Background:

Cluster randomised trials, like individually randomised trials, may benefit from a baseline period of data collection. We consider trials in which clusters prospectively recruit or identify participants as a continuous process over a given calendar period, and ask whether and for how long investigators should collect baseline data as part of the trial, in order to maximise precision.

Methods:

We show how to calculate and plot the variance of the treatment effect estimator for different lengths of baseline period in a range of scenarios, and offer general advice.

Results:

In some circumstances it is optimal not to include a baseline, while in others there is an optimal duration for the baseline. All other things being equal, the circumstances where it is preferable not to include a baseline period are those with a smaller recruitment rate, smaller intracluster correlation, greater decay in the intracluster correlation over time, or wider transition period between recruitment under control and intervention conditions.

Conclusion:

The variance of the treatment effect estimator can be calculated numerically, and plotted against the duration of baseline to inform design. It would be of interest to extend these investigations to cluster randomised trial designs with more than two randomised sequences of control and intervention condition, including stepped wedge designs.

中文翻译：

---

连续招募和前瞻性基线期整群随机试验的优化设计

背景：

与单独随机试验一样，整群随机试验可能受益于数据收集的基线期。我们将集群前瞻性招募或识别参与者的试验视为在给定日历期内的连续过程，并询问研究人员是否应收集基线数据以及收集基线数据作为试验的一部分，以最大限度地提高精度。

方法：

我们展示了如何在一系列场景中计算和绘制不同长度的基线期的治疗效果估计量的方差，并提供一般建议。

结果：

在某些情况下，最好不包括基线，而在其他情况下，基线有一个最佳持续时间。在其他条件相同的情况下，最好不包括基线期的情况是那些招募率较小、簇内相关性较小、簇内相关性随时间衰减较大或控制条件下招募与干预条件下招募之间过渡期较长的情况。

结论：

治疗效果估计量的方差可以通过数值计算，并根据基线持续时间绘制，以告知设计。将这些研究扩展到具有两个以上随机控制和干预条件序列的集群随机试验设计，包括阶梯式楔形设计，将是很有意义的。