A dynamic power prior approach to non-inferiority trials for normal means.

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Pharmaceutical Statistics Pub Date : 2024-03-01 Epub Date: 2023-11-14 DOI:10.1002/pst.2349
Francesco Mariani, Fulvio De Santis, Stefania Gubbiotti
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引用次数: 0

Abstract

Non-inferiority trials compare new experimental therapies to standard ones (active control). In these experiments, historical information on the control treatment is often available. This makes Bayesian methodology appealing since it allows a natural way to exploit information from past studies. In the present paper, we suggest the use of previous data for constructing the prior distribution of the control effect parameter. Specifically, we consider a dynamic power prior that possibly allows to discount the level of borrowing in the presence of heterogeneity between past and current control data. The discount parameter of the prior is based on the Hellinger distance between the posterior distributions of the control parameter based, respectively, on historical and current data. We develop the methodology for comparing normal means and we handle the unknown variance assumption using MCMC. We also provide a simulation study to analyze the proposed test in terms of frequentist size and power, as it is usually requested by regulatory agencies. Finally, we investigate comparisons with some existing methods and we illustrate an application to a real case study.

正态均值非劣效性试验的动态功率先验方法。
非劣效性试验将新的实验疗法与标准疗法(主动对照)进行比较。在这些实验中,通常可以获得对照处理的历史信息。这使得贝叶斯方法具有吸引力,因为它允许以一种自然的方式从过去的研究中挖掘信息。在本文中,我们建议使用以前的数据来构造控制效果参数的先验分布。具体来说,我们考虑了一个动态先验,它可能允许在过去和当前控制数据之间存在异质性的情况下贴现借款水平。先验的折扣参数是基于控制参数的后验分布之间的海灵格距离,分别基于历史和当前数据。我们开发了比较正态均值的方法,并使用MCMC处理未知方差假设。根据监管机构的要求,我们还提供了一个模拟研究来分析拟议的测试的频率大小和功率。最后,与现有方法进行了比较,并举例说明了该方法在实际案例中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
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