Comparison of Simulation-Guided Design to Closed-Form Power Calculations in Planning a Cluster Randomized Trial with Covariate-Constrained Randomization: A Case Study in Rural Chad
协变量约束随机化的整群试验规划：模拟设计与闭式功效计算的乍得农村案例比较AI 解读 ↓
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协变量约束随机化的整群试验规划：模拟设计与闭式功效计算的乍得农村案例比较
- 研究问题
- 具有多层嵌套结构且采用协变量约束随机化的整群试验，常规闭式功效公式能否准确反映增加群内样本量的设计收益？
- 方法与思路
- 作者以乍得农村试验规划为例，模拟卫生区随机分配与村庄抽样，比较不同组内相关系数下的模拟功效和常规公式结果。
- 材料陈述的贡献
- 摘要称常规计算出现功效平台，而匹配随机化、协变量调整和抽样设计的模拟显示功效继续增加，提示复杂设计需要更贴合的规划。
- 阅读关注点
- 应核对模拟的数据生成机制、组内相关设定、约束随机化接受规则与最终分析方法，并检查单侧检验的错误率和功效计算。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；这是试验规划案例，摘要未提供完整模拟设定，不能推广为所有整群设计均无功效平台。
原文摘要
Current practices for designing cluster-randomized trials (cRCTs) typically rely on closed-form formulas for power calculations. For cRCTs using covariate-constrained randomization, the utility of conventional calculations might be limited, particularly when data is nested. We compared simulation-based planning of a nested cRCT using covariate-constrained randomization to conventional power calculations using OptiMAx-Chad as a case study. OptiMAx-Chad will examine the impact of embedding mass distribution of small-quantity lipid-based nutrient supplements within an expanded programme on immunization on first-dose measles-containing vaccine (MCV1) coverage among children aged 12-24 months in rural villages in Ngouri. Within the 12 health areas to be randomized, a random subset of villages will be selected for outcome collection. 1,000,000 assignments of health areas with different possible village selections were generated using covariate-constrained randomization to balance baseline village characteristics. The empirically estimated intracluster correlation coefficient (ICC) and the World Health Organization (WHO) recommended values of 1/3 and 1/6 were considered. The desired operating characteristics were 80% power at 0.05 one-sided type I error rate. Using conventional calculations target power for a realistic treatment effect could not be achieved with the WHO recommended values. Conventional calculations also showed a plateau in power after a certain cluster size. Our simulations matched the design of OptiMAx-Chad with covariate adjustment and random selection, and showed that power did not plateau. Instead, power increased with increasing cluster size. Planning complex cRCTs with covariate constrained randomization and a multi-nested data structure with conventional closed-form formulas can be misleading. Simulations can improve the planning of cRCTs.