Singh Satya Prakash, Yadav Pradeep
Department of Mathematics, Indian Institute of Technology Hyderabad, Telangana, India.
American Express Company, New York, United States.
J Appl Stat. 2020 Jun 12;48(9):1527-1540. doi: 10.1080/02664763.2020.1779195. eCollection 2021.
In cluster-randomized trials, investigators randomize clusters of individuals such as households, medical practices, schools or classrooms despite the unit of interest are the individuals. It results in the loss of efficiency in terms of the estimation of the unknown parameters as well as the power of the test for testing the treatment effects. To recoup this efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, the clusters within a treatment arm might be heterogeneous in nature. In this article, we propose a locally optimal design that accounts the clusters heterogeneity and optimally allocates the subjects within each cluster. To address the dependency of design on the unknown parameters, we also discuss Bayesian optimal designs. Performances of proposed designs are investigated numerically through some data examples.
在整群随机试验中，研究者将个体群（如家庭、医疗实践机构、学校或教室）进行随机分组，尽管感兴趣的单位是个体。这导致在估计未知参数以及检验治疗效果的检验效能方面效率损失。为了弥补这种效率损失，一些研究将相似的群配对，并在配对内随机分配治疗。然而，一个治疗组内的群在本质上可能是异质性的。在本文中，我们提出一种局部最优设计，该设计考虑了群的异质性，并在每个群内最优地分配受试者。为了解决设计对未知参数的依赖性，我们还讨论了贝叶斯最优设计。通过一些数据实例对所提出设计的性能进行了数值研究。
