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Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial
Trials ( IF 2.0 ) Pub Date : 2021-09-15 , DOI: 10.1186/s13063-021-05590-1
Ahmed A Al-Jaishi 1, 2, 3 , Stephanie N Dixon 1, 3, 4, 5 , Eric McArthur 3 , P J Devereaux 2 , Lehana Thabane 2 , Amit X Garg 1, 2, 3, 4
Affiliation  

Some parallel-group cluster-randomized trials use covariate-constrained rather than simple randomization. This is done to increase the chance of balancing the groups on cluster- and patient-level baseline characteristics. This study assessed how well two covariate-constrained randomization methods balanced baseline characteristics compared with simple randomization. We conducted a mock 3-year cluster-randomized trial, with no active intervention, that started April 1, 2014, and ended March 31, 2017. We included a total of 11,832 patients from 72 hemodialysis centers (clusters) in Ontario, Canada. We randomly allocated the 72 clusters into two groups in a 1:1 ratio on a single date using individual- and cluster-level data available until April 1, 2013. Initially, we generated 1000 allocation schemes using simple randomization. Then, as an alternative, we performed covariate-constrained randomization based on historical data from these centers. In one analysis, we restricted on a set of 11 individual-level prognostic variables; in the other, we restricted on principal components generated using 29 baseline historical variables. We created 300,000 different allocations for the covariate-constrained randomizations, and we restricted our analysis to the 30,000 best allocations based on the smallest sum of the penalized standardized differences. We then randomly sampled 1000 schemes from the 30,000 best allocations. We summarized our results with each randomization approach as the median (25th and 75th percentile) number of balanced baseline characteristics. There were 156 baseline characteristics, and a variable was balanced when the between-group standardized difference was ≤ 10%. The three randomization techniques had at least 125 of 156 balanced baseline characteristics in 90% of sampled allocations. The median number of balanced baseline characteristics using simple randomization was 147 (142, 150). The corresponding value for covariate-constrained randomization using 11 prognostic characteristics was 149 (146, 151), while for principal components, the value was 150 (147, 151). In this setting with 72 clusters, constraining the randomization using historical information achieved better balance on baseline characteristics compared with simple randomization; however, the magnitude of benefit was modest.

中文翻译:

在平衡基线特征方面与协变量约束随机化方法相比简单:在集群试验中随机分配 72 个血液透析中心的案例研究

一些平行组整群随机试验使用协变量约束而不是简单随机化。这样做是为了增加在集群和患者级别基线特征上平衡组的机会。本研究评估了与简单随机化相比,两种受协变量约束的随机化方法平衡基线特征的效果。我们在 2014 年 4 月 1 日开始到 2017 年 3 月 31 日结束了一项没有主动干预的模拟 3 年整群随机试验。我们共纳入了来自加拿大安大略省 72 个血液透析中心(群)的 11,832 名患者。我们使用 2013 年 4 月 1 日之前可用的个人和集群级别数据,在单个日期以 1:1 的比例将 72 个集群随机分配到两组。最初,我们使用简单随机化生成了 1000 个分配方案。然后,作为替代方案,我们根据这些中心的历史数据进行了协变量约束随机化。在一项分析中,我们限制了一组 11 个个体水平的预后变量;另一方面,我们限制了使用 29 个基线历史变量生成的主成分。我们为协变量约束随机化创建了 300,000 个不同的分配,并且我们将我们的分析限制为基于惩罚标准化差异的最小总和的 30,000 个最佳分配。然后我们从 30,000 个最佳分配中随机抽取了 1000 个方案。我们将每种随机化方法的结果总结为平衡基线特征的中位数(第 25 和第 75 个百分位数)数。有 156 个基线特征,当组间标准化差异≤10%时,一个变量被平衡。在 90% 的抽样分配中,三种随机化技术至少具有 156 个平衡基线特征中的 125 个。使用简单随机化的平衡基线特征的中位数为 147 (142, 150)。使用 11 个预后特征的协变量约束随机化的相应值为 149 (146, 151),而对于主成分,该值为 150 (147, 151)。在这个有 72 个集群的设置中,与简单随机化相比,使用历史信息约束随机化在基线特征上实现了更好的平衡;然而,收益的幅度不大。使用简单随机化的平衡基线特征的中位数为 147 (142, 150)。使用 11 个预后特征的协变量约束随机化的相应值为 149 (146, 151),而对于主成分,该值为 150 (147, 151)。在这个有 72 个集群的设置中,与简单随机化相比,使用历史信息约束随机化在基线特征上实现了更好的平衡;然而,收益的幅度不大。使用简单随机化的平衡基线特征的中位数为 147 (142, 150)。使用 11 个预后特征的协变量约束随机化的相应值为 149 (146, 151),而对于主成分,该值为 150 (147, 151)。在这个有 72 个集群的设置中,与简单随机化相比,使用历史信息约束随机化在基线特征上实现了更好的平衡;然而,收益的幅度不大。与简单随机化相比,使用历史信息约束随机化在基线特征上实现了更好的平衡;然而,收益的幅度不大。与简单随机化相比,使用历史信息约束随机化在基线特征上实现了更好的平衡;然而,收益的幅度不大。
更新日期:2021-09-16
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