Consistency assessment with global and bridging development strategies in emerging markets

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.

Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods

OBJECTIVE

When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes.

METHODS

Web of Science was used to identify papers proposing methods relevant for using data from a 'source population' to support inferences for a 'target population'. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes.

RESULTS

Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic.

CONCLUSIONS

Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.