Sample Size Estimation of Multiregional Clinical Trials with Heterogeneous Variability Across Regions

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.

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.

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.

An Analysis of Japanese Patients Enrolled in Multiregional Clinical Trials in Oncology

The Japanese regulatory agency, the Ministry of Health, Labour and Welfare, requires sponsors to enroll a specific number or proportion of Japanese patients in multiregional clinical trials (MRCTs) in order to allow for the appropriate statistical evaluation of the efficacy and safety of an investigational drug in the Japanese population. This means the actual proportion of Japanese patients to the total sample size would need to be determined by taking into account the proportion of patients in other regions as well as the appropriate statistical considerations. Determining the proportion of Japanese patients that satisfies the regulatory agency's statistical requirement, along with taking into account the practical limitations of patient enrollment, would be difficult for sponsors. We believe that recent studies about the proportion of Japanese patients enrolled in MRCTs provides sponsors with useful information about partitioning sample size into individual regions for MRCTs in oncology. In this study, we investigated the proportion of Japanese patients in MRCTs and further compared the efficacy results from the overall population to that of the Japanese population. The proportion of Japanese patients averaged approximately 10.9%, but the proportion varied depending on the drug type. The results of the primary endpoints in Japanese patients were similar to those of the overall population, regardless of the proportion of Japanese patients.