Design and analysis of group-randomized trials: A review of recent developments
Design and analysis of group-randomized trials: A review of recent developments
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DOI：
10.1016/s1047-2797(97)80009-9
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发表时间：
1997-10-01
影响因子：
5.6
通讯作者：
Murray, DM
中科院分区：
文献类型：
--
作者：
Murray, DM
目的：本文将回顾组随机试验的设计和分析问题，并总结影响这些问题的最新进展。方法：分组随机试验包括分配可识别的组而不是个体来研究条件；例子包括整个社区、工作场所、学校和诊所。测量这些群体的成员，以评估干预措施的影响；例子包括住院医生、雇员、学生和病人。结果：可识别组的分配保证了同一组成员的观察结果之间存在一定程度的相关性。这种类内相关性违背了独立误差的假设，而独立误差是临床试验中使用的大多数分析方法的基础。在群体随机试验中使用这些方法可能导致研究人员夸大他们的发现的重要性，往往是严重的。许多注意力都集中在作为适当的分析单位的组上。然而，这既不是有效分析的必要条件，也不是充分条件。在某些条件下，这样的分析可能具有高度膨胀的I型错误率。在其他条件下，忽略组的分析可能具有5%的第一类错误率。结论：应注意根据试验设计，将分析模型与数据的基本结构仔细匹配。分析模型将需要反映所有可测量的变化来源以及群体和成员之间和内部的变化模式。并提出了具体的建议。(C) 1997爱思唯尔科学有限公司
PURPOSE: This paper will review the design and analysis issues in group-randomized trials and summarize recent developments that affect those issues.METHODS: Group-randomized trials involve the allocation of identifiable groups instead of individuals to study conditions; examples include whole communities, worksites, schools, and clinics. Members of those groups are measured to assess the impact of an intervention; examples include residents, employees, students, and patients.RESULTS: The allocation of identifiable groups guarantees some level of correlation among the observations taken from members of the same group. That intraclass correlation violates the assumption of independent errors that underlies most of the analysis methods used in clinical trials. Use of those methods in group-randomized trials can lead the investigators to overstate the significance of their findings, often badly. Much attention has been focused on the group as the proper unit of analysis. However, that is neither a necessary nor a sufficient condition for a valid analysis. Under certain conditions, such an analysis can have a highly inflated Type I error rate. Under other conditions, an analysis that ignores the group can have the nominal 5% Type I error rate.CONCLUSIONS: Attention should be focused on careful matching of the analytic model to the underlying structure of the data, as dictated by the design of the trial. The analytic model will need to reflect all measurable sources of variation as well as the pattern of variation both between and within groups and members. Specific recommendations are presented in the paper. (C) 1997 Elsevier Science Inc.