Gentile Susanna, Sambucini Valeria
Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy.
Biom J. 2023 Oct;65(7):e2200183. doi: 10.1002/bimj.202200183. Epub 2023 May 9.
Classical power analysis for sample size determination is typically performed in clinical trials. A "hybrid" classical Bayesian or a "fully Bayesian" approach can be alternatively used in order to add flexibility to the design assumptions needed at the planning stage of the study and to explicitly incorporate prior information in the procedure. In this paper, we exploit and compare these approaches to obtain the optimal sample size of a single-arm trial based on Poisson data. We adopt exact methods to establish the rejection of the null hypothesis within a frequentist or a Bayesian perspective and suggest the use of a conservative criterion for sample size determination that accounts for the not strictly monotonic behavior of the power function in the presence of discrete data. A Shiny web app in R has been developed to provide a user-friendly interface to easily compute the optimal sample size according to the proposed criteria and to assure the reproducibility of the results.
在临床试验中，通常会进行用于确定样本量的经典功效分析。为了增加研究规划阶段所需设计假设的灵活性，并在过程中明确纳入先验信息，也可以采用“混合”经典贝叶斯方法或“完全贝叶斯”方法。在本文中，我们利用并比较这些方法，以基于泊松数据获得单臂试验的最优样本量。我们采用精确方法在频率主义或贝叶斯视角内建立对原假设的拒绝，并建议使用一种保守标准来确定样本量，该标准考虑了在存在离散数据时功效函数并非严格单调的行为。已经开发了一个R语言的Shiny网络应用程序，以提供一个用户友好的界面，根据所提出的标准轻松计算最优样本量，并确保结果的可重复性。
