Statistical Considerations on Design and Analysis of Bridging and Multiregional Clinical Trials

Tsong, Yi

doi:10.1080/10543406.2012.702652

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Under this choice, 95% prior probability mass is concentrated on the interval [0.5, 1.5], meaning that the region-specific MTDs, if divergent, have less than 0.5-fold change between one another. The variance 2 ' could be increased if even larger differences in toxicity across regions are considered plausible.…”

Section: Bayesian Hierarchical Model For Animal Data and Heterogeneous Human Datamentioning

confidence: 99%

“…Bridging strategies are increasingly being used in the paradigm of global drug development [1][2][3][4] to minimise duplication of clinical research without disregarding heterogeneity between patient groups. Bridging studies may be conducted in a new geographic region to evaluate whether a medicine's performance (typically efficacy) is consistent with its performance in other parts of the world where it has been approved based on a complete development program.…”

Section: Introductionmentioning

confidence: 99%

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Bridging across patient subgroups in phase I oncology trials that incorporate animal data

Zheng

Jaki

2021

Stat Methods Med Res

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In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose–toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose–toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, ‘average’ human dosing scale, human dose–toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.

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Section: Bayesian Hierarchical Model For Animal Data and Heterogeneous Human Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bridging across patient subgroups in phase I oncology trials that incorporate animal data

Zheng

Jaki

2021

Stat Methods Med Res

View full text Add to dashboard Cite

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“…Bridging strategies are increasingly being used in the paradigm of global drug development (Huang et al, 2012;Li and Wang, 2012;Tsong, 2012;Viergever and Li, 2015), aiming to minimise duplication of clinical research without disregarding heterogeneity between patient groups. Bridging studies may be conducted in a new geographic region such as Japan to evaluate similarity of the performance, typically with respect to efficacy, of a medicine which has likely been approved in other parts of the world, for instance, Europe, based on a complete clinical drug development program.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian hierarchical model for bridging across patient subgroups in phase I clinical trials with animal data

Zheng,

Hampson,

Jaki

2019

Preprint

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Incorporating preclinical animal data, which can be regarded as a special kind of historical data, into phase I clinical trials can improve decision making when very little about human toxicity is known. In this paper, we develop a robust hierarchical modelling approach to leverage animal data into new phase I clinical trials, where we bridge across non-overlapping, potentially heterogeneous patient subgroups. Translation parameters are used to bring both historical and contemporary data onto a common dosing scale. This leads to feasible exchangeability assumptions that the parameter vectors, which underpin the dose-toxicity relationship per study, are assumed to be drawn from a common distribution. Moreover, human dose-toxicity parameter vectors are assumed to be exchangeable either with the standardised, animal study-specific parameter vectors, or between themselves. Possibility of non-exchangeability for each parameter vector is considered to avoid inferences for extreme subgroups being overly influenced by the other. We illustrate the proposed approach with several trial data examples, and evaluate the operating characteristics of our model compared with several alternatives in a simulation study. Numerical results show that our approach yields robust inferences in circumstances, where data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.

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Statistical Considerations on Design and Analysis of Bridging and Multiregional Clinical Trials

Cited by 2 publications

References 7 publications

Bridging across patient subgroups in phase I oncology trials that incorporate animal data

Bridging across patient subgroups in phase I oncology trials that incorporate animal data

A Bayesian hierarchical model for bridging across patient subgroups in phase I clinical trials with animal data

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