Multiregional Trials Research Articles

An Approach to Determine Sample Size and to Allocate Sample Size for a Specific Region in a Multiregional Trial for Survival (Time-to-Event) Data under Accelerated Failure Time Model

For the time-to-event outcome, current methods for sample determination are based on the proportional hazard model. However, if the proportionality assumption fails to capture the relationship between the hazard time and covariates, the proportional hazard model is not suitable to analyze survival data. The accelerated failure time (AFT) model is an alternative method to deal with survival data. In this paper, we address the issue that the relationship between the hazard time and the treatment effect is satisfied with the AFT model to design a multiregional trial. The log-rank test is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is used to determine the total sample size for a multiregional trial, and the proposed criteria are used to rationalize partition sample size to each region.

Bayesian modeling and prediction of accrual in multi-regional clinical trials.

In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different initial accrual rates across regions/centers. Our numerical studies show that the proposed method improves accuracy and precision of accrual prediction compared to existing methods including the nonhomogeneous Poisson process model that does not model region-specific accrual.

Detecting qualitative interactions in clinical trials with binary responses.

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.

Designing Multiregional Trials Under the Discrete Random Effects Model

A discrete random effects model (Lan and Pinheiro, 2012) was proposed recently for multiregional clinical trials for continuous responses. This article elucidates further the application of this model to time-to-event and binary responses. We provide some guidelines on how to design multiregional trials and also show how the same model lends itself naturally to meta-analysis.

PMH3 - Points to Consider in Multiregional Trials Using PHQ-9: A Meta-Analysis on PHQ-9

PMH3 - Points to Consider in Multiregional Trials Using PHQ-9: A Meta-Analysis on PHQ-9

A Design For Survival Studies: A Multi-regional Trial With Unrecognized Heterogeneity

A bridging study defined by 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 delay 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. Recently, multi-regional trials have attracted much attention from sponsors as well as regulatory authorities. Current methods for sample determination are based on the assumption that true treatment effect is uniform across regions. However, unrecognized heterogeneity among patients as ethnic or genetic factor will effect patients’ survival. Using the simple log-rank test for analysis of treatment effect on survival in studies under heterogeneity may be severely underpowered. In this article, we address the issue that the treatment effects are different among regions to design a multi-regional trial. The optimal log-rank test is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is used to determine the total sample size for a multi-regional trial and the consistent trend and the proposed criteria are used to rationalize partition sample size to each region.

Comparison of Two Ethnic Populations Using Distribution Adjusted Mean

In similarly designed clinical trials conducted in different regions or a multiregional trial, we sometimes observe different mean responses between trials or regions due to differences in distributions of an ethnic factor. Uesaka et al. (2003) used adjusted means for covariate, the distribution adjusted mean, to deal with such a situation. This paper examines distributional properties of the distribution adjusted mean and shows its applicability to comparison of mean responses from clinical trials where the distribution of an ethnic factor is different between regions. A real example and simulation results are shown. The results of this paper enable us to evaluate the similarity of different responses in clinical trials from different ethnic populations.

A Statistical Issue About a Phase III Multi-regional Trial for the Survival Data under Accelerated Failure Time Model with Unrecognized Heterogeneity

For the time-to-event outcome, current methods for sample size determination are based on the proportional hazard model. However, if the proportionality assumption fails to capture the relationship between the hazard time and covariates, the proportional hazard model is not suitable to analyze survival data. The accelerated failure time model is an alternative method to deal with survival data. In this article, we address the issue that the relationship between the hazard time and the treatment effect is satisfied with the accelerated failure time model to design a multi-regional trial for a phase III clinical trial. The log-rank test is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is used to determine the total sample size for a multi-regional trial and the consistent trend is used to rationalize partition sample size to each region.

Approaches to Allocate Sample Size Rationally Into Individual Regions for a Multi-regional Trial Under Heterogeneous Effect Size

A bridging study defined by 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 delay 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. Recently, multi-regional trials have attracted much attention from sponsors as well as regulatory authorities. 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 Q & A for the ICH E5 guideline also comments the concept of a multi-regional 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 multi-regional trial. Kawai et al. (2008) developed an approach to rationalize partitioning the total sample size among the regions so that a high probability of observing a consistent trend under the assumptions of the positive treatment effect and uniform across regions in a confirmatory multi-regional trial. Ko et al. (2010) focused on a specific region and establish statistical criteria for consistency between the region of interest and overall results. The sample size calculation for a specific region was also provided. These methods were based on the assumption that true effect size is uniform across regions. In this article, we address the issue that the treatment effects are different among regions to design a multi-regional trial. The random effect model is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is also established and the consistent trend and the proposed criteria are used to rationalize partition sample size to each region.

Assessing Dose–Region Profile of Drug Efficacy: A Multiregional Trial Strategy

In this article, we try to address the optimal dosing issue using a multiregional trial. In this trial, in addition to the treatment group using a globally promising dose of study drug, we have another treatment group using a lower dose treatment with which the drug also shows substantial treatment effect in some of the regions, and both treatment groups share the same placebo (control) group. The incorporation of an additional low-dose treatment group in the multiregional trial can provide the following advantages: (i) The multiregional trial can establish the whole treatment effect profile over different regions as well as drug doses; (ii) the multiregional trial allows for sharing drug efficacy information across different regions; and (iii) the use of a common placebo (control) group for the high- and low-dose treatment groups in the multiregional trial results in logistical convenience. We also examine the regional sample size allocation in the proposed multiregional trial for ensuring a desired power in a local region, using the Bayesian approach we proposed previously for analyzing the multiregional trial data.

Design and Evaluation of Multiregional Trials with Heterogeneous Treatment Effect Across Regions

To speed up drug development to allow faster access to medicines for patients globally, conducting multiregional trials incorporating subjects from many countries around the world under the same protocol may be desired. Several statistical methods have been proposed for the design and evaluation of multiregional trials. However, in most of the recent approaches for sample size determination in multiregional trials, a common treatment effect of the primary endpoint across regions is usually assumed. In practice, it might be expected that there is a difference in treatment effect due to regional difference (e.g., ethnic difference). In this article, a random effect model for heterogeneous treatment effect across regions is proposed for the design and evaluation of multiregional trials. We also address consideration of the determination of the number of subjects in a specific region to establish the consistency of treatment effects between the specific region and the entire group.

Assessment of Regional Treatment Effect in a Multiregional Clinical Trial

The 11th question-and-answer document (Q&A) for ICH E5 (1998) was published in 2006. This Q&A describes points to consider for evaluating the possibility of bridging among regions by a multiregional trial. The primary objective of a multiregional bridging trial is to show the overall efficacy of a drug in all participating regions while also evaluating the possibility of applying the overall trial results to each region. To apply the overall results to a specific region, it suggested that the results in that region should be consistent with the overall results. The Japanese Ministry of Health, Labor, and Welfare (MHLW) published the “Basic Principles on Global Clinical Trials” guidance document (2007) and proposed two methods to support the bridging claims. Due to the limited sample sizes allocated to the region, the regular interaction test for treatment by region is not practical. On the other hand, the sample size requirement for the Japanese region as described in Uyama et al. (2005) and Uesaka (2009) is to satisfy an 80% or greater power for the Japanese region, conditioning on the effect of the overall global trial. Quan et al. (2010) further extended the results to trials with various endpoints. Ko, Tsou, Liu and Hsiao (2010) focused on a specific region and established statistical criteria for consistency between the region of interest and overall results. The proposed method was based on the assumption that true effect size is uniform across regions. In this article, we propose to analyze a completed multiregional trial for any specific regional effect by controlling the type I error rate adjusted for the regional sample size and the planned power of the global trial. Accordingly, in order to attain the approval for a specific region, we propose to determine the sample size requirement for the specific regions using the overall power planned and a regional acceptable type I error rate.

Exploratory Analyses of Efficacy Data From Schizophrenia Trials in Support of New Drug Applications Submitted to the US Food and Drug Administration

There has been concern about a high rate of placebo response and a decline in treatment effect over time in schizophrenia trials as well as the implications of increasing conduct of such trials outside North America. This report explores differences in efficacy data over an 18-year period from randomized placebo-controlled trials submitted in support of new drug applications (NDAs) for the treatment of schizophrenia and differences in results between trials conducted in North America and elsewhere. Clinical trial data that were submitted to the US Food and Drug Administration (FDA) as part of NDAs for the indication of schizophrenia between 1991 and 2009. Efficacy data were compiled from 32 clinical trials with 11,567 evaluable patients with schizophrenia. Data from completed, randomized, multicenter, double-blind, placebo-controlled, 4- to 8-week clinical trials in adult patients diagnosed with schizophrenia according to DSM-III or DSM-IV criteria were included. Baseline demographic and disease characteristics, including mean Positive and Negative Syndrome Scale (PANSS) total scores, were summarized and compared between North American and multiregional trials. Mean change from baseline to endpoint in PANSS total scores was utilized as the primary outcome of interest. We explored differences in treatment effect and success rate of these trials based on when and where the studies were conducted, sample size, trial duration, and baseline patient characteristics. Twenty-one of the 32 trials were conducted solely in North America, and 11 were carried out in multiple regions. Of those 11 multiregional trials, 2 were conducted exclusively in foreign countries. Although the observed responses (change from baseline) in placebo and drug-treated groups in multiregional trials tended to be larger than in North American trials, the treatment effects (drug-placebo difference) were -9 and -8 PANSS units for North American and multiregional trials, respectively. When time of trial conduct was taken into account, an increasing placebo response and a diminishing treatment effect over time were observed in North American trials from -10.8 PANSS units for the first period (1991-1998) to -6.0 PANSS units for the later period (1999-2008). The overall trial success rate over the almost 2 decades was 78%, declining slightly in trials conducted after 1999, the time period during which multiregional trials were first conducted (74% for 1999-2008 vs 85% for 1991-1998), despite increasing sample sizes in the later period. The mean baseline PANSS total score was in the range of 87-100 for most of these trials. Trials in patients with higher mean baseline PANSS total scores tended to show larger treatment effects than those in patients with lower scores. The mean body weight and body mass index (BMI) were higher in patients in North American trials and North America-predominant multiregional trials compared to those in foreign-predominant multiregional trials (mean body weights of 85 kg and 81 kg vs 72 kg, and BMIs of 29 and 27 vs 25, respectively). Treatment effects decreased as body weights increased, especially in North American trials. In foreign-predominant multiregional trials, there were higher proportions of women than in North American trials and North America-predominant multiregional trials (40% vs 22% and 27%, respectively) and a relatively larger proportion of Asians (21% vs 1% and 8%, respectively). A high and increasing placebo response and a declining treatment effect are of great concern in schizophrenia trials conducted in North America. In this era of global clinical trials, close attention is needed to the design and conduct of these trials.

Combined Estimation of Treatment Effects Under a Discrete Random Effects Model

Combining information from different groups of data, such as regions in a multi-regional study or trials in a meta-analysis, is an increasingly important problem in clinical drug development. This paper focuses on the combination of treatment effect estimates from independent sources of data (e.g., regions, trials) under a discrete, patient-level random effects model. The approach is motivated by multi-regional clinical studies, being also illustrated in the context of meta-analysis. Comparisons to traditional combination of information methods based on fixed effects (multi-regional trials) and study-level random effects (meta-analysis) are also discussed.

RFunctions for Sample Size and Probability Calculations for Assessing Consistency of Treatment Effects in Multi-Regional Clinical Trials

Multi-regional clinical trials have been widely used for efficient global new drug developments. Due to potential heterogeneity of patient populations, it is critical to evaluate consistency of treatment effects across different regions in a multi-regional trial in order to determine the applicability of the overall treatment effect to the patients in individual regions. Quan et al. (2010) proposed definitions for the assessments of consistency of treatment effects in multi-regional trials. To facilitate the application of their ideas to design multi-regional trials, in this paper, we provide the corresponding R functions for calculating the unconditional and conditional probabilities for demonstrating consistency in relationship with the overall/regional sample sizes and the anticipated treatment effects. Detailed step by step instructions and trial examples are also provided to illustrate the applications of these R functions.

Approaches to Japanese Dose Evaluation in Global Drug Development: Factors That Generate Different Dosages Between Japan and the United States

Drug development in Japan is shifting from a bridging strategy to a global strategy, and the number of multiregional trials in which Japan is included is increasing every year. The Japanese drug regulatory authority requires that data be collected in Japanese populations, and therefore dose-response studies of various drugs are frequently conducted in Japan. However, the current standard for adequate dose-finding processes may sometimes hinder the timely participation of Japan in these multiregional trials. We studied the development approaches and review patterns of 99 new molecular entities (NMEs) approved in 2003-2008 and have identified some common factors that result in differences in approved dosages in Japan as compared with other countries, such as dose-response study design, pharmacokinetics, and the timing of development. The findings of our research will serve as an initial information base on which to build an efficient global drug development strategy in Japan.

Bayesian Approaches in Medical Device Clinical Trials: A Discussion with Examples in the Regulatory Setting

Challenging statistical issues often arise in the design and analysis of clinical trials to assess safety and effectiveness of medical devices in the regulatory setting. The use of Bayesian methods in the design and analysis of medical device clinical trials has been increasing significantly in the past decade, not only due to the availability of prior information, but mainly due to the appealing nature of Bayesian clinical trial designs. The Center for Devices and Radiological Health at the Food and Drug Administration (FDA) has gained extensive experience with the use of Bayesian statistical methods and has identified some important issues that need further exploration. In this article, we discuss several topics relating to the use of Bayesian statistical methods in medical device trials, based on our experience and real applications. We illustrate the benefits and challenges of Bayesian approaches when incorporating prior information to evaluate the effectiveness and safety of a medical device. We further present an example of a Bayesian adaptive clinical trial and compare it to a traditional frequentist design. Finally, we discuss the use of Bayesian hierarchical models for multiregional trials and highlight the advantages of the Bayesian approach when specifying clinically relevant study hypotheses.

A consistency approach to evaluation of bridging studies and multi‐regional trials

Recently, global drug developments have attracted much attention from sponsors as well as regulatory authorities. The ICH E5 guideline defines a bridging study as a supplementary study conducted in the new region to provide pharmacodynamic or clinical data on efficacy, safety, dosage, and dose regimen to allow extrapolation of the foreign clinical data to the population of the new region. On the other hand, a multi-regional trial may incorporate subjects from many regions around the world under the same protocol so that after showing the overall efficacy of a drug in all global regions, we can simultaneously evaluate the possibility of applying the overall trial results to each region and consequently support drug registration in each region. In this paper, we develop a consistency approach for assessment of similarity between a bridging study conducted in a new region and studies conducted in the original region. A statistical criterion is also established to assess the consistency between the region of interest and overall results in a multi-regional trial. The method for sample size determination for the bridging study is also proposed. Numerical examples illustrate applications of the proposed approaches in different scenarios.

Multisite international collaborative clinical trials in mania

Multi-regional collaborative clinical trials include those conducted across heterogeneous areas of the world under common protocols. Such trials appear to be driven primarily to provide data required for regulatory approval or licensing of new drug products in a relatively rapid and presumably efficient and cost-effective manner. Commonly, they include underserved populations and areas where costs of trials are lower than in most developed countries. In addition, such studies can potentially make innovative treatments widely and rapidly available in vast, international markets. Other potential benefits to collaborating sites may include diffusion of knowledge and improvement of research skills, as well as improvement of treatment and a broader salutary impact on health services and perhaps on employment opportunities and economic growth (Demol +6; Weihrauch, 1997; Glickman et al. 2009; Gopal et al. 2005; Greco +6; Diniz, 2008; ICH Guideline, 2002; Smulevich et al. 2005; U.S. FDA, 1998). Successful conduct of international trials requires compliance with varying local and international laws, regulations and ethical requirements, and confronting a range of systems of review of ethical aspects of subject recruitment, compensation, consenting procedures, research protocols, and provision of aftercare – all which can add complexity. In addition, there is variance among regions, countries and cultures in levels of education, and in the nature of information, financial inducements, clinical care and aftercare provided to research subjects. Complexities arise also from culture-dependent conceptualizations of mental disorders, criteria for diagnosis, and efforts at validating, interpreting and scoring of symptom ratings designed to characterize changes during treatment, and methods for detecting adverse events. In the continuing quest to define core or universal features of psychiatric disorders, it is crucial to consider the anthropological and cultural context in which they develop and are modified (Karno +6; Jenkins, 1993; Lopez-Ibor, 2003; …

Considerations on Observing Consistent Results of Treatment Effects in Multiregional Trials

We evaluate the probability of observing consistent results and the relationships between the required sample size and the effect size for a confirmatory multiregional trial. Three methods are evaluated, including two methods (methods 1 and 2) already proposed by the Ministry of Health, Labor and Welfare in 2007 and a new method proposed to confirm the ratio of one region to the other regions of the true treatment effect in a multiregional trial. We discuss method 1 and a new method with a survival endpoint and method 2 with a normal endpoint using numeric equations and simulations. We show that the probability of observing consistent results is not related to the effect size and the conditional probability is slightly superior to the unconditional one in both method 1 and the new method. We conclude that the probability of observing consistent results with the new method decreases according to the increase of the number of regions in method 1 previously proposed. Nonetheless, the slight difference betwee...