Regional consistency assessment in multiregional clinical trials

Multiregional clinical trials (MRCTs) have become a favored strategy for new drug development. The accurate evaluation of treatment effects across different regions is crucial for interpreting the results of MRCTs. Consistency between regional and overall results ensures the extrapolability of the overall conclusions to individual regions. While numerous statistical methods have been proposed for consistency assessment, a notable proportion necessitate a substantial escalation in sample size, particularly in scenarios involving more than four regions within MRCTs. This, paradoxically, undermines the fundamental intent of MRCTs. In addition, standardized statistical criteria for concluding consistency are yet to be established. In this paper, we develop further consistency assessment approaches in the framework of two multivariate likelihood ratio test-based methods, namely mLRTa and mLRTb, wherein consistency is cast as the alternative and null hypotheses. Notably, our exploration unveils that qualitative methods such as the funnel approach and PMDA methods are special instances of mLRTa. Furthermore, our work underscores that these three qualitative methodologies roughly share the same level of assurance probability (AP). Intriguingly, when the number of regions in an MRCT surpasses five, even when the overall sample size guarantees a power of 90% or more and the true treatment effects remain uniform across regions, the AP remains below the 70% mark. Drawing from our meticulous examination of operational attributes, we recommend mLRTa with positive treatment effects in all regions in the alternative hypothesis with significance level 0.5 or mLRTb with all regional treatment effects being equal in the null and significance level of 0.2.

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.

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.

A proposed statistical framework for the management of subgroup analyses for large clinical trials

Planned and unplanned subgroup analyses of large clinical trials are frequently performed and the results are sometimes difficult to interpret. The source of a nominal significant finding may come from a true signal, variation of the clinical trial outcome or the observed data structure. Quantitative assessment is critical to the interpretation of the totality of the clinical data. In this article we provide a general framework to manage subgroup analyses and to interpret the findings through a set of supplement analyses to planned main (primary and secondary) analyses, as an alternative to the commonly used multiple comparison framework. The proposed approach collectively and coherently utilizes several quantitative methods and enhances the credibility and interpretability of subgroup analyses. A case study is used to illustrate the application of the proposed method.

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.

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.

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.