The organisation of a design of experiments, for example, for the realisation of a clinical trial, is crucial. It is often desirable to balance designs so that the means of the covariates are approximately the same in the test and control groups. In survey sampling theory, balanced sampling and calibration are two techniques that improve the precision of estimates. In this paper, we show the links between the two areas. We begin by assessing the gain in precision between a balanced design and a simple random sampling for the least squares estimators and the estimator by differences. We compare rerandomisation techniques and the cube method in order to balance the design. We propose a new method, particularly efficient, which combines the cube method with multivariate matching. A set of simulations is carried out in order to evaluate the different methods. The interest of the calibration is shown even if the design is almost balanced. It is thus shown that tools used by survey statisticians can be useful for experimental designs and clinical trials.

中文翻译：

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受调查抽样理论启发建立有效临床试验的一些解决方案

实验设计的组织，例如，为了实现临床试验，是至关重要的。通常需要平衡设计，以便协变量的均值在测试组和对照组中大致相同。在调查抽样理论中，平衡抽样和校准是提高估计精度的两种技术。在本文中，我们展示了这两个领域之间的联系。我们首先评估平衡设计与最小二乘估计量和差异估计量的简单随机抽样之间的精度增益。我们比较了重新随机化技术和立方体方法以平衡设计。我们提出了一种特别有效的新方法，它将立方体方法与多元匹配相结合。进行了一组模拟以评估不同的方法。即使设计几乎是平衡的，也会显示出校准的兴趣。因此表明，调查统计学家使用的工具可用于实验设计和临床试验。