Wu Sheng, Wong Weng Kee, Crespi Catherine M
Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, California 90095-1772, U.S.A.
Biometrics. 2017 Sep;73(3):916-926. doi: 10.1111/biom.12659. Epub 2017 Feb 9.
We consider design issues for cluster randomized trials (CRTs) with a binary outcome where both unit costs and intraclass correlation coefficients (ICCs) in the two arms may be unequal. We first propose a design that maximizes cost efficiency (CE), defined as the ratio of the precision of the efficacy measure to the study cost. Because such designs can be highly sensitive to the unknown ICCs and the anticipated success rates in the two arms, a local strategy based on a single set of best guesses for the ICCs and success rates can be risky. To mitigate this issue, we propose a maximin optimal design that permits ranges of values to be specified for the success rate and the ICC in each arm. We derive maximin optimal designs for three common measures of the efficacy of the intervention, risk difference, relative risk and odds ratio, and study their properties. Using a real cancer control and prevention trial example, we ascertain the efficiency of the widely used balanced design relative to the maximin optimal design and show that the former can be quite inefficient and less robust to mis-specifications of the ICCs and the success rates in the two arms.
我们考虑二元结局的整群随机试验（CRT）的设计问题，其中两组的单位成本和组内相关系数（ICC）可能不相等。我们首先提出一种使成本效率（CE）最大化的设计，成本效率定义为疗效测量精度与研究成本的比率。由于此类设计可能对未知的ICC以及两组预期的成功率高度敏感，基于对ICC和成功率的单一最佳猜测集的局部策略可能存在风险。为缓解此问题，我们提出一种极大极小最优设计，该设计允许为每组的成功率和ICC指定取值范围。我们推导了干预效果的三种常用测量指标（风险差、相对风险和比值比）的极大极小最优设计，并研究它们的性质。通过一个实际的癌症控制与预防试验示例，我们确定了广泛使用的平衡设计相对于极大极小最优设计的效率，并表明前者可能效率相当低，且对两组ICC和成功率的错误设定缺乏稳健性。
