Sample size determination in clinical trials with multiple co-primary endpoints including mixed continuous and binary variables
Sample size determination in clinical trials with multiple co-primary endpoints including mixed continuous and binary variables
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DOI：
10.1002/bimj.201100221
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发表时间：
2012-09-01
影响因子：
1.7
通讯作者：
Hamasaki, Toshimitsu
中科院分区：
文献类型：
--
作者：
Sozu, Takashi;Sugimoto, Tomoyuki;Hamasaki, Toshimitsu
在药物开发领域，关于建立具有统计学意义的结果来证明具有多个共同主要终点的新治疗方法的疗效，已经进行了广泛的讨论。当设计具有如此多共同主要终点的临床试验时，确定适当的样本量以表明所有共同主要终点的统计显著性并保持期望的总体功率是至关重要的，因为II型错误率随着共同主要终点的数量而增加。我们考虑了由混合连续变量和二元变量组成的多个共同主要端点的总体幂函数和样本量决定，并提供了数值示例来说明总体幂函数和样本量的行为。在表述问题时，我们假设响应变量服从多元正态分布，其中二元变量在具有某二分类点的二分类正态分布中被观察到。数值算例表明，当每个端点的幂近似相等时，随着相关性的增加，样本量减小。
In the field of pharmaceutical drug development, there have been extensive discussions on the establishment of statistically significant results that demonstrate the efficacy of a new treatment with multiple co-primary endpoints. When designing a clinical trial with such multiple co-primary endpoints, it is critical to determine the appropriate sample size for indicating the statistical significance of all the co-primary endpoints with preserving the desired overall power because the type II error rate increases with the number of co-primary endpoints. We consider overall power functions and sample size determinations with multiple co-primary endpoints that consist of mixed continuous and binary variables, and provide numerical examples to illustrate the behavior of the overall power functions and sample sizes. In formulating the problem, we assume that response variables follow a multivariate normal distribution, where binary variables are observed in a dichotomized normal distribution with a certain point of dichotomy. Numerical examples show that the sample size decreases as the correlation increases when the individual powers of each endpoint are approximately and mutually equal.