Quan Hui, Luo Xiaohui, Capizzi Tom
Clinical Biostatistics and Research Decision Sciences, Merck Research Laboratories, RY34-A320, 126 E. Lincoln Ave., Rahway, NJ 07065-0914, USA.
Stat Med. 2005 Jul 30;24(14):2151-70. doi: 10.1002/sim.2101.
Frequently, multiple doses of an active treatment and multiple endpoints are simultaneously considered in the designs of clinical trials. For these trials, traditional multiplicity adjustment procedures such as Bonferroni, Hochberg and Hommel procedures can be applied when treating the comparisons of different doses to the control on all endpoints at the same level. However, these approaches will not take into account the possible dose-response relationship on each endpoint, and therefore are less specific and may have lower power. To gain power, in this paper, we consider the problem as a two-dimensional multiplicity problem: one dimension concerns the multiple doses and the other dimension concerns the multiple endpoints. We propose procedures which consider the dose order to form the closure of the procedures and control the family-wise type I error rate in a strong sense. For this two-dimensional problem, numerical examples show that procedures proposed in this paper in general have higher power than the commonly used procedures (e.g. the regular Hochberg procedure) especially for comparing the higher dose to the control.
在临床试验设计中,通常会同时考虑活性治疗的多个剂量和多个终点。对于这些试验,在将不同剂量与所有终点的对照进行比较时,若将其视为同一水平,可应用传统的多重性调整程序,如邦费罗尼、霍赫贝格和霍梅尔程序。然而,这些方法不会考虑每个终点上可能存在的剂量反应关系,因此特异性较低,功效可能也较低。为了提高功效,在本文中,我们将该问题视为二维多重性问题:一个维度涉及多个剂量,另一个维度涉及多个终点。我们提出了一些程序,这些程序考虑剂量顺序以形成程序的闭包,并在强意义上控制族系I型错误率。对于这个二维问题,数值示例表明,本文提出的程序通常比常用程序(如常规霍赫贝格程序)具有更高的功效,特别是在将高剂量与对照进行比较时。