Maroufy Vahed, Marriott Paul, Pezeshk Hamid
a Department of Statistics and Actuarial Science , University of Waterloo , Waterloo , Ontario , Canada.
J Biopharm Stat. 2014;24(4):715-31. doi: 10.1080/10543406.2014.902851.
In this article, we discuss an optimization approach to the sample size question, founded on maximizing the value of information in comparison studies with binary responses. The expected value of perfect information (EVPI) is calculated and the optimal sample size is obtained by maximizing the expected net gain of sampling (ENGS), the difference between the expected value of sample information (EVSI) and the cost of conducting the trial. The data are assumed to come from two independent binomial distributions, while the parameter of interest is the difference between the two success probabilities, [Formula: see text]. To formulate our prior knowledge on the parameters, a Dirichlet prior is used. Monte Carlo integration is used in the computation and optimization of ENGS. We also compare the results of this approach with existing Bayesian methods and show how the new approach reduces the computational complexity considerably.
在本文中，我们讨论了一种针对样本量问题的优化方法，该方法基于在二元反应的比较研究中最大化信息价值。计算了完美信息的期望值（EVPI），并通过最大化抽样的预期净收益（ENGS）来获得最优样本量，ENGS是样本信息的期望值（EVSI）与进行试验的成本之间的差值。假设数据来自两个独立的二项分布，而感兴趣的参数是两个成功概率之间的差值，[公式：见原文]。为了构建我们对参数的先验知识，使用了狄利克雷先验。在ENGS的计算和优化中使用了蒙特卡罗积分。我们还将这种方法的结果与现有的贝叶斯方法进行了比较，并展示了新方法如何显著降低计算复杂度。
