Bayesian Nonparametric Models

Müller, Peter; Mitra, Riten

doi:10.1007/978-3-319-19518-6_1

Cited by 2 publications

(1 citation statement)

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“…The DP method is thoroughly described by Dey et al., 31 Blei et al., 32 Gershman and Blei, 33 and Müller and Rodriguez. 34 We use a DP mixture model to do the data analysis, and the subsequent posterior inference is performed based on a hierarchical model where y are the observed values, G is the random probability measure, and DPtrue(α, G0true) is a DP prior on random probability measure G. α is the parameter controlling the number of clusters in the sampling and G0 is the hyperprior distribution of G. Let i denote the index of the subgroup i.…”

Section: Multiple Clustering Bayesian Nonparametric Methodsmentioning

confidence: 99%

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

Chen

Lee

2020

Stat Methods Med Res

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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.

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