Bayesian Modelling Strategies for Borrowing of Information in Randomised Basket Trials

Abstract

Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects ('treatment effect borrowing', TEB) to borrowing over the subtrial groupwise responses ('treatment response borrowing', TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeablyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses

“…To address phase II basket trials, several methods have been developed based on the concept of pairwise borrowing. Notable examples include the works of Fujikawa et al, 55 Zheng and Wason, 56 and Ouma et al 57 …”

“…To address phase II basket trials, several methods have been developed based on the concept of pairwise borrowing. Notable examples include the works of Fujikawa et al, 55 Zheng and Wason, 56 and Ouma et al 57 Albeit being different in the specific details, the general idea of pairwise borrowing can be described as follows. Let p(p gj jData) denote the posterior distribution of p gj based on the data observed in subgroup g. Under the Bayesian framework, assuming pairwise exchangeability, an informative prior for p gj of a subgroup g can be chosen to be the posterior distribution p(p g 0 j jData) of a complementary subgroup g 0 , where g 0 6 ¼ g. The informative prior can be constructed using the notion of the power prior, 58 commensurate prior, 59 among others.…”

Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research

“…Typically in an umbrella trial, disease subtypes or patient-level characteristics determine the subtrial that the patient can be enrolled in, that is, which treatment options they are considered eligible for. Each subtrial evaluates one or more specific, targeted experimental treatment(s) (Ouma et al, 2022) and may be either single-arm (with exploratory objectives)…”

Dynamic Enrichment of Bayesian Small-Sample, Sequential, Multiple Assignment Randomized Trial Design Using Natural History Data: A Case Study from Duchenne Muscular Dystrophy

“…In the context of basket trials, Ouma et al. (2022) explored Bayesian treatment effect borrowing and treatment response borrowing models that can be expanded to enable external control data borrowing. In addition to Bayesian methods, frequentist methods such as propensity score based matching (Rosenbaum & Rubin, 1983), stratification, and inverse probability weighting (Lin et al., 2018) are widely used when aggregate level information and baseline covariates are available.…”

“…Notable dynamic methods include normalized power prior (Duan, 2005;Neuenschwander et al, 2009), commensurate priors (Hobbs et al, 2011), and robust meta-analytic-predictive priors (Neuenschwander et al, 2010(Neuenschwander et al, , 2016Spiegelhalter et al, 2004;Schmidli et al, 2014). In the context of basket trials, Ouma et al (2022) explored Bayesian treatment effect borrowing and treatment response borrowing models that can be expanded to enable external control data borrowing. In addition to Bayesian methods, frequentist methods such as propensity score based matching (Rosenbaum & Rubin, 1983), stratification, and inverse probability weighting (Lin et al, 2018) are widely used when aggregate level information and baseline covariates are available.…”