Abstract

This study compared three approaches for handling a fourth level of nesting when analyzing cluster-randomized trial (CRT) data. Although CRT data analyses may include repeated measures, individual, and cluster levels, there may be an additional fourth level that is typically ignored. This study examined the impact of ignoring this fourth level, accounting for it using a model-based approach, and accounting for it using a design-based approach on parameter and standard error (SE) estimates. Several fixed effect and random effect variance parameters and SEs were biased across all three models. Results suggest if a meaningful fourth level exists, researchers should acknowledge it using a design-based approach. If the fourth level is not practically important, researchers may ignore it altogether, resulting in more accurate parameter and SE estimates.

摘要

本研究比较了在分析集群随机试验 (CRT) 数据时处理第四级嵌套的三种方法。尽管 CRT 数据分析可能包括重复测量、个体和集群级别，但可能还有一个通常被忽略的附加第四级。本研究检验了忽略这第四层的影响，使用基于模型的方法对其进行解释，并使用基于设计的参数和标准误差 ( SE ) 估计方法对其进行解释。几个固定效应和随机效应方差参数和SEs 在所有三个模型中都有偏差。结果表明，如果存在有意义的第四层，研究人员应该使用基于设计的方法来承认它。如果第四级实际上并不重要，研究人员可能会完全忽略它，从而得到更准确的参数和SE估计。