Pezeshk Hamid, Nematollahi Nader, Maroufy Vahed, Gittins John
Center of Excellence in Biomathematics and School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran.
Stat Methods Med Res. 2009 Apr;18(2):183-94. doi: 10.1177/0962280208089298. Epub 2008 Apr 29.
Sample size computations are largely based on frequentist or classical methods. In the Bayesian approach the prior information on the unknown parameters is taken into account. In this work we consider a fully Bayesian approach to the sample size determination problem which was introduced by Grundy et al. and developed by Lindley. This approach treats the problem as a decision problem and employs a utility function to find the optimal sample size of a trial. Furthermore, we assume that a regulatory authority, which is deciding on whether or not to grant a licence to a new treatment, uses a frequentist approach. We then find the optimal sample size for the trial by maximising the expected net benefit, which is the expected benefit of subsequent use of the new treatment minus the cost of the trial.
样本量计算主要基于频率论或经典方法。在贝叶斯方法中，未知参数的先验信息会被考虑在内。在这项工作中，我们考虑一种完全贝叶斯方法来解决由格伦迪等人提出并由林德利发展的样本量确定问题。这种方法将该问题视为一个决策问题，并采用效用函数来找到试验的最优样本量。此外，我们假设一个决定是否批准新疗法许可的监管机构采用频率论方法。然后，我们通过最大化预期净收益来找到试验的最优样本量，预期净收益是新疗法后续使用的预期收益减去试验成本。
