Efficient treatment allocation in two-way nested designs
Efficient treatment allocation in two-way nested designs
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DOI：
10.1177/0962280213502145
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发表时间：
2015-10-01
影响因子：
2.3
通讯作者：
Berger, Martijn P. F.
中科院分区：
文献类型：
--
作者：
Lemme, Francesca;van Breukelen, Gerard J. P.;Berger, Martijn P. F.
聚类随机和多中心试验有时在析因设计中结合A和B两种治疗方法，如A、B、A和B，或无。这导致了双向嵌套设计。通常的样本量和功率问题现在出现在各种临床相关的对比假设中。假设每个水平的总样本量固定（集群或中心的数量，患者的数量），我们得出分配给每个治疗组的总样本的最佳比例。我们首先考虑最高水平的治疗分配（集群随机试验），然后考虑最低水平的治疗分配（多中心试验）。我们推导出各种临床相关假设的最佳分配比例。然后，我们评估了每个分配的效率，并表明除了将一个治疗组与所有其他治疗组进行对比外，流行的平衡设计对于一系列研究问题是最优或高效的。最后，我们给出了在平衡设计中测试每个感兴趣的效应所需的总样本量的简单方程，作为效应大小、功率和I型误差的函数。所有结果都在一项关于小学预防吸烟的集群随机试验和一项关于改善一般生活方式的多中心试验中得到说明。
Cluster randomized and multicenter trials sometimes combine two treatments A and B in a factorial design, with conditions such as A, B, A and B, or none. This results in a two-way nested design. The usual issue of sample size and power now arises for various clinically relevant contrast hypotheses. Assuming a fixed total sample size at each level (number of clusters or centers, number of patients), we derive the optimal proportion of the total sample to be allocated to each treatment arm. We consider treatment assignment first at the highest level (cluster randomized trial) and then at the lowest level (multicenter trial). We derive the optimal allocation ratio for various sets of clinically relevant hypotheses. We then evaluate the efficiency of each allocation and show that the popular balanced design is optimal or highly efficient for a range of research questions except for contrasting one treatment arm with all other treatment arms. We finally present simple equations for the total sample size needed to test each effect of interest in a balanced design, as a function of effect size, power and type I error . All results are illustrated on a cluster-randomized trial on smoking prevention in primary schools and on a multicenter trial on lifestyle improvement in general practices.