Comparison of Bayesian methods for incorporating adult clinical trial data to improve certainty of treatment effect estimates in children

Ruth Walker; Bob Phillips; Sofia Dias

doi:10.1371/journal.pone.0281791

Comparison of Bayesian methods for incorporating adult clinical trial data to improve certainty of treatment effect estimates in children

Ruth Walker^*, Bob Phillips, Sofia Dias

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

There are challenges associated with recruiting children to take part in randomised clinical trials and as a result, compared to adults, in many disease areas we are less certain about which treatments are most safe and effective. This can lead to weaker recommendations about which treatments to prescribe in practice. However, it may be possible to 'borrow strength' from adult evidence to improve our understanding of which treatments work best in children, and many different statistical methods are available to conduct these analyses. In this paper we discuss four Bayesian methods for extrapolating adult clinical trial evidence to children. Using an exemplar dataset, we compare the effect of their modelling assumptions on the estimated treatment effect and associated heterogeneity. These modelling assumptions range from adult evidence being completely generalisable to being completely unrelated to the children's evidence. We finally discuss the appropriateness of these modelling assumptions in the context of estimating treatment effect in children.

Original language	English
Article number	e0281791
Number of pages	11
Journal	PLOS ONE
Volume	18
Issue number	6
DOIs	https://doi.org/10.1371/journal.pone.0281791
Publication status	Published - 15 Jun 2023

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10.1371/journal.pone.0281791Licence: CC BY

journal.pone.0281791
© 2023 Walker et al.
Final published version, 594 KBLicence: CC BY

Cite this

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