specific region: two methods use contrast statistics to show consistency in dose-response signals, and the third method uses the maximum absolute difference between two dose-response curves to show consistency in the dose-response profiles. The proposed methods do not require studies to have sufficient power to detect a truly consistent dose-response relationship in a confirmatory manner, but rather to allow for quantitative design considerations that can ensure such a relationship is observed at the end of the study with acceptable probability. The three methods are illustrated through an anti-anxiety drug example, resulting in a recommended proportion of 20% for subjects in one specific region. The illustration indicates the recommended proportion could vary depending on the total study sample size.
... So far, studies on statistical issues of MRCT have been roughly made in the following direction. The first direction is to assess the consistency of the treatment effect across regions (Chen et al. , 2011Quan, Li, et al. 2010;Liu, Chow, and Hsiao 2012;Tsou et al. 2012;Quan et al. 2013Quan et al. , 2014Liu et al. 2016;Diao et al. 2017;Teng, Lin, and Zhang 2018). The second direction is to allocate a sufficient number of patients to each region (Kawai et al. 2008;Uesaka 2009;Ikeda and Bretz 2010;Ko et al. 2010;Quan, Zhao, et al. 2010;Quan et al. 2013Quan et al. , 2014Diao et al. 2017). ...
Data observed in multi-regional clinical trials are structurally hierarchical in the sense that the patient population consists of several regions and patients are nested within their own regions. In order to reflect such hierarchical structure, in this paper, we propose two-level hierarchical linear models in which the level-1 model is based on patient-level data such as treatment indicator and age, and the level-2 model is based on region-level data such as medical practices. The fixed effect model and the continuous random effect model are shown to be special cases of hierarchical linear models. We conducted simulation studies to investigate the empirical type I error rates of three methods for testing the overall treatment effect. The performance of the testing method with sample ratios as weights and the empirical Bayes estimator for between-region variability is better than that of the other two testing methods.
... It is important to allocate a sufficient number of patients to each region in a MRCT in order to assess the consistency of the treatment effect across regions, and several studies have investigated this issue [19][20][21][22][23][24][25][26][27][28][29][30][31]. In this paper, we will discuss what has been termed Method 1, which was originally proposed by the Japanese government. ...
... Quan et al. [4] calculated the sample size required for Japan in an MRCT with normal, binary, and survival endpoints based on Method 1. Kawai et al. [5] proposed an approach, based on Method 2, to allocate the total sample size to the regions so that a high probability of observing a consistent trend under the assumed treatment effect across regions can be obtained. In addition, consistency criteria different from those of the Japan guidance have been established, such as those by Tsou et al. [6], Uesaka [7], Ko et al. [8], and Tsou et al. [9]. On the other hand, Chen et al. [10] and Huang et al. [11] considered ethnic differences and proposed methods that apply different treatment effects across regions to the design and evaluation of MRCTs. ...
Recently, multi-regional clinical trials (MRCTs), which incorporate subjects from many countries/regions around the world under the same protocol, have been widely conducted by many global pharmaceutical companies. The objective of such trials is to accelerate the development process for a drug and shorten the drug’s approval time in key markets. Several statistical methods have been purposed for the design and evaluation of MRCTs, as well as for assessing the consistency of treatment effects across all regions with one primary endpoint. However, in some therapeutic areas (e.g., Alzheimer’s disease), the clinical efficacy of a new treatment may be characterized by a set of possibly correlated endpoints, known as multiple co-primary endpoints. In this paper, we focus on a specific region and establish three statistical criteria for evaluating consistency between the specific region and overall results in MRCTs with multiple co-primary endpoints. More specifically, two of those criteria are used to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other criterion is used to assess the consistency of the treatment effect of the specific region achieving a pre-specified threshold. The sample size required for the region of interest can also be evaluated based on these three criteria. © 2017 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
... Quan et al. (2012) proposed the empirical shrinkage estimation approach based on the random effect model to assess the consistency of treatment effect across regions, which presumably could help obtain better consistency compared to the fixed effect model. Tsou et al. (2012) also proposed another consistency criterion to examine whether the overall results can be applied to all participating regions, and sample size requirement was also discussed. Some other consistency requirement and sample size planning for MRCT was also discussed in Chen et al. (2010a;2010b), Hung et al. (2010), Ikeda and Bretz (2010), Lan and Pinheiro (2012), Lan et al. (2014) and Luo et al. (2010). ...
To speed up the process of bringing a new drug to the market, more and more clinical trials are being conducted simultaneously in multiple regions. After demonstrating the overall drug’s efficacy across regions, the regulatory and drug sponsor may also want to assess the drug’s effect in specific region(s). Most of the recent approaches imposed a uniform criterion to assess the consistency of treatment effects between the interested region(s) and the entire study population regardless the number of regions in a Multi-Regional Clinical Trials (MRCT). As a result, the needed sample size to achieve the desired probability of satisfying the regional requirement could be huge and implausible for the trial sponsors to implement.
In this paper, we propose a unified additional requirement for regional approval by differing the parameters in the additional requirement depending on the number of planned regions. In particular, the values of the parameters are determined by reasonable sample size increase with the desired probability satisfying the additional requirement. Considering the practicality of the global trial or sample size increase, we recommend specific values of the parameters for different number of planned regions. We also introduce the assurance probability curve to evaluate the performance of different regional requirements.
... Quan et al. (2012) proposed the empirical shrinkage estimation approach based on the random effect model to assess the consistency of treatment effect across regions, which presumably could help obtain better consistency compared to the fixed effect model. Tsou et al. (2012) also proposed another consistency criterion to examine whether the overall results can be applied to all participating regions, sample size requirement were also discussed. ...
In recent years, there is an increasing trend to conduct multi-regional clinical trials (MRCT) for drug development in Pharmaceuticals industry. A carefully designed MRCT could be used in supporting the new drug’s approval in different regions simultaneously. The primary objective of an MRCT is to investigate the drug’s overall efficacy across regions while also assessing the drug’s performance in some specific regions. In order to claim the study drug’s efficacy and get drug approval in some specific region(s), the local regulatory authority may require the sponsors to provide evidence of consistency in the treatment effect between the overall patient population and the local region. Usually, the regional specific consistency requirement needs to be pre-specified before the study conduct and the consistency in treatment effect between the region(s) of interest and overall population will be evaluated at the final analysis. In this paper, we evaluate the consistency requirements in multi-regional clinical trials for different endpoints, i.e., continuous, binary and survival endpoints. We also compare the different consistency requirements of the same endpoint/measurement if multiple consistency requirements are enforced and our recommendations for each endpoint/measurement will be made based on the comprehensive consideration.
Multiregional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market; however, small regional sample sizes can lead to poor estimation quality of region-specific effects when using current statistical methods. With the publication of the International Conference for Harmonisation E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that improve the quality of region-specific inference. In this article, we develop a novel methodology for estimating region-specific and global treatment effects for MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions, and this metric can be used by regulatory authorities for drug approval. We show through simulations that the proposed modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model.
Due to the globalization of drug development, multi-regional clinical trials (MRCTs) have been increasingly adopted in clinical evaluations. In MRCTs, the primary objective is to demonstrate the efficacy of new drugs in all participating regions, but heterogeneity of various relevant factors across these regions can cause inconsistency of treatment effects. In particular, outlying regions with extreme profiles can influence the overall conclusions of these studies. In this article, we propose quantitative methods to detect these outlying regions and to assess their influences in MRCTs. The approaches are as follows: (1) a method using a dfbeta-like measure, a studentized residual obtained by a leave-one-out cross-validation (LOOCV) scheme; (2) a model-based significance testing method using a mean-shifted model; (3) a method using a relative change measure for the variance estimate of the overall effect estimator; and (4) a method using a relative change measure for the heterogeneity variance estimate in a random-effects model. Parametric bootstrap schemes are proposed to accurately assess the statistical significance and variabilities of the aforementioned influence diagnostic tools. We illustrate the effectiveness of these proposed methods via applications to two MRCTs, the RECORD and RENAAL studies.
Multi-regional clinical trials have a hierarchical data structure because several regions form a patient population and individual patients are nested within their own regions. Data are obtained from two different levels: regions and patients. In order to incorporate such a hierarchical structure, hierarchical linear models were proposed for the response variables following a normal distribution by Kim and Kang (Statistics in Biopharmaceutical Research 2020). In this paper we extend the hierarchical linear models to propose hierarchical generalized linear models so that the response variables can follow the exponential family. We describe the details of the model when the response variable follows the Bernoulli distribution and the Poisson distribution. Simulation studies show that the empirical powers of the hierarchical generalized linear model are greater than random effects model when region-level covariates are incorporated.
The main objective of a confirmatory multiregional clinical trial (MRCT) is to demonstrate the overall efficacy of test drugs in all participating regions as well as to evaluate the possibility of extrapolating the overall results to each region. With the emergence of the demands of biosimilar drugs development, some guidelines recommended using equivalence design to demonstrate the comparability of efficacy between biosimilar and reference drugs. Previous discussions about assessing regional consistency in MRCT are mainly focused on superiority or non‐inferiority designs, while the extensions to equivalence designs were limited. In this work, we proposed a flexible regional consistency criterion for the MRCT with equivalence design. Based on this criterion, sample size determination and sample allocation were also discussed.
In recent years, multiregional clinical trial (MRCT) has become a preferred strategy to develop new medicines. Implementing the same protocol to include subjects from many geographical regions around the world, MRCTs could speed up the patient enrollment, thus resulted in a quicker drug development and obtain faster approval of the drug globally. At the same time, the MRCT strategy is expected to maintain the sample size at the similar level, i.e., without significantly driving up the cost and slowing down the speed of the development.
The one of the principles described in ICH E9 is that only results obtained from pre-specified statistical methods in a protocol are regarded as confirmatory evidence. However, in multi-regional clinical trials, even when results obtained from pre-specified statistical methods in protocol are significant, it does not guarantee that the test treatment is approved by regional regulatory agencies. In other words, there is no so-called global approval, and each regional regulatory agency makes its own decision in the face of the same set of data from a multi-regional clinical trial. Under this situation, there are two natural methods a regional regulatory agency can use to estimate the treatment effect in a particular region. The first method is to use the overall treatment estimate, which is to extrapolate the overall result to the region of interest. The second method is to use regional treatment estimate. If the treatment effect is completely identical across all regions, it is obvious that the overall treatment estimator is more efficient than the regional treatment estimator. However, it is not possible to confirm statistically that the treatment effect is completely identical in all regions. Furthermore, some magnitude of regional differences within the range of clinical relevance may naturally exist for various reasons due to, for instance, intrinsic and extrinsic factors. Nevertheless, if the magnitude of regional differences is relatively small, a conventional method to estimate the treatment effect in the region of interest is to extrapolate the overall result to that region. The purpose of this paper is to investigate the effects produced by this type of extrapolation via estimations, followed by hypothesis testing of the treatment effect in the region of interest. This paper is written from the viewpoint of regional regulatory agencies. © 2018 The Korean Statistical Society, and Korean International Statistical Society.
In recent years, there is an increasing trend to conduct multi-regional clinical trials (MRCT) for drug development in Pharmaceuticals industry. A carefully designed MRCT could be used in supporting the new drug's approval in different regions simultaneously. The primary objective of an MRCT is to investigate the drug's overall efficacy across regions while also assessing the drug's performance in some specific regions. In order to claim the study drug's efficacy and get drug approval in some specific region(s), the local regulatory authority may require the sponsors to provide evidence of consistency in the treatment effect between the overall patient population and the local region. Usually, the regional specific consistency requirement needs to be pre-specified before the study conduct and the consistency in treatment effect between the region(s) of interest and overall population will be evaluated at the final analysis. In this paper, we evaluate the consistency requirements in multi-regional clinical trials for different endpoints, i.e., continuous, binary and survival endpoints. We also compare the different consistency requirements of the same endpoint/measurement if multiple consistency requirements are enforced and our recommendations for each endpoint/measurement will be made based on the comprehensive consideration.
The primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP). However, the operating characteristics of this statistical framework were not well considered. Therefore, a nested group sequential program for regional efficacy evaluation is proposed in this paper. It is an extension of Huang’s SGDDP framework and allows interim analysis after MRCT and in the course of local clinical trial (LCT) phase. It is able to well control the familywise type I error in the program level and enhances the flexibility of the program. In LCT sample size estimation, we introduce virtual trial which is transformed from the original program by using discounting factor and an iteration method is employed to calculate the sample size and stopping boundaries of interim analyses. The proposed sample size estimation method is validated in the simulations and the effect of varied weight, effect size of TE population and design setting is explored. Examples with normal endpoint, binary endpoint and survival endpoint are shown to illustrate the application of the proposed nested group sequential program.
The traditionally uniform treatment effect assumption may be inappropriate in an multiregional clinical trial (MRCT) because of the impact on the drug effect due to regional differences. Lan and and Pinheiro (2012) proposed a discrete random effects model (DREM) to account the treatment effects heterogeneity among regions. However, the benefit of the overall drug effect and the consistency of the treatment effect in each region are two major issues in MRCTs. In this article, the power of benefit is derived under DREM and the overall sample size determination in an MRCT. Comparison of DREM and traditional continuous random effects model (CREM) is also illustrated here. In order to assess the treatment benefit and consistency simultaneously under DREM, we consider the concept of the Method 2 in “Basic Principles on Global Clinical Trials” guidance to construct the probability function of benefit and consistency. We also optimize the sample size allocation to reach maximum power for the benefit and consistency.
Current statistical design and analysis of multiregional clinical trials for medical devices generally follows a paradigm where the treatment effect of interest is assumed consistent among US and OUS regions. In this paper, we discuss the situations where the treatment effect might vary among US and OUS regions, and propose a two-component Bayesian approach for targeted decision making. In this approach, anticipated treatment difference among US and OUS regions is formally taken into account by design, hopefully leading to increased transparency and predictability of targeted decision making.
The two key components of the pharmacology of a drug—dose–concentration (pharmacokinetic) and/or concentration–response (pharmacodynamic) relationships—are often influenced by genetic variations. These account for a substantial fraction of variability in dose–response or drug response, not only between individuals, but also between different ethnic groups. The approval of ‘BiDil’ for the treatment of cardiac failure in self-identified black patients is a spectacular example of inter-ethnic differences in drug response and regulatory awareness of ethnicity of the study population. Drug development programs are increasingly undertaken globally to reduce costs, shorten timeframes, and address issues concerning global prescribing. Regulatory authorities have responded to this globalization of drug development by promulgating guidelines that recommend sponsors of new drugs to explore the role of genetic variations, and potential differences in drug response, between different ethnic populations. They may refuse to accept an application, or require bridging studies, when such differences are anticipated but not adequately addressed. These bridging studies may include (i) pharmacokinetic studies, (ii) pharmacodynamic studies, (iii) dose–response studies, and/or (iv) in extreme cases, pivotal phase III studies in order to extrapolate efficacy and/or safety data from one population to another.
This study considers the detection of treatment-by-subset interactions in a stratified, randomised clinical trial with a binary-response variable. The focus lies on the detection of qualitative interactions. In addition, the presented method is useful more generally, as it can assess the inconsistency of the treatment effects among strata by using an a priori-defined inconsistency margin. The methodology presented is based on the construction of ratios of treatment effects. In addition to multiplicity-adjusted p-values, simultaneous confidence intervals are recommended to use in detecting the source and the amount of a potential qualitative interaction. The proposed method is demonstrated on a multi-regional trial using the open-source statistical software R. Copyright © 2014 John Wiley & Sons, Ltd.
To accelerate the drug development process and shorten approval time, the design of multiregional clinical trials (MRCTs) incorporates subjects from many countries/regions around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. In this paper, we focus on a specific region and establish a statistical criterion to assess the consistency between the specific region and overall results in an MRCT. More specifically, we treat each region in an MRCT as an independent clinical trial, and each perhaps has different treatment effect. We then construct the empirical prior information for the treatment effect for the specific region on the basis of all of the observed data from other regions. We will conclude similarity between the specific region and all regions if the posterior probability of deriving a positive treatment effect in the specific region is large, say 80%. Numerical examples illustrate applications of the proposed approach in different scenarios. Copyright © 2013 John Wiley & Sons, Ltd.
Multiregional clinical trials (MRCTs) present great opportunities but also challenges to the trial community. To address the challenges and fully realize the opportunities, a PhRMA MRCT Cross-Functional Key Issue Team (KIT) was formed in 2008. One of the work streams within the KIT particularly focuses on the assessment of consistency of treatment effects across regions. As the main objective of this work stream, this research explores a number of definitions for consistency assessments. We address the issues primarily for superiority trials with continuous endpoints, then extend briefly to noninferiority trials, random effect models, binary endpoints, and survival endpoints. Computations and simulations are used to study the properties of the proposed definitions, particularly the power for showing consistency. To illustrate applications of the methods, we use a trial example with a continuous endpoint. We discuss considerations for trial design as well as for data analysis. The consistency assessment relies heavily on the definition of regions and the number of regions. We recommend working with health authorities to define region in a manner that is sensible from a practical interpretation standpoint and also makes region consistency assessment a feasible undertaking.
The desire to make valuable medicines available to patients globally at approximately the same time has led many pharmaceutical sponsors to consider conducting multiregional trials. In this article, we propose an approach to rationalize partitioning the total sample size among the constituent regions in a confirmatory multiregional trial. Our approach is to find the minimal sample size for the smallest region so that there is a high probability of observing a consistent trend in the estimated treatment effect across regions if the treatment effect is positive and uniform across regions. We investigate this probability in two ways, namely, unconditional and conditional on the overall treatment effect being statistically significant. In the case of three regions, the proportion of patients from the smallest region could be as low as 15% to have an 80% probability that the observed treatment effects are consistent across the three regions, conditional on concluding a statistically significant overall treatment effect.
Clinical trial strategy, particularly in developing pharmaceutical products, has recently expanded to a global level in the sense that multiple geographical regions participate in the trial simultaneously under the same study protocol. The possible benefits of this strategy are obvious, at least from the cost and efficiency considerations. The challenges with this strategy are many, ranging from trial or data quality assurance to statistical methods for design and analysis of such trials. In many regulatory submissions, the presence of regional differences in the estimated treatment effect, whether they are different only in magnitude or in direction, often presents great difficulty in interpretation of the global trial results, particularly for the acceptability by the local regulatory authorities. This article presents a number of useful statistical analysis tools for exploration of regional differences and a method that may be worth consideration in designing a multi-regional clinical trial.
In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and to shorten approval time, the design of multi-regional trials incorporates subjects from many countries around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. Recently, the trend for simultaneous clinical development in Asian countries being undertaken simultaneously with clinical trials conducted in Europe and the United States has been rapidly rising. In this paper, proposals of statistical consideration to multi-regional trials are provided. More specifically, three aspects are addressed: the definition of the 'Asian region,' the consistency criterion between the 'Asian region' and the overall regions, and the sample size determination for the multi-regional trial.
Recently, geotherapeutics have attracted much attention from sponsors as well as regulatory authorities. A bridging study defined by the International Conference on Harmonisation (ICH) E5 is usually conducted in the new region after the test product has been approved for commercial marketing in the original region due to its proven efficacy and safety. However, extensive duplication of clinical evaluation in the new region not only requires valuable development resources but also delays availability of the test product to the needed patients in the new regions. To shorten the drug lag or the time lag for approval, simultaneous drug development, submission, and approval in the world may be desirable. On September 28, 2007, the Ministry of Health, Labour and Welfare (MHLW) in Japan published the "Basic Principles on Global Clinical Trials" guidance related to the planning and implementation of global clinical studies. The 11th question and answer for the ICH E5 guideline also discuss the concept of a multiregional trial. Both guidelines have established a framework on how to demonstrate the efficacy of a drug in all participating regions while also evaluating the possibility of applying the overall trial results to each region by conducting a multiregional trial. In this paper, we focus on a specific region and establish statistical criteria for consistency between the region of interest and overall results. More specifically, four criteria are considered. Two criteria are to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other two criteria are to assess the consistency of the treatment effect of the specific region with other regions or the regions overall. Sample size required for the region of interest can also be evaluated based on these four criteria.
The objective of a multiregional bridging trial is to show the efficacy of a drug in various global regions, and at the same time to evaluate the possibility of applying the overall trial results to each region. However, to apply overall results to a specific region, the result in that region should be consistent with either the overall results or the results of other regions. This article discusses methods of sample size allocation to regions by introducing statistical criteria for consistency between regional and overall results. Specifically, three rules of sample size allocation are discussed: (1) allocating equal size to all regions, (2) minimizing total sample size, and (3) minimizing the sample size of a specific region. Some total and regional sample sizes calculated under each allocation rule are illustrated.
Since the publication of the International Conference on Harmonization E5 guideline, new drug approvals in Japan based on the bridging strategy have been increasing. To further streamline and expedite new drug development in Japan, the Ministry of Health, Labour and Welfare, the Japanese regulatory authority, recently issued the 'Basic Principles on Global Clinical Trials' guidance to promote Japan's participation in multi-regional trials. The guidance, in a Q&A format, provides two methods as examples for recommending the number of Japanese patients in a multi-regional trial. Method 1 in the guidance is the focus of this paper. We derive formulas for the sample size calculations for normal, binary and survival endpoints. Computations and simulation results are provided to compare different approaches. Trial examples are used to illustrate the applications of the approaches.
The response to many drugs in common use varies greatly among patients. After the intake of identical doses of a given agent, some patients may have clinically significant adverse effects, whereas others may have no therapeutic response. Some of this diversity in rates of response can be ascribed to differences in the rate of drug metabolism, particularly by the cytochrome P-450 superfamily of enzymes. Ten isoforms of cytochrome P-450 are responsible for the oxidative metabolism of most drugs, each having selective yet overlapping substrate specificity. Variability among patients in the activity of these enzymes reflects a complex interaction between environmental . . .
Labour and welfare of Japan (MHLW) Basic Prin-ciples on Global Clinical Trials
- Ministry
- Health
Ministry of Health. Labour and welfare of Japan (MHLW). Basic Prin-ciples on Global Clinical Trials 2007. http://www.pmda.go.jp/english/ service/pdf/notifications/0928010_e.pdf (Access on Apr. 10, 2012).
Labour and welfare of Japan (MHLW)
- Ministry of Health
Tripartite guidance E5 ethnic factors in the acceptability of foreign data
- ICH, International Conference on Harmonisation