Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework introduces additional design considerations and has the potential to identify designs with more statistical power, even when some parameters are constrained due to immutable practical concerns. The results also suggest that the gains in efficiency introduced through the expanded framework are fairly robust to misspecifications of the expanded cost structure and concomitant design parameters (e.g., intraclass correlation coefficient). The proposed framework is implemented in the R package odr.

中文翻译：

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成本不等的簇随机试验中的最优样本分配

传统的最佳设计框架考虑的采样成本结构范围很窄，从而限制了它们确定最强大和最有效设计的能力。通过允许在集群随机试验中使用可变的抽样成本，我们放宽了先前最佳设计框架的一些限制。所提出的框架引入了更多的设计考虑因素，并且即使在某些参数由于不可变的实际考虑而受到约束的情况下，也有可能以更大的统计能力来识别设计。结果还表明，通过扩展框架引入的效率提高对于扩展成本结构的错误指定和随之而来的设计参数（例如，类内相关系数）非常可靠。建议的框架在R包odr中实现。