先验概率
样本量测定
贝叶斯概率
统计
事先信息
共轭先验
计算机科学
杠杆(统计)
数学
事件(粒子物理)
成对比较
计量经济学
人工智能
物理
量子力学
作者
Haiyan Zheng,Thomas Jaki,James Wason
出处
期刊:PubMed
日期:2022-03-06
卷期号:79 (2): 669-683
被引量:4
标识
DOI:10.1111/j.1541-0420.2005.00454.x
摘要
This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant pre-experimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information, and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on pre-experimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pre-trial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.
科研通智能强力驱动
Strongly Powered by AbleSci AI