Department of Methodology and Statistics, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands.
Behav Res Methods. 2021 Aug;53(4):1731-1745. doi: 10.3758/s13428-020-01529-7. Epub 2021 Feb 2.
The reduced efficiency of the cluster randomized trial design may be compensated by implementing a multi-period design. The trial then becomes longitudinal, with a risk of intermittently missing observations and dropout. This paper studies the effect of missing data on design efficiency in trials where the periods are the days of the week and clusters are followed for at least one week. The multilevel model with a decaying correlation structure is used to relate outcome to period and treatment condition. The variance of the treatment effect estimator is used to measure efficiency. When there is no data loss, efficiency increases with increasing number of subjects per day and number of weeks. Different weekly measurement schemes are used to evaluate the impact of planned missing data designs: the loss of efficiency due to measuring on fewer days is largest for few subjects per day and few weeks. Dropout is modeled by the Weibull survival function. The loss of efficiency due to dropout increases when more clusters drop out during the course of the trial, especially if the risk of dropout is largest at the beginning of the trial. The largest loss is observed for few subjects per day and a large number of weeks. An example of the effect of waiting room environments in reducing stress in dental care shows how different design options can be compared. An R Shiny app allows researchers to interactively explore various design options and to choose the best design for their trial.
群随机试验设计效率的降低可以通过采用多周期设计来补偿。试验随后成为纵向的，存在间歇性缺失观测值和脱落的风险。本文研究了在以周为周期且将群组至少随访一周的试验中，缺失数据对设计效率的影响。使用具有衰减相关结构的多水平模型将结果与周期和处理条件联系起来。用处理效果估计值的方差来衡量效率。当没有数据丢失时，效率随每天的受试者数量和每周的数量增加而增加。使用不同的每周测量方案来评估计划缺失数据设计的影响：每天受试者数量较少且每周较少时，由于测量天数减少而导致的效率损失最大。使用 Weibull 生存函数对脱落进行建模。在试验过程中，随着越来越多的群组脱落，由于脱落导致的效率损失增加，特别是如果脱落的风险在试验开始时最大。每天受试者数量较少且每周较多时，观察到的损失最大。一个减少牙科护理压力的候诊室环境的例子展示了如何比较不同的设计方案。一个 R Shiny 应用程序允许研究人员交互式探索各种设计方案，并为他们的试验选择最佳设计。
