Wang Lu, Chen Yong, Zhu Hongjian
Department of Biostatistics, University of Texas Health Science Center School of Public Health at Houston, 1200 Pressler St, Houston, Texas 77030, USA.
Department of Biostatistics and Epidemiology, University of Pennsylvania, 210 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.
J Stat Plan Inference. 2017 Mar;182:88-99. doi: 10.1016/j.jspi.2016.09.002. Epub 2016 Oct 7.
Modern clinical trials are often complex, with multiple competing objectives and multiple endpoints. Such trials should be both ethical and efficient. In this paper, we overcome the obstacles introduced by the large number of unknown parameters and the possible correlations between the multiple endpoints. We obtain the optimal allocation proportions for the following two optimization problems: (1) maximizing the power of the test of homogeneity with a fixed sample size, and (2) minimizing the expected weighted number of failures with a fixed power. Further, we implement these optimal allocations through response-adaptive randomization procedures. Our theoretical results provide the foundation for the implementation and further investigation of the procedure, and our numerical studies demonstrate its ability to achieve diverse objectives.
现代临床试验通常很复杂，有多个相互竞争的目标和多个终点。此类试验应兼具伦理道德性和高效性。在本文中，我们克服了大量未知参数以及多个终点之间可能存在的相关性所带来的障碍。我们针对以下两个优化问题获得了最优分配比例：（1）在固定样本量的情况下最大化齐性检验的功效，以及（2）在固定功效的情况下最小化预期加权失败次数。此外，我们通过响应自适应随机化程序来实施这些最优分配。我们的理论结果为该程序的实施和进一步研究提供了基础，并且我们的数值研究证明了其实现不同目标的能力。
