School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.
Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS One. 2021 Jul 29;16(7):e0255389. doi: 10.1371/journal.pone.0255389. eCollection 2021.
In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes-the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.
在整群随机试验（cluster-randomized trial，CRT）中，各群组的入组人数通常不同。在试验设计和数据分析过程中，都应考虑这种变异性，以确保实现统计学性能目标。大多数关于 CRT 设计的方法学文献都假设了相等的群组大小。本范围综述重点关注不等群组大小 CRT 的方法学。检索了 EMBASE、Medline、Google Scholar、MathSciNet 和 Web of Science 数据库，以确定截至 2021 年 3 月发表的关于不等群组大小 CRT 方法学的英文文章。我们提取了关于论文重点（功效计算、I 类错误等）、CRT 类型、研究的参数类型和范围（群组数量、群组平均大小、群组大小变异系数、组内相关系数等）以及主要结论的数据。在 5032 篇确定的论文中，有 79 篇符合纳入标准。论文主要集中在平行臂 CRT（parallel-arm CRT，p-CRT，n=60，76%）和阶梯式楔形 CRT（stepped-wedge CRT，n=14，18%）上。大约 75%的论文涉及试验设计问题（样本量/功效计算），而 25%的论文则侧重于分析考虑因素（I 类错误、偏差等）。不同研究中探索的参数值范围差异很大。对于正态分布和二分类结局，已经广泛研究了在 p-CRT 中考虑不等群组大小的方法。由于不等群组大小对输入参数组合和范围的影响幅度差异很大，因此综合这些工作的结果非常困难。对于其他 CRT 设计与结局类型的组合，特别是涉及二分类结局（临床试验中最常用的主要结局类型）的方法学，进行的研究有限。p-CRT 以外具有正态分布或二分类结局的方法学论文很少，这突出表明需要进一步发展方法学来填补空白。
