背景:使用四个案例研究,我们旨在为整群随机对照试验的分析提供实用的指导和建议.
方法:四种建模方法(具有通过最大似然/受限最大似然估计的参数的广义线性混合模型;具有通过广义估计方程(一阶或二阶)和二次推理函数估计的参数的广义线性模型,为了分析相关的个体参与者水平,我们回顾了文献,确定了整群随机对照试验的结局.我们系统地搜索了MEDLINE的在线参考书目数据库,EMBASE,PsycINFO(通过OVID),CINAHL(通过EBSCO),和SCOPUS。我们确定了上述四种统计分析方法,并将其应用于四个集群随机对照试验的案例研究,集群数量从10到100不等,个体参与者从748到9,207不等。使用R和SAS统计软件包获得连续和二元结果的结果。
结果:病例研究的池内相关系数(ICC)估计值小于0.05,与初级保健和社区整群随机对照试验中常见的观察到的ICC值一致。在大多数情况下,这四种方法产生了相似的结果。然而,在一些分析中,与广义线性混合模型相比,二次推断函数产生了不同的结果,一阶广义估计方程,和二阶广义估计方程,特别是在具有小到中等数量的集群的试验中。
结论:本文展示了使用四种建模方法对集群随机对照试验的分析。在大多数情况下获得的结果相似,然而,对于少数集群的试验,我们建议谨慎使用二次推理函数,并在可能的情况下使用小样本校正。我们的结果的概括性仅限于与我们的案例研究具有相似特征的研究,例如,使用类似大小的ICC进行研究。进行仿真研究以全面评估四种建模方法的性能非常重要。
Using four
case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials.
Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four
case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages.
The intracluster correlation coefficient (ICC) estimates for the
case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters.
This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our
case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.