Department of Biostatistics, University of Washington, Seattle, WA, USA.
Division of Biostatistics, The Ohio State University, Columbus, OH, USA.
Clin Trials. 2020 Apr;17(2):176-183. doi: 10.1177/1740774520901488. Epub 2020 Feb 6.
BACKGROUND/AIMS: In a stepped wedge study design, study clusters usually start with the baseline treatment and then cross over to the intervention at randomly determined times. Such designs are useful when the intervention must be delivered at the cluster level and are becoming increasingly common in practice. In these trials, if the outcome is death or serious morbidity, one may have an ethical imperative to monitor the trial and stop before maximum enrollment if the new therapy is proven to be beneficial. In addition, because formal monitoring allows for the stoppage of trials when a significant benefit for new therapy has been ruled out, their use can make a research program more efficient. However, use of the stepped wedge cluster randomized study design complicates the implementation of standard group sequential monitoring methods. Both the correlation of observations introduced by the clustered randomization and the timing of crossover from one treatment to the other impact the rate of information growth, an important component of an interim analysis.
We simulated cross-sectional stepped wedge study data in order to evaluate the impact of sequential monitoring on the Type I error and power when the true intracluster correlation is unknown. We studied the impact of varying intracluster correlations, treatment effects, methods of estimating the information growth, and boundary shapes.
While misspecified information growth can impact both the Type I error and power of a study in some settings, we observed little inflation of the Type I error and only moderate reductions in power across a range of misspecified information growth patterns in our simulations.
Taking the study design into account and using either an estimate of the intracluster correlation from the ongoing study or other data in the same clusters should allow for easy implementation of group sequential methods in future stepped wedge designs.
背景/目的:在阶梯式楔形设计研究中,研究群组通常从基线治疗开始,然后在随机确定的时间交叉到干预。当干预措施必须在群组级别实施时,这种设计非常有用,并且在实践中越来越常见。在这些试验中,如果结局是死亡或严重的发病率,由于新疗法被证明是有益的,可能会有道德上的要求,即在最大入组前监测试验并停止。此外,由于正式监测允许在排除新疗法的显著益处后停止试验,因此它们的使用可以使研究计划更有效。然而,使用阶梯式楔形群组随机设计会使标准群组序贯监测方法的实施复杂化。聚类随机化引入的观察值的相关性以及从一种治疗方法交叉到另一种治疗方法的时间都会影响信息增长的速度,这是中期分析的一个重要组成部分。
我们模拟了横断面阶梯式楔形研究数据,以评估当真实的组内相关系数未知时,序贯监测对Ⅰ型错误和功效的影响。我们研究了不同的组内相关系数、治疗效果、估计信息增长的方法和边界形状对研究的影响。
虽然信息增长的错误指定可能会在某些情况下影响研究的Ⅰ型错误和功效,但我们观察到,在我们的模拟中,信息增长的错误指定模式的范围内,只有少量的Ⅰ型错误膨胀和适度的功效降低。
考虑到研究设计,并使用正在进行的研究中的组内相关系数估计值或同一群组中的其他数据,应该可以轻松地在未来的阶梯式楔形设计中实施群组序贯方法。