Department of General Practice, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
NIHR Applied Research Collaboration South West Peninsula (PenARC), University of Exeter, Exeter, UK.
Stat Med. 2021 Nov 20;40(26):5765-5778. doi: 10.1002/sim.9152. Epub 2021 Aug 14.
For cluster randomized trials (CRTs) with a small number of clusters, the matched-pair (MP) design, where clusters are paired before randomizing one to each trial arm, is often recommended to minimize imbalance on known prognostic factors, add face-validity to the study, and increase efficiency, provided the analysis recognizes the matching. Little evidence exists to guide decisions on when to use matching. We used simulation to compare the efficiency of the MP design with the stratified and simple designs, based on the mean confidence interval width of the estimated intervention effect. Matched and unmatched analyses were used for the MP design; a stratified analysis was used for the stratified design; and analyses without and with post-stratification adjustment for factors that would otherwise have been used for restricted allocation were used for the simple design. Results showed the MP design was generally the most efficient for CRTs with 10 or more pairs when the correlation between cluster-level outcomes within pairs (matching correlation) was moderate to strong (0.3-0.5). There was little gain in efficiency for the MP or stratified designs compared to simple randomization when the matching correlation was weak (0.05-0.1). For trials with four pairs of clusters, the simple and stratified designs were more efficient than the MP design because greater degrees of freedom were available for the analysis, although an unmatched analysis of the MP design recovered precision for weak matching correlations. Practical guidance on choosing between the MP, stratified, and simple designs is provided.
对于集群随机试验（CRTs），当集群数量较少时，配对设计（MP）通常被推荐用于最小化已知预后因素的不平衡，为研究增加表面有效性，并提高效率，前提是分析能够识别配对。关于何时使用匹配的决策，几乎没有证据可以指导。我们使用模拟比较了 MP 设计与分层设计和简单设计的效率，基于干预效果估计的平均置信区间宽度。MP 设计使用匹配和非匹配分析；分层设计使用分层分析；简单设计则使用没有和没有对那些原本用于限制分配的因素进行后分层调整的分析。结果表明，当配对内集群水平结局之间的相关性（匹配相关性）为中度至高度（0.3-0.5）时，对于有 10 对或更多对的 CRTs，MP 设计通常是最有效的。当匹配相关性较弱（0.05-0.1）时，MP 或分层设计与简单随机化相比，效率提高很小。对于有四对集群的试验，简单设计和分层设计比 MP 设计更有效，因为分析有更多的自由度，尽管 MP 设计的非匹配分析可以恢复弱匹配相关性的精度。提供了在 MP、分层和简单设计之间进行选择的实用指南。
