Dataset Information

BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data.

ABSTRACT: This article introduces the R (R Core Team 2019) package BayesCTDesign for two-arm randomized Bayesian trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The package BayesCTDesign, which is available on CRAN, has two simulation functions, historic_sim() and simple_sim() for studying trial characteristics under user defined scenarios, and two methods print() and plot() for displaying summaries of the simulated trial characteristics. The package BayesCTDesign works with two-arm trials with equal sample sizes per arm. The package BayesCTDesign allows a user to study Gaussian, Poisson, Bernoulli, Weibull, Lognormal, and Piecewise Exponential (pwe) outcomes. Power for two-sided hypothesis tests at a user defined alpha is estimated via simulation using a test within each simulation replication that involves comparing a 95% credible interval for the outcome specific treatment effect measure to the null case value. If the 95% credible interval excludes the null case value, then the null hypothesis is rejected, else the null hypothesis is accepted. In the article, the idea of including historical control data in a Bayesian analysis is reviewed, the estimation process of BayesCTDesign is explained, and the user interface is described. Finally, the BayesCTDesign is illustrated via several examples.

SUBMITTER: Eggleston BS

PROVIDER: S-EPMC8715862 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data.

Eggleston Barry S BS Ibrahim Joseph G JG McNeil Becky B Catellier Diane D

Journal of statistical software 20211130 21

This article introduces the R (R Core Team 2019) package <b>BayesCTDesign</b> for two-arm randomized Bayesian trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The package <b>BayesCTDesign</b>, which is available on CRAN, has two simulation functions, historic_sim() and simple_sim() for studying trial characteristics under user defined scenarios, and two methods print() and plot() for disp ...[more]

PMID: 34975350

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Project description:BACKGROUND:The basket trial evaluates the treatment effect of a targeted therapy in patients with the same genetic or molecular aberration, regardless of their cancer types. Bayesian hierarchical modeling has been proposed to adaptively borrow information across cancer types to improve the statistical power of basket trials. Although conceptually attractive, research has shown that Bayesian hierarchical models cannot appropriately determine the degree of information borrowing and may lead to substantially inflated type I error rates. METHODS:We propose a novel calibrated Bayesian hierarchical model approach to evaluate the treatment effect in basket trials. In our approach, the shrinkage parameter that controls information borrowing is not regarded as an unknown parameter. Instead, it is defined as a function of a similarity measure of the treatment effect across tumor subgroups. The key is that the function is calibrated using simulation such that information is strongly borrowed across subgroups if their treatment effects are similar and barely borrowed if the treatment effects are heterogeneous. RESULTS:The simulation study shows that our method has substantially better controlled type I error rates than the Bayesian hierarchical model. In some scenarios, for example, when the true response rate is between the null and alternative, the type I error rate of the proposed method can be inflated from 10% up to 20%, but is still better than that of the Bayesian hierarchical model. LIMITATION:The proposed design assumes a binary endpoint. Extension of the proposed design to ordinal and time-to-event endpoints is worthy of further investigation. CONCLUSION:The calibrated Bayesian hierarchical model provides a practical approach to design basket trials with more flexibility and better controlled type I error rates than the Bayesian hierarchical model. The software for implementing the proposed design is available at http://odin.mdacc.tmc.edu/~yyuan/index_code.html.

Dataset Information

BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data.

Publications

BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data.

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