Baek InYoung, Zhu Wei, Wu Xiangfeng, Wong Weng Kee
Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, New York, USA.
J Biopharm Stat. 2006;16(5):679-93. doi: 10.1080/10543400600860501.
In a dose-response study, there are frequently multiple goals and not all planned observations are realized at the end of the study. Subjects drop out and the initial design can be quite different from the final design. Consequently, the final design can be inefficient. Single- and multiple-objective Bayesian optimal designs that account for potentially missing observations in quantal response models were recently proposed in Baek (2005). In this work, we investigate the efficiencies of the conventional optimal designs that do not incorporate potential missing information relative to our proposed designs. Furthermore, we examine the impact of restricted dose range on the resulting optimal designs. As an application, we used missing data information from a study by Yocum et al. (2003) to design a study for estimating dose levels of tacrolimus that will result in a certain percentage of rheumatoid arthritis patients having an ACR20 response at 6 months.
在剂量反应研究中，通常存在多个目标，并且并非所有计划的观察在研究结束时都能实现。受试者会退出，初始设计可能与最终设计有很大不同。因此，最终设计可能效率低下。Baek（2005年）最近提出了单目标和多目标贝叶斯最优设计，该设计考虑了定量反应模型中潜在的缺失观察值。在这项工作中，我们研究了相对于我们提出的设计而言，未纳入潜在缺失信息的传统最优设计的效率。此外，我们研究了受限剂量范围对所得最优设计的影响。作为一个应用，我们使用了Yocum等人（2003年）一项研究中的缺失数据信息来设计一项研究，以估计他克莫司的剂量水平，该剂量水平将使一定比例的类风湿性关节炎患者在6个月时产生ACR20反应。
