Weighted Evidence Approach of Bridging Study

Abstract

The ICH E5 Guidance facilitates the registration of medicine among ICH regions by recommending a framework for evaluating the impact of ethnic factors upon a medicine's effect. It further describes the use of bridging studies, when necessary, to allow extrapolation of foreign clinical data to a new region. Bridging studies are performed in a new region for medicines already approved in the original region. The conventional noninferiority criterion requires the treatment effect (adjusted for placebo) attained in the new region preserves a prespecified proportion of the treatment effect attained in the original region. Such a bridging criterion, however, is often impractical. Hsiao et al. (2007) proposed a Bayesian approach that borrows the strength of the original trial to establish the treatment effect in the bridging region through using a weighted prior distribution. The weight, however, is often difficult to prespecify. In this presentation, we consider the overall treatment effect by combining the weighted effects attained in the original and bridging regions. The maximum weight allowed to be placed on the estimate of bridging region in order to show a significant overall treatment effect represents the strength of the treatment effect in the bridging region. Regional approval will be evaluated either by comparing the weight estimate with the prespecified limit or by benefit-risk evaluation of the medicine. Sample size requirements for the approaches are derived. The simulation results of type I error rate and power for the proposed methods are given. An example illustrates the application of the proposed procedures.

Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

“…It should also be noted that this bias cannot be eliminated by the identification of additional covariates. Extrapolation to a different population requires accurate estimation of the underlying parameter‐covariate correlations, which in turn imposes the availability of data (likelihood) or alternatively, the use of priors that support inferences about the parameter distribution in a different population, including the magnitude and nature of the covariate effects in those conditions . At present, our findings suggest that only interpolation is feasible when making use of non‐linear hierarchical models to describe pharmacokinetics in children.…”

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

“…Forty-four frequentist methods were identified 15,34–36,41–66 of which 11 methods 41–51 synthesised data from source and target populations in a joint model, three methods 52–54 combined data across populations through a weighted test statistic and 30 methods 15,34–36,48–51,55–66 proposed criteria to assess the consistency of estimates of key parameters in different populations.…”

“…De Santis [81] consider using a design prior borrowing information from historical data to plan a clinical trial, for instance to inform sample size selections. Additionally, 12 methods included in the review [18,21,28,33, 37,39,47, 51, 53,55, 73] use source data to inform the design (through sample size calculations) and analysis of 21 [20,26,32, 75] use source and target data to inform mid-study adaptations to the study in the target population.…”

“…Three methods [53][54][55] propose making final inferences about the efficacy of a new treatment in a new geographic region on the basis of a test-statistic combining information from the source and target populations. Suppose Z T and Z S are standardised test statistics comparing mean responses on a new treatment and placebo in a new and original region, respectively.…”

“…De Santis [81] consider using a design prior borrowing information from historical data to plan a clinical trial, for instance to inform sample size selections. Additionally, 12 methods included in the review [18,21,28,33, 37,39,47, 51, 53,55, 73] use source data to inform the design (through sample size calculations) and analysis of 21 [20,26,32, 75] use source and target data to inform mid-study adaptations to the study in the target population.Software was available for few of the 102 methods identified by this review. Computer syntax was included in a main paper or accompanying supplementary material for 9 methods [21, 27,32,33,35,36,39]; code was stated as available upon request from the corresponding author of one method [70]; syntax for another method [17] was included in a related commentary article [82].…”