A Quantitative Framework to Inform Extrapolation Decisions in Children

Wadsworth, Ian; Jaki, Thomas; Sills, Graeme J.; Marson, Anthony G; Appleton, Richard

doi:10.1111/rssa.12532

Cited by 2 publications

(2 citation statements)

References 48 publications

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“…For example, the main ODYSSEY trial population of older children included two baskets (children starting first-line or second-line ART) that were adequately powered for standalone analysis. Similar methods can be used to incorporate information from adult trials when analysing paediatric trials [ 17 , 18 ].…”

Section: Discussionmentioning

confidence: 99%

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

Turner

Turkova

Moore

et al. 2022

BMC Med Res Methodol

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Background Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis. Methods We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined. Results The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis. Conclusions Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.

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Section: Discussionmentioning

confidence: 99%

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

Turner

Turkova

Moore

et al. 2022

BMC Med Res Methodol

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“…We measure exposure by C min . Following Wadsworth et al., 34 we sample log(Cmin) from a N(log(2.94),0.921) distribution, truncating samples above by log(17.27) to avoid excessively high concentrations. We sample random errors from a N(0,0.02) distribution.…”

Section: Design Of the Simulation Studymentioning

confidence: 99%

Exposure–response modelling approaches for determining optimal dosing rules in children

Wadsworth

Bornkamp

Jaki

2020

Stat Methods Med Res

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Within paediatric populations, there may be distinct age groups characterised by different exposure–response relationships. Several regulatory guidance documents have suggested general age groupings. However, it is not clear whether these categorisations will be suitable for all new medicines and in all disease areas. We consider two model-based approaches to quantify how exposure–response model parameters vary over a continuum of ages: Bayesian penalised B-splines and model-based recursive partitioning. We propose an approach for deriving an optimal dosing rule given an estimate of how exposure–response model parameters vary with age. Methods are initially developed for a linear exposure–response model. We perform a simulation study to systematically evaluate how well the various approaches estimate linear exposure–response model parameters and the accuracy of recommended dosing rules. Simulation scenarios are motivated by an application to epilepsy drug development. Results suggest that both bootstrapped model-based recursive partitioning and Bayesian penalised B-splines can estimate underlying changes in linear exposure–response model parameters as well as (and in many scenarios, better than) a comparator linear model adjusting for a categorical age covariate with levels following International Conference on Harmonisation E11 groupings. Furthermore, the Bayesian penalised B-splines approach consistently estimates the intercept and slope more accurately than the bootstrapped model-based recursive partitioning. Finally, approaches are extended to estimate Emax exposure–response models and are illustrated with an example motivated by an in vitro study of cyclosporine.

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A Quantitative Framework to Inform Extrapolation Decisions in Children

Cited by 2 publications

References 48 publications

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

Exposure–response modelling approaches for determining optimal dosing rules in children

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