Speaker
Description
Bayesian dynamic borrowing (BDB) methods are popular for incorporating historical data in rare disease or paediatric clinical trials, in particular with regard to control groups. They can be used to leverage the historical information while mitigating the consequences of potential prior-data conflicts to some degree. However, these methods do not consider baseline covariate information that might be prognostic for the outcome and could therefore be relevant to explain discrepancies between the outcomes of the current and historical control groups. To address this, novel methods have been proposed that integrate techniques from the causal inference literature into the BDB framework. They claim to make the borrowing more robust and efficient by, at least partially, relating the discrepancy (agreement) in the outcome distributions to differences (similarities) in baseline characteristics and making corresponding adjustments.
A number of such methods are now available with propensity score integrated priors [1] forming the largest group. While such methodological developments are desirable, they also pose new challenges, particularly in choosing the appropriate method to apply to a specific clinical trial. The performance of these methods is usually assessed via simulation studies, which can lead to over-optimistic conclusions, making this choice non-trivial. Neutral comparison studies that investigate existing methods can address this issue [2].
In this work, we apply the idea of neutral comparison studies to Bayesian methods integrating causal inference approaches for borrowing historical control data in clinical trials for continuous outcomes and compare three recently proposed methods, namely: the propensity score integrated commensurate prior [1], propensity score weighted multi-source exchangeability models [3], and Bayesian additive regression trees [4]. We assess their performance in a large simulation study covering, among others, different historical data sample sizes, varying degrees of observed and unobserved confounding, as well as different effect sizes.
[1]X. Wang, L. Suttner, T. Jemielita, and X. Li, Propensity score-integrated Bayesian prior approaches for augmented control designs: a simulation study, J. Biopharm. Stat. 32, 170 (2022).
[2]A.-L. Boulesteix, S. Lauer, and M. J. A. Eugster, A Plea for Neutral Comparison Studies in Computational Sciences, PLOS ONE 8, e61562 (2013).
[3]W. Wei, Y. Zhang, S. Roychoudhury, and the A. D. N. Initiative, Propensity score weighted multi-source exchangeability models for incorporating external control data in randomized clinical trials, Stat. Med. 43, 3815 (2024).
[4]T. Zhou and Y. Ji, Incorporating external data into the analysis of clinical trials via Bayesian additive regression trees, Stat. Med. 40, 6421 (2021).
21429408755