Using historical data to inform extrapolation decisions in children
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Abstract: 'Using historical data to inform extrapolation decisions in children'.
When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that exposure-response (E-R) relationships are similar in these different groups. In this presentation, we consider how 'source' data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether E-R relationships are similar in adults and younger children.
A Bayesian multivariate meta-analytic model is used to synthesise the E-R data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of E-R relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar E-R relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.