Bayesian cluster hierarchical model for subgroup borrowing in the design and analysis of basket trials with binary endpoints

Abstract

Master protocol designs are often proposed to improve the efficiency of drug development with multiple subgroups. In the basket trial design, different subgroups can have similar biological pathogenesis pathways. Hence, a target therapy can result in similar responses. A good information sharing strategy between different subgroups can potentially improve the efficiency of evaluating treatment efficacy. In traditional hierarchical models, based on the exchangeability assumption, all subgroups are placed into the same sharing pool for cross subgroup information sharing. However, due to the heterogeneity between subgroups, there can be large differences in drug efficacy. Under such cases, strong borrowing across all subgroups is not suitable and no borrowing can be inefficient, because the treatment effect is analyzed in each subgroup separately. We propose a Bayesian cluster hierarchical model (BCHM) to improve the operating characteristics of estimating the treatment effect in multiple subgroups in basket trials. Bayesian nonparametric method is applied to dynamically calculate the number of clusters by conducting a multiple cluster classification based on subgroup outcomes. A hierarchical model is used to compute the posterior probability of the treatment effect, with the borrowing strength determined by the Bayesian nonparametric clustering and the similarities between subgroups. We apply the BCHM to clinical trials with binary endpoints. For treatment effect estimation, the BCHM yields lower mean squared error values, when compared to the independent analyses. In scenarios with a heterogeneous treatment effect, the BCHM provides lower mean squared error values compared to traditional hierarchical models. In addition, we can construct a loss function to optimize the design parameters. BCHM provides a balanced approach and smart borrowing, which yields better results in assessing the treatment effect in different scenarios compared to other conventional methods.

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

“…BHM has been developed into different variants, such as the calibrated Bayesian hierarchical model (CBHM), 22 Bayesian hierarchical classification and information sharing (BaCIS), 23 and Bayesian cluster hierarchical model (BCHM). 24 Although MEMs are not variants of BHM, MEMs have been extended to basket trials. 25 - 26 Application scenarios and the features of each method are listed in Table 1 .…”

Categories, components, and techniques in a modular construction of basket trials for application and further research

“…This is applied in, for example, Jin et al (2020a) which consists of two different BHMs where each can stand for a different clinical scenario, for example a promising response rate and a rather futile one. From our perspective, especially the modification of the variance is an appealing idea because the amount of sharing can be controlled and adapted individually, and consequently, a subset of baskets can share more information between each other (Chen & Lee, 2020;Jin et al, 2020b). The idea of partially exchangeable baskets originates in Hobbs and Landin (2018) as they implement a multisource exchangeability model (MEM) for a basket trial to leave behind the single source exchangeability models (SEM) like the BHM by (Berry et al, 2013;Thall et al, 2003).…”

“…(2020a) explicitly called their design a proof‐of‐concept (PoC) approach for early development with the goal to state that the treatment works in at least one indication. Other authors (Asano & Hirakawa, 2020; Chen & Lee, 2019, 2020; Berry et al., 2013; Chu & Yuan, 2018b; Psioda et al., 2019; Chu & Yuan, 2018a; Neuenschwander et al., 2016; Fujikawa et al., 2020; Jin et al., 2020b; Hobbs & Landin, 2018; Lyu et al., 2020; Zhou & Ji, 2020; Simon et al., 2016; Zheng & Wason, 2019) refer to early clinical development in phase II with the intention to detect indications that work or at least present promising efficacy results. On the other hand (Chen et al., 2016; Li et al., 2017) propose designs for confirmatory phase III basket trials aiming to achieve approval for the treatment in several indications.…”

“…The latter are stored as a diagonal matrix denoted by . corBHM applies an additional hierarchy stage for the variance in . BCHM —Chen and Lee (2020)The BCHM design shares information in a two‐step procedure. In the first step, a clustering with a Dirichlet process mixture model (DPM) is conducted and the pairwise membership of baskets in the same cluster throughout the sampling process of the DPM is evaluated.…”

Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers

“…Chu and Yuan 15 described a Bayesian latent subgroup trial (BLAST) design to account for the heterogeneity of treatment effects by adaptively grouping the cancer types into clusters based on their treatment responses as well as the longitudinal biomarker measurements and then borrowing information within the clusters using a hierarchical Bayesian model. Chen and Lee 16 discussed the Bayesian hierarchical classification and information sharing for a basket trial to borrow across “similar” subgroups and not to borrow across “dissimilar” ones. Zhou and Ji 17 applied a robust Bayesian hypothesis testing (RoBoT) design to partition the baskets into latent subgroups through a Dirichlet process mixture (DPM) model to achieve more flexible and proper information borrowing.…”