School of Population and Public Health, University of British Columbia, 2206 E Mall, Vancouver, BC, V6T 1Z3, Canada.
Centre for Health Evaluation and Outcome Sciences, 588-1081 Burrard Street, St Paul's Hospital, Vancouver, BC, V6Z 1Y6, Canada.
BMC Med Res Methodol. 2020 Jun 24;20(1):166. doi: 10.1186/s12874-020-01036-5.
In a cross-sectional stepped-wedge trial with unequal cluster sizes, attained power in the trial depends on the realized allocation of the clusters. This attained power may differ from the expected power calculated using standard formulae by averaging the attained powers over all allocations the randomization algorithm can generate. We investigated the effect of design factors and allocation characteristics on attained power and developed models to predict attained power based on allocation characteristics.
Based on data simulated and analyzed using linear mixed-effects models, we evaluated the distribution of attained powers under different scenarios with varying intraclass correlation coefficient (ICC) of the responses, coefficient of variation (CV) of the cluster sizes, number of cluster-size groups, distributions of group sizes, and number of clusters. We explored the relationship between attained power and two allocation characteristics: the individual-level correlation between treatment status and time period, and the absolute treatment group imbalance. When computational time was excessive due to a scenario having a large number of possible allocations, we developed regression models to predict attained power using the treatment-vs-time period correlation and absolute treatment group imbalance as predictors.
The risk of attained power falling more than 5% below the expected or nominal power decreased as the ICC or number of clusters increased and as the CV decreased. Attained power was strongly affected by the treatment-vs-time period correlation. The absolute treatment group imbalance had much less impact on attained power. The attained power for any allocation was predicted accurately using a logistic regression model with the treatment-vs-time period correlation and the absolute treatment group imbalance as predictors.
In a stepped-wedge trial with unequal cluster sizes, the risk that randomization yields an allocation with inadequate attained power depends on the ICC, the CV of the cluster sizes, and number of clusters. To reduce the computational burden of simulating attained power for allocations, the attained power can be predicted via regression modeling. Trial designers can reduce the risk of low attained power by restricting the randomization algorithm to avoid allocations with large treatment-vs-time period correlations.
在不等群组大小的交叉阶段楔形试验中，试验的实际功效取决于群组的实际分配。该实际功效可能与使用标准公式计算的预期功效不同，可通过对随机分配算法生成的所有分配的实际功效进行平均来实现。我们研究了设计因素和分配特征对实际功效的影响，并根据分配特征开发了预测实际功效的模型。
我们基于线性混合效应模型模拟和分析的数据，评估了在不同情况下实际功效的分布，这些情况的反应内相关系数（ICC）、群组大小的变异系数（CV）、群组大小分组数量、群组大小分布和群组数量各不相同。我们探讨了实际功效与两种分配特征之间的关系：治疗状态与时间段之间的个体水平相关性，以及绝对治疗组不平衡性。当由于一个场景的可能分配数量过多而导致计算时间过长时，我们开发了回归模型，使用治疗与时间段的相关性和绝对治疗组不平衡性作为预测因子来预测实际功效。
随着 ICC 或群组数量的增加以及 CV 的降低，实际功效低于预期或名义功效 5%的风险降低。实际功效受到治疗与时间段相关性的强烈影响。绝对治疗组不平衡性对实际功效的影响较小。使用包含治疗与时间段相关性和绝对治疗组不平衡性作为预测因子的逻辑回归模型可以准确预测任何分配的实际功效。
在不等群组大小的阶段楔形试验中，随机分配产生实际功效不足的分配的风险取决于 ICC、群组大小的 CV 和群组数量。为了降低模拟分配实际功效的计算负担，可以通过回归建模来预测实际功效。试验设计者可以通过限制随机分配算法来避免使用具有较大治疗与时间段相关性的分配，从而降低实际功效低的风险。
