Biostatistics Core, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Center for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada.
BMC Med Res Methodol. 2023 Sep 12;23(1):206. doi: 10.1186/s12874-023-02027-y.
Stepped-wedge cluster randomized trials (SWCRTs) are a type of cluster-randomized trial in which clusters are randomized to cross-over to the active intervention sequentially at regular intervals during the study period. For SWCRTs, sequential imbalances of cluster-level characteristics across the random sequence of clusters may lead to biased estimation. Our study aims to examine the effects of balancing cluster-level characteristics in SWCRTs.
To quantify the level of cluster-level imbalance, a novel imbalance index was developed based on the Spearman correlation and rank regression of the cluster-level characteristic with the cross-over timepoints. A simulation study was conducted to assess the impact of sequential cluster-level imbalances across different scenarios varying the: number of sites (clusters), sample size, number of cross-over timepoints, site-level intra-cluster correlation coefficient (ICC), and effect sizes. SWCRTs assumed either an immediate "constant" treatment effect, or a gradual "learning" treatment effect which increases over time after crossing over to the active intervention. Key performance metrics included the relative root mean square error (RRMSE) and relative mean bias.
Fully-balanced designs almost always had the highest efficiency, as measured by the RRMSE, regardless of the number of sites, ICC, effect size, or sample sizes at each time for SWCRTs with learning effect. A consistent decreasing trend of efficiency was observed by increasing RRMSE as imbalance increased. For example, for a 12-site study with 20 participants per site/timepoint and ICC of 0.10, between the most balanced and least balanced designs, the RRMSE efficiency loss ranged from 52.5% to 191.9%. In addition, the RRMSE was decreased for larger sample sizes, larger number of sites, smaller ICC, and larger effect sizes. The impact of pre-balancing diminished when there was no learning effect.
The impact of pre-balancing on preventing efficiency loss was easily observed when there was a learning effect. This suggests benefit of pre-balancing with respect to impacting factors of treatment effects.
阶梯式整群随机临床试验（SWCRTs）是一种整群随机临床试验，其中在研究期间以固定间隔顺序将整群随机分配到交叉到主动干预。对于 SWCRTs，随着随机序列中整群的顺序，集群级特征的连续不平衡可能导致有偏差的估计。我们的研究旨在检验 SWCRTs 中平衡集群级特征的效果。
为了量化集群级不平衡的程度，根据集群级特征与交叉时间点的 Spearman 相关和秩回归，开发了一种新的不平衡指数。进行了一项模拟研究，以评估在不同场景下顺序集群级不平衡对以下方面的影响：站点（集群）数量、样本量、交叉时间点数量、站点内聚类相关系数（ICC）和效应大小。SWCRTs 假设立即存在“恒定”治疗效果，或者在交叉到主动干预后随时间逐渐增加的“学习”治疗效果。关键绩效指标包括相对均方根误差（RRMSE）和相对平均偏差。
完全平衡的设计几乎总是具有最高的效率，这可以通过 RRMSE 来衡量，无论 SWCRTs 中学习效果的站点数量、ICC、效应大小或每个时间点的样本大小如何。随着不平衡程度的增加，效率呈一致的递减趋势。例如，对于一项 12 个站点的研究，每个站点/时间点有 20 名参与者，ICC 为 0.10，在最平衡和最不平衡的设计之间，RRMSE 效率损失范围从 52.5%到 191.9%。此外，随着样本量增加、站点数量增加、ICC 减小和效应大小增加，RRMSE 减小。当没有学习效果时，预平衡的影响会降低。
当存在学习效果时，预平衡对防止效率损失的影响很容易观察到。这表明了预平衡在影响治疗效果的因素方面的好处。
