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.

Operational Experiences in China and Statistical Issues on the Conduct of Clinical Trials During the COVID-19 Pandemic

The COVID-19 outbreak is impacting clinical trials in many ways, such as patient recruitment, data collection and data analysis. To proceed in this difficult time, the adoption of new technologies and new approaches for conducting clinical trials needs to be accelerated. Simultaneously, regulatory agencies such as the US FDA and EMA have issued guidance to help the pharmaceutical industry conduct clinical trials of medical products during the COVID-19 pandemic. In this article, we will address some statistical issues and operational experiences in the conduction of clinical trials during the COVID-19 pandemic. Specifically, we will share experiences in the applications of remote clinical trials in China. Statistical issues related to protocol modifications caused by COVID-19 will be raised.