Barry S Eggleston, Joseph G Ibrahim, Becky McNeil, Diane Catellier
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引用次数: 1
摘要
本文介绍了R(R Core Team 2019)软件包BayesCTDesign,用于在可用的情况下使用历史对照数据进行双臂随机贝叶斯试验设计,以及在没有历史对照数据时进行简单的双臂随机贝叶斯试验设计。CRAN上提供的包BayesCTDesign有两个模拟函数,historic_sim()和simple_sim(。BayesCTDesign软件包适用于两个手臂试验,每个手臂的样本量相等。包BayesCTDesign允许用户研究高斯、泊松、伯努利、威布尔、对数正态和逐段指数(pwe)结果。通过在每个模拟复制中使用测试的模拟来估计用户定义的α下的双侧假设测试的功率,该测试涉及将结果特异性治疗效果测量的95%可信区间与零病例值进行比较。如果95%可信区间排除了零情况值,则拒绝零假设,否则接受零假设。本文回顾了将历史控制数据纳入贝叶斯分析的思想,解释了贝叶斯设计的估计过程,并描述了用户界面。最后,通过几个例子说明了贝叶斯TDesign。
BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data.
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.
期刊介绍:
The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.