Statistical Methods for Bridging Studies

Chow, Shein‐Chung; Chiang, Chieh; Liu, Jen‐pei; Hsiao, Chin‐Fu

doi:10.1080/10543406.2012.701578

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…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.…”

Section: Discussionmentioning

confidence: 94%

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

Piana

Danhof

Pasqua

2014

Brit J Clinical Pharma

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AIMSThe accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes. METHODSPlasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10-15 kg and 30-40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups. RESULTSOur findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population. CONCLUSIONSIn contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Modelling and simulation (M&S) is often applied as a design tool for pharmacokinetic and pharmacokinetic-pharmacodynamic bridging in paediatric research.• When models are used for the selection of dose and prediction of drug exposure and/or effects in a new population the identification of influential covariates can play a major role in the accuracy of parameter estimates and subsequent predictions.• The identification of the correct covariates in children is often complex due to the presence of correlations and co-linearity between covariates. WHAT THIS STUDY ADDS• Unless a mechanism-based model can be warranted, the use of a stepwise approach for covariate analysis is not recommended when small datasets are available.• Extrapolation of the covariate effects beyond the parameter distributions explored during model building cannot be performed without bias and, consequently, lead to erroneous dosing recommendations.• The covariate point estimate must be retained in the model when predictions refer to a population in which median or mean values differ from the population used during model building.

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Section: Discussionmentioning

confidence: 94%

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

Piana

Danhof

Pasqua

2014

Brit J Clinical Pharma

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“…Searches identified 58 Bayesian methods from 25 papers. 2–4,16–37 Of these, 54 methods 2–4,16–33 sought to create an informative prior for θ T while four 34–37 assessed the consistency of treatment effects or PK responses between the source and target populations.…”

Section: Thematic Analysis Of Methods For Extrapolationmentioning

confidence: 99%

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

Wadsworth

Jaki

2016

Stat Methods Med Res

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Abstract: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 https://mc.manuscriptcentral.com/smmr Statistical Methods in Medical Research F o r P e e r R e v i e w commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects. Page 2 of 66https://mc.manuscriptcentral.com/smmr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Abstract 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'. Fou...

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“…Several methods have been developed for the design and analysis of bridging studies; see Chow et al . () for a review. Specifically, Shao and Chow () proposed a sensitivity index to assess the reproducibility probability, and Shih () suggested a sequential procedure to test the consistency between the foreign and bridging studies.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses

2019

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The pharmaceutical industry and regulatory agencies are increasingly interested in conducting bridging studies in order to bring an approved drug product from the original region (eg, United States or European Union) to a new region (eg, Asian‐Pacific countries). In this article, we provide a new methodology for the design and analysis of bridging studies by assuming prior knowledge on how the null and alternative hypotheses in the original, foreign study are related to the null and alternative hypotheses in the bridging study and setting the type I error for the bridging study according to the strength of the foreign‐study evidence. The new methodology accounts for randomness in the foreign‐study evidence and controls the average type I error of the bridging study over all possibilities of the foreign‐study evidence. In addition, the new methodology increases statistical power, when compared to approaches that do not use foreign‐study evidence, and it allows for the possibility of not conducting the bridging study when the foreign‐study evidence is unfavorable. Finally, we conducted extensive simulation studies to demonstrate the usefulness of the proposed methodology.

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Statistical Methods for Bridging Studies

Cited by 13 publications

References 8 publications

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

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

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

Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses

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