Studies with group treatments required special power calculations, allocation methods, and statistical analyses
Studies with group treatments required special power calculations, allocation methods, and statistical analyses
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DOI：
10.1016/j.jclinepi.2011.05.007
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发表时间：
2012-02-01
影响因子：
7.2
通讯作者：
Borm, George F.
中科院分区：
文献类型：
--
作者：
Faes, Miriam C.;Reelick, Miriam F.;Borm, George F.
目的：在一些试验中，干预措施以小组形式提供给个人，例如，一起锻炼的小组。这类试验的组结构在分析中必须考虑到，并且对试验的有效性有影响。我们的目的是为此类试验的设计和分析提供最佳方法。研究设计和环境：我们描述了各种治疗分配方法，并提出了一种新的分配算法：最优批次最小化（OBM）。我们进行了一项模拟研究，以评估无限制随机化、分层、排列块随机化、确定性最小化和OBM的性能。此外，我们描述了适当的分析方法，并推导出计算研究规模的公式。结果：分层、确定性最小化和OBM的不平衡风险明显低于无限制随机化和排列块随机化。此外，OBM导致不可预测的治疗分配。研究的样本量计算和分析必须建立在考虑试验群体结构的多层次模型的基础上。结论：评估后续组干预措施的试验需要调整治疗分配、功效计算和分析方法。从获得整体平衡的角度出发，我们得出最小化是选择的方法。当影响预后的因素较少时，分层是一个很好的选择。OBM在批内实现了更好的平衡，但它更复杂。在有许多预后因素的试验中，这可能是最有价值的。从可预测性的角度来看，一种治疗分配方法，如OBM，同时分配几个受试者，优于其他方法，因为它导致的可预测性最低。(C) 2012爱思唯尔公司版权所有。
Objective: In some trials, the intervention is delivered to individuals in groups, for example, groups that exercise together. The group structure of such trials has to be taken into consideration in the analysis and has an impact on the power of the trial. Our aim was to provide optimal methods for the design and analysis of such trials.Study Design and Setting: We described various treatment allocation methods and presented a new allocation algorithm: optimal batchwise minimization (OBM). We carried out a simulation study to evaluate the performance of unrestricted randomization, stratification, permuted block randomization, deterministic minimization, and OBM. Furthermore, we described appropriate analysis methods and derived a formula to calculate the study size.Results: Stratification, deterministic minimization, and OBM had considerably less risk of imbalance than unrestricted randomization and permuted block randomization. Furthermore, OBM led to unpredictable treatment allocation. The sample size calculation and the analysis of the study must be based on a multilevel model that takes the group structure of the trial into account.Conclusion: Trials evaluating interventions that are carried out in subsequent groups require adapted treatment allocation, power calculation, and analysis methods. From the perspective of obtaining overall balance, we conclude that minimization is the method of choice. When the number of prognostic factors is low, stratification is an excellent alternative. OBM leads to better balance within the batches, but it is more complicated. It is probably most worthwhile in trials with many prognostic factors. From the perspective of predictability, a treatment allocation method, such as OBM, that allocates several subjects at the same time, is superior to other methods because it leads to the lowest possible predictability. (C) 2012 Elsevier Inc. All rights reserved.