Shih W J, Zhao P L
Merck Research Laboratories, Rahway, New Jersey 07065, USA.
Stat Med. 1997 Sep 15;16(17):1913-23. doi: 10.1002/(sici)1097-0258(19970915)16:17<1913::aid-sim610>3.0.co;2-z.
Estimation of sample size in clinical trials requires knowledge of parameters that involve the treatment effect and variability, which are usually uncertain to medical researchers. The recent release within the European Union of a Note for Guidance from the Commission for Proprietary Medical Products (CPMP) highlights the importance of this issue. Most previous papers considered the case of continuous response variables that assume a normal distribution; some regarded the portion up to the interim stage as an 'internal pilot study' and required unblinding. In this paper, our concern is with the case of binary response variables, which is more difficult than the normal case since the mean and variance are not distinct parameters. We offer a design with a simple stratification strategy that enables us to verify and update the assumption of the response rates given initially in the protocol. The design provides a method to re-estimate the sample size based on interim data while preserving the trial's blinding. An illustrative numerical example and simulation results show slight effect on the type I error rate and the decision making characteristics on sample size adjustment.
在临床试验中估计样本量需要了解涉及治疗效果和变异性的参数，而医学研究人员通常并不确定这些参数。欧盟最近发布的一份来自专利药品委员会（CPMP）的指导说明凸显了这一问题的重要性。之前的大多数论文考虑的是连续反应变量服从正态分布的情况；一些论文将中期阶段之前的部分视为“内部预试验”，并要求解除盲法。在本文中，我们关注的是二元反应变量的情况，这种情况比正态情况更难，因为均值和方差不是不同的参数。我们提供了一种采用简单分层策略的设计，使我们能够验证和更新方案中最初给出的反应率假设。该设计提供了一种基于中期数据重新估计样本量的方法，同时保持试验的盲法。一个说明性的数值例子和模拟结果表明，对I型错误率以及样本量调整的决策特征影响较小。
