Increasing the efficiency of oncology basket trials using a Bayesian approach

Liu, Rong; Liu, Zheyu; Ghadessi, Mercedeh; Vonk, Richardus

doi:10.1016/j.cct.2017.06.009

Cited by 48 publications

(66 citation statements)

References 24 publications

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“…In frequentist approaches, Cunannan et al [59] modified the method of using independent Simon two-stage designs for each basket to improve the overall efficiency of the trial overall. A hybrid design that combines the Bayesian and frequentist models (hybrid design) [60], has also been proposed. In this design, if the homogeneity of treatment effect among strata is not rejected at an interim analysis, the hierarchical Bayesian model is used at the final analysis.…”

Section: New Trial Designs In Master Protocol Trialsmentioning

confidence: 99%

Master protocol trials in oncology: Review and new trial designs

Hirakawa

Asano

Sato

et al. 2018

Contemporary Clinical Trials Communications

103

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In oncology, next generation sequencing and comprehensive genomic profiling have enabled the detailed classification of tumors using molecular biology. However, it is unrealistic to conduct phase I–III trials according to each sub-population based on patient molecular subtypes. Common protocols that assess the combination of several molecular markers and their targeted therapies by means of multiple sub-studies are required. These protocols are called “master protocols,” and are drawing attention as a next-generation clinical trial design. Recently, several reviews of clinical trials based on the master protocol design have been published, but their definitions of these such trials, including basket, umbrella, and platform trials, were not consistent. Concurrently, the acceleration of the development of new statistical designs for master protocol trials has been underway. This article provides an overview of recent reviews for master protocols, including their statistical design methodologies in Oncology. We also introduce several examples of previous and on-going master protocol trials along with their classifications by some recent studies.

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Section: New Trial Designs In Master Protocol Trialsmentioning

confidence: 99%

Master protocol trials in oncology: Review and new trial designs

Hirakawa

Asano

Sato

et al. 2018

Contemporary Clinical Trials Communications

103

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“…Ventz et al (2017) developed a design with response-adaptive randomization. Liu, Liu, Ghadessi, and Vonk (2017) presented a design applying a Bayesian hierarchical model if the efficacy was homogenous across the strata. Chu and Yuan (2018a) presented a calibrated Bayesian hierarchical model using simulations.…”

mentioning

confidence: 99%

A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability

et al. 2019

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Basket trials simultaneously evaluate the effect of one or more drugs on a defined biomarker, genetic alteration, or molecular target in a variety of disease subtypes, often called strata. A conventional approach for analyzing such trials is an independent analysis of each of the strata. This analysis is inefficient as it lacks the power to detect the effect of drugs in each stratum. To address these issues, various designs for basket trials have been proposed, centering on designs using Bayesian hierarchical models. In this article, we propose a novel Bayesian basket trial design that incorporates predictive sample size determination, early termination for inefficacy and efficacy, and the borrowing of information across strata. The borrowing of information is based on the similarity between the posterior distributions of the response probability. In general, Bayesian hierarchical models have many distributional assumptions along with multiple parameters. By contrast, our method has prior distributions for response probability and two parameters for similarity of distributions. The proposed design is easier to implement and less computationally demanding than other Bayesian basket designs. Through a simulation with various scenarios, our proposed design is compared with other designs including one that does not borrow information and one that uses a Bayesian hierarchical model.

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“…However, the borrowing in this method is essentially a global borrowing. In the left panel of the figure, each subgroup is analyzed using a beta distribution and in the right panel, We have also compared the performance of BaCIS with the two-stage basket design proposed by Liu et al (2017). Liu et al proposed a Bayesian hierarchical mixture model (BHMM) to assess the homogeneity of the response rates of different subgroups (Liu, Liu, Ghadessi, & Vonk, 2017).…”

Section: Basket Trial Designmentioning

confidence: 99%

“…The parameter adjusts the information borrowing magnitude from the hypothesis of "all subgroups are identical" to "all subgroups are independent" and the method does not include the subgroup borrowing within clusters. Liu et al proposed a Bayesian hierarchical mixture model (BHMM) to assess the homogeneity of the response rates of different subgroups (Liu, Liu, Ghadessi, & Vonk, 2017). Similar to the method proposed by Simon et al, a type of global borrowing is applied and information borrowing within clusters is not used in this method.…”

Section: Basket Trial Designmentioning

confidence: 99%

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Bayesian hierarchical classification and information sharing for clinical trials with subgroups and binary outcomes

Chen

Lee

2018

Biometrical J

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Bayesian hierarchical models have been applied in clinical trials to allow for information sharing across subgroups. Traditional Bayesian hierarchical models do not have subgroup classifications; thus, information is shared across all subgroups. When the difference between subgroups is large, it suggests that the subgroups belong to different clusters. In that case, placing all subgroups in one pool and borrowing information across all subgroups can result in substantial bias for the subgroups with strong borrowing, or a lack of efficiency gain with weak borrowing. To resolve this difficulty, we propose a hierarchical Bayesian classification and information sharing (BaCIS) model for the design of multigroup phase II clinical trials with binary outcomes. We introduce subgroup classification into the hierarchical model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multigroup clinical trials with binary outcomes. Compared to the traditional hierarchical models, better operating characteristics are obtained with the BaCIS model under various scenarios. K E Y W O R D Sclassification, clinical trial design, hierarchical model, information borrowing, multigroup phase II trial, subgroup analysis

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Increasing the efficiency of oncology basket trials using a Bayesian approach

Cited by 48 publications

References 24 publications

Master protocol trials in oncology: Review and new trial designs

Master protocol trials in oncology: Review and new trial designs

A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability

Bayesian hierarchical classification and information sharing for clinical trials with subgroups and binary outcomes

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