A quantitative framework to inform extrapolation decisions in children

Ian Wadsworth, Lisa V. Hampson, Thomas Jaki, Graeme J. Sills, Anthony G. Marson and Richard Appleton

Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 2, 515-534

Abstract: When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognized. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that of similar exposure–response (E–R‐) relationships. Motivated by applications to antiepileptic drug development, we consider how data that are available from existing trials of adults and adolescents 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 fitted to existing E–R‐data and adjusted for external biases that arise because these data are not perfectly relevant to the comparison of interest. We propose a strategy for eliciting expert prior opinion on external biases. From the bias‐adjusted meta‐analysis, we derive prior distributions quantifying our uncertainty about the degree of similarity between E–R‐relationships for adults and younger children. Using these we calculate the prior probability that average pharmacodynamic responses in adults and younger children, both on placebo and at an effective concentration, are sufficiently similar to justify a complete extrapolation of efficacy data. A simulation study is performed to evaluate the operating characteristics of the approach proposed.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:183:y:2020:i:2:p:515-534

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