Consideration of regional difference in design and analysis of multi-regional trials

Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates

For non-inferiority/superiority and equivalence tests of two Poisson rates, the determination of the required number of sample sizes has been studied but the studies for the number of events to be observed are very limited. To fill the gap, the present study first is aimed toward determining the number of events to be observed for testing non-inferiority/superiority and equivalence of two Poisson rates, respectively. Also, considering the cost for each event, the second purpose is to apply an exhaustive search to find the unequal but optimal allocation of events for each group such that the budget is minimal for a user-specified power level, or the statistical power is maximal for a user-specified budget. Four R Shiny apps were developed to obtain the number of events needed for each group. A simulation study showed the proposed approach to be valid in terms of Type I error and statistical power. A comparison of the proposed approach with extant methods from various disciplines was performed, and an illustrative example of comparing the adverse reactions to the COVID-19 vaccines was demonstrated. By applying the proposed approach, researchers also can estimate the most economical number of subjects or time intervals after determining the number of events.

Random intercept hierarchical linear model for multi-regional clinical trials

In multi-regional clinical trials, hierarchical linear models have been actively studied because they can reflect that patients in the same region share common intrinsic and extrinsic factors. In this paper, we investigate the statistical properties of the hierarchical linear model including a random effect in the intercept. The big advantage of the random intercept hierarchical linear model is that it can control the type I error rates of testing the overall treatment effect when there are no or clinically negligible regional differences in the treatment effect. Moreover, we compare the pros and cons with the hierarchical linear model in which the random effect is included in the slope. For the two hierarchical linear models, the model selection criteria are determined according to the magnitude of the difference in treatment effect across the regions, and we provide the criteria through simulation studies.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with ordinal responses

Global clinical trials involving multiple regions are common in current drug development processes. Determining the regional treatment effects of a new therapy over an existing therapy is important to both the sponsors and the regulatory agencies in the regions. Existing methods are mainly for continuous primary endpoints and use subjectively specified models, which may deviate from the true model. Here, we consider trials that have ordinal responses as the primary endpoint. This article extends the recently developed robust semiparametric ordinal regression model to estimate regional treatment effects, in which the regression coefficients and regional effects are modeled parametrically for ease of interpretation, and the regression link function is specified nonparametrically for robustness. The model parameters are estimated by semiparametric maximum likelihood estimation, and the null hypothesis of no regional effect is tested by the Wald test. Simulation studies are conducted to evaluate the performance of the proposed method and compare it with the commonly used parametric model. The results of the former show an improved overall performance over the latter. In particular, the model yields much higher precision in estimation and prediction than the fixed-link model. This result is especially appealing since our interest is to estimate the treatment effect more efficiently and the estimand is of particular interest in multiregional clinical trials. We then apply the method by analyzing real multiregional clinical trials with ordinal responses as their primary endpoint.

Use of likelihood estimates for variances for the design and evaluation of multiregional clinical trials with heterogeneous variances

Globalized drug development studies, such as multiregional clinical trials (MRCTs), have attracted much attention due to their ability to expedite drug development and shorten the time lag of drug release. While observing the overall effect of a new drug, the region-specific effects to support drug registration in constituent regions can also be evaluated. Several challenges arise in conducting MRCTs, such as the heterogeneity in the variability of the primary endpoint across regions. However, most of the existing statistical methods assume a common variability, which may not be valid in practice due to differences across regions (eg, diversities in ethnicity or disparities in medical culture/practice). We present a statistical method for the design and evaluation of MRCTs to consider the heterogeneous variability across regions. We assessed the overall sample size requirement and addressed the region-specific sample size determination to establish the consistency of treatment effects between the specific region and the entire group. We demonstrate the proposed approach with numerical examples.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with semiparametric logistic model

In multiregional randomized clinical trials (MRCTs), determining the regional treatment effect of a new treatment over an existing one is important to both the sponsor and related regulatory agencies. Also of particular interest is to test the null hypothesis that the treatment benefit is the same among all the regions. Existing methods are mainly for continuous endpoint and use parametric models, which are not robust. MRCTs are known for facing increased variation and heterogeneity and a robust model for its design and analysis would be desirable. We consider clinical trials with a binary primary endpoint and propose a robust semiparametric logistic model which has a known parametric and an unknown nonparametric component. The parametric component represents our prior knowledge about the model, and the nonparametric part reflects uncertainty. Compared to the classic logistic model for this problem, the proposed model has the following advantages: robust to model assumption, more flexible and accurate to model the relationship between the response and covariates, and possibly more accurate parameter estimates. The model parameters are estimated by profile maximum likelihood approach, and the null hypothesis of regional treatment difference being the same is tested by the profile likelihood ratio statistic. Asymptotic properties of the estimates are derived. Simulation studies are conducted to evaluate the performance of the proposed model, which demonstrated clear advantages over the classic logistic model. The method is then applied to analyzing a real MRCT.

Bayesian multiregional clinical trials using model averaging

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.

A Cross-sectional literature survey showed the reporting quality of multicenter randomized controlled trials should be improved

OBJECTIVE

To assess the reporting quality of randomized controlled trials (RCTs) with multicenter design, particularly whether necessary information related to multicenter characteristics was adequately reported.

STUDY DESIGN AND SETTING

Through a search of 4 international electronic databases, we identified multicenter RCTs published in English from 1975 to 2019. Reporting quality was assessed by the CONSORT (Consolidated Standards of Reporting Trials) checklist (37 items) and by a self-designed multicenter-specific checklist (27 items covering multicenter design, implement and analysis). The scores of trials published in three time periods (1975-1995; 1996-2009; and 2010-2019) were also compared.

RESULTS

A total of 2,844 multicenter RCTs were included. For the CONSORT checklist, the mean (standard deviation) reporting score was 24.1 (5.5), 12 items were assessed as excellent (>90%), 12 items as good (50%-90%), and 13 items as poor (<50%). For the multicenter checklist, the reporting score was 3.9 (2.2), only 3 items were excellent or good, and the remaining 24 items were poor. Time period comparison showed that reporting quality improved over time, especially after the CONSORT 2010 issued.

CONCLUSION

Although CONSORT appears to have enhanced the reporting quality of multicenter RCTs, further improvement is needed. A "CONSORT extension for multicenter trials" should be developed.

Use of interval estimations in design and evaluation of multiregional clinical trials with continuous outcomes

Multiregional clinical trials have been accepted in recent years as a useful means of accelerating the development of new drugs and abridging their approval time. The statistical properties of multiregional clinical trials are being widely discussed. In practice, variance of a continuous response may be different from region to region, but it leads to the assessment of the efficacy response falling into a Behrens–Fisher problem—there is no exact testing or interval estimator for mean difference with unequal variances. As a solution, this study applies interval estimations of the efficacy response based on Howe’s, Cochran–Cox’s, and Satterthwaite’s approximations, which have been shown to have well-controlled type I error rates. However, the traditional sample size determination cannot be applied to the interval estimators. The sample size determination to achieve a desired power based on these interval estimators is then presented. Moreover, the consistency criteria suggested by the Japanese Ministry of Health, Labour and Welfare guidance to decide whether the overall results from the multiregional clinical trial obtained via the proposed interval estimation were also applied. A real example is used to illustrate the proposed method. The results of simulation studies indicate that the proposed method can correctly determine the required sample size and evaluate the assurance probability of the consistency criteria.

Example-based illustrations of design, conduct, analysis and result interpretation of multi-regional clinical trials

Extensive research has been conducted in the Multi-Regional Clinical Trial (MRCT) area. To effectively apply an appropriate approach to a MRCT, we need to synthesize and understand the features of different approaches. In this paper, examples are used to illustrate considerations regarding design, conduct, analysis and interpretation of result of MRCTs. We start with a brief discussion of region definitions and the scenarios where different regions have differing requirements for a MRCT. We then compare different designs and models as well as the corresponding interpretation of the results. We highlight the importance of paying special attention to trial monitoring and conduct to prevent potential issues associated with the final trial results. Besides evaluating the overall treatment effect for the entire MRCT, we also consider other key analyses including quantification of regional treatment effects within a MRCT, and assessment of consistency of these regional treatment effects.

A nested group sequential framework for regional evaluation in global drug development program

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 family-wise 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 end point, binary end point, and survival end point are shown to illustrate the application of the proposed nested group sequential program.

Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects

Noninferiority multiregional clinical trials (MRCTs) have recently received increasing attention in drug development. While a major goal in an MRCT is to estimate the global treatment effect, it is also important to assess the consistency of treatment effects across multiple regions. In this paper, we propose an intuitive definition of consistency of noninferior treatment effects across regions under the random-effects modeling framework. Specifically, we quantify the consistency of treatment effects by the percentage of regions that meet a predefined treatment margin. This new approach enables us to achieve both goals in one modeling framework. We propose to use a signed likelihood ratio test for testing the global treatment effect and the consistency of noninferior treatment effects. In addition, we provide guidelines for the allocation rule to achieve optimal power for testing consistency among multiple regions. Extensive simulation studies are conducted to examine the performance of the proposed methodology. An application to a real data example is provided.

Unified additional requirement in consideration of regional approval for multiregional clinical trials

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 of the number of regions in multiregional 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 a 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 a different number of planned regions. We also introduce the assurance probability curve to evaluate the performance of different regional requirements.

Inconsistency and drop-minimum data analysis

Even though consistency is an important issue in multi-regional clinical trials and inconsistency is often anticipated, solutions for handling inconsistency are rare. If a region's treatment effects are inconsistent with that of the other regions, pooling all the regions to estimate the overall treatment effect may not be reasonable. Unlike the multiple center clinical trials conducted in the USA and Europe, in multi-regional clinical trials, different regional regulatory agencies may have their own ways to interpret data and approve new drugs. It is therefore practical to consider the case in which the data from the region with the minimal observed treatment effect is excluded from the analysis in order to attain the regulatory approval of the study drug. Under such cases, what is the appropriate statistical approach for the remaining regions? We provide a solution first formulated within the fixed effects framework and then extend it to discrete random effects models. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison of Treatment Effects Between US and Non-US Study Sites in Multiregional Alzheimer Disease Clinical Trials

BACKGROUND

Conducting clinical trials across multiple regions of the world has become common practice. A multiregional clinical trial (MRCT) presents opportunities as well as challenges. However, regional differences of treatment effects appear in many MRCTs, which make the interpretation of clinical trial results difficult and presents challenges for clinical trial design. Alzheimer disease (AD) is a progressive neurodegenerative disorder that affects approximately 5 million people in the United States and is the sixth leading cause of death in the country. In 2014, AD cost the United States $214 billion, and the cost is expected to rise to $1.2 trillion by 2050.

METHODS

In this article, we utilize data from New Drug Applications (NDAs) that have been approved for the treatment of AD to study whether there are differences in treatment effect between US and non-US study sites. Using an analysis of covariance (ANCOVA) model and forest plot, we analyze the treatment difference by region (US and non-US) from 3 separate perspectives: by region for each trial, by region for each endpoint, and by region and trial for each endpoint.

RESULTS

Overall, the analyses indicate that treatment effects in clinical trials for AD are generally in the expected direction in both US and non-US sites. There was no clear evidence of heterogeneity in treatment effects between US and non-US sites.

CONCLUSIONS

It appears that there is no clear evidence to suggest that MRCTs should not be used to study AD.

Multiplicity in confirmatory clinical trials: a case study with discussion from a JSM panel

An invited panel session was conducted in the 2012 Joint Statistical Meetings, San Diego, California, USA, to stimulate the discussion on multiplicity issues in confirmatory clinical trials for drug development. A total of 11 expert panel members were invited and 9 participated. Prior to the session, a case study was previously provided to the panel members to facilitate the discussion, focusing on the key components of the study design and multiplicity. The Phase 3 development program for this new experimental treatment was based on a single randomized controlled trial alone. Each panelist was asked to clarify if he or she responded as if he or she were a pharmaceutical drug sponsor, an academic panelist or a health regulatory scientist.

Methods for exploring treatment effect heterogeneity in subgroup analysis: an application to global clinical trials

Multi-country randomised clinical trials (MRCTs) are common in the medical literature, and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in an MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over-interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this paper, we consider some exploratory analysis tools to address this issue. We present three measures derived using the theory of order statistics, which can be used to understand the magnitude and the nature of the variation in treatment effects that can arise merely as an artefact of chance. These measures are not intended to replace a formal test of interaction but instead provide non-inferential visual aids, which allow comparison of the observed and expected differences between regions or other subgroups and are a useful supplement to a formal test of interaction. We discuss how our methodology differs from recently published methods addressing the same issue. A case study of our approach is presented using data from the Study of Platelet Inhibition and Patient Outcomes (PLATO), which was a large cardiovascular MRCT that has been the subject of controversy in the literature. An R package is available that implements the proposed methods.

An overview of statistical planning to address subgroups in confirmatory clinical trials

The effects of treatments within demographic and clinical subgroups of patients are of major interest in most confirmatory clinical trials. Potential factors for defining subgroups include gender, age, disease severity, and geographic region. A major statistical issue for the interpretation of treatment comparisons for subgroups is whether the role of a subgroup is inferential, supportive, or exploratory through respectively corresponding to a primary, key secondary, or hypothesis-generating assessment. This article discusses statistical planning to control type 1 error for the multiple comparisons that correspond to the scope of prespecified inferential subgroups, and it provides some suggestions for addressing the type 2 error that can pertain to prespecified supportive subgroups. Treatment comparisons for exploratory subgroups without a priori specification should always have a very cautious interpretation that accounts for how random variation can influence their pattern of results, although the suggested methods for supportive subgroups can be helpful in this light.

Graphical assessment of consistency in treatment effect among countries in multi-regional clinical trials

Background

One key objective of a multi-regional clinical trial (MRCT) is to use the trial results to ‘bridge’ from the global level to local region in support of local registrations. However, data from each individual country are typically limited and the large number of countries will increase the chance of false positive findings.

Purpose

Graphical tools to facilitate identification of potential outlying countries could be useful for country-level assessment. Existing methods such as funnel plot and expected range of treatment effect can substantially increase the false positive rate. The expected range approach can also have a very low power when there are a large number of small countries, which is typical in a MRCT.

Methods

In this article, we apply normal probability plots, commonly used as a diagnostic tool in linear regression analysis, to assess the differences among countries. Evidence of possible inconsistency, which incorporates both the estimated treatment effect and sample size, is plotted against its expected order statistic.

Results

A simulation study is conducted to assess the impact of the negative correlation among residuals due to unequal sample sizes among countries and the performance of the proposed methods compared to existing approaches. The proposed methods tend to have a balanced consideration with substantially smaller false positive rate and reasonable probability to identify outlying countries in realistic scenarios.

Limitations

While much lower than that of commonly used methods, the false positive rates of the proposed methods are not strictly controlled. This may be acceptable for these graphical tools with intention to flag potential outliers for investigation.

Conclusions

We recommend routine use of normal probability plots in MRCTs as a tool to identify potential outliers. If the normal probability plot is approximately linear but has heavy tails with a few outlying countries, these potential outliers should be examined carefully to understand the possible reasons.

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.

Empirical shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

Multi-regional clinical trials have been widely used for efficient global new drug developments. Both a fixed-effect model and a random-effect model can be used for trial design and data analysis of a multi-regional clinical trial. In this paper, we first compare these two models in terms of the required sample size, type I error rate control, and the interpretability of trial results. We then apply the empirical shrinkage estimation approach based on the random-effect model to two criteria of consistency assessment of treatment effects across regions. As demonstrated in our computations, compared with the sample estimator, the shrinkage estimator of the treatment effect of an individual region borrowing information from the other regions is much closer to the estimator of the overall treatment effect, has smaller variability, and therefore provides much higher probability for demonstrating consistency. We use a multinational trial example with time to event endpoint to illustrate the application of the method.

Current situation of clinical trials in Beijing, China

PURPOSE

This study investigated the current quality of clinical trials conducted in China.

METHODS

Questionnaires were administered to medical doctors belonging to institutes affiliated to Peking University in Beijing, China. The delivery and collection of questionnaires were conducted by a research team from China. Analysis and evaluation were conducted by research teams from both China and Japan.

RESULTS

A total of 145 questionnaires were administered and 117 respondents included the name of the medical institution to which they belonged. A total of 56.3% of the respondents participated in audit and inspection by institutes and 50.5% of the respondents reported receipt of the audit findings. A further 23.6% participated in audits and inspections performed by an external authority and 20.2% reported the receipt of the audit findings.

CONCLUSION

Our research suggests that clinical trials in Beijing are well conducted and are monitored by both institutions and external authorities.

An adaptive strategy for assessing regional consistency in multiregional clinical trials

Background Unexpected regional difference in treatment effect has been reported in recent multiregional clinical trials (MRCTs). This may cause difficulty in interpreting results and can have regulatory implications such as marketing approvals and/or product labels in various markets. Careful consideration of consistency across regions and appropriate plans to address potential regional difference are necessary at the design stage. However, assessment of consistency in treatment effect is generally not the primary objective, and therefore, when there is no strong a priori reason to expect a regional difference, a MRCT is not usually designed to address the regional consistency. Unexpected regional finding may arise and increase the risk of ambiguous or controversial results at the end of the study.

Purpose To mitigate this risk, we propose an adaptive strategy for regional assessment based upon accumulated blinded data.

Methods If review of accumulated blinded data shows unexpectedly severe imbalance in an intrinsic or extrinsic factor, and further assessment indicates that this factor could be a potential effect modifier as supported by biological plausibility or blinded correlation analysis, a stratified regional analysis controlled for this factor may be specified and documented before database lock.

Results The proposed adaptive strategy can help with the interpretation of unexpected regional finding. A recent trial is used to illustrate the approach.

Limitations Even if the imbalanced factor may appear to explain away the regional difference, establishment of causal effect is usually difficult and requires more involved effort.

Conclusions This approach, by prespecifying the stratified analysis, can reduce the risk of post hoc exaggerated emphases across many possible exploratory analyses and provide greater confidence in the validity of the conclusions. If a causal effect can be established that the apparent regional difference is likely caused by this intrinsic or extrinsic factor, this prespecified analysis can also help guide clinical practice.

APEC Harmonization Center: challenges and issues relating to multiregional clinical trials in the APEC region

The Asia-Pacific Economic Cooperation (APEC) Harmonization Center (AHC) was established in 2009 with the purpose of promoting harmonization of regulatory processes for drugs and medical devices. The AHC held three training workshops on multiregional clinical trials (MRCTs); these workshops provided forums for discussing the value and potential benefits of MRCTs. Participants from regulatory agencies, the pharmaceutical industry, and academia identified many issues and made recommendations for resolving major challenges with the aim of improving the capacity of the Asia-Pacific region to carry out MRCTs.