Optimal Study Designs for Cluster Randomised Trials: An Overview of Methods and Results
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整群随机试验最优设计：方法与结果概览
- 研究问题
- 面对群内和时间相关性，如何选择整群试验的干预切换时点、群体分配及各期观测数量，以提高处理效应估计效率？
- 方法与思路
- 作者综述方差公式、实验单位权重估计和组合优化三类方法，并讨论权重整数化、非高斯模型及不同协方差结构。
- 材料陈述的贡献
- 摘要将相关观测的实验设计方法适配到整群试验，并通过多个例子比较群体序列分配与群体各期观测数量的优化方案。
- 阅读关注点
- 应核对最优设计的目标函数、成本和可行性约束，以及协方差设定、权重取整与非高斯扩展如何改变设计效率。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未提供最优配置和比较数值，不能认为某种设计在所有约束下最佳。
原文摘要
There are multiple cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at that time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding weights to whole numbers of clusters and extensions to non-Gaussian models. We present results from multiple cluster trial examples that compare the different methods, including problems involving determining optimal allocation of clusters across a set of cluster sequences, and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures.