Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Center for Health Statistics, The University of Chicago, Chicago, IL, USA.
Prev Sci. 2022 Nov;23(8):1321-1332. doi: 10.1007/s11121-022-01426-9. Epub 2022 Sep 9.
Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.
许多预防试验将个体随机分配到干预条件下，然后在小组环境中实施。其他试验则随机分配更高层次的单位，例如组织，然后使用由多个组织组成的学习协作来支持改进的实施或维持。其他试验随机分配或扩展现有的社交网络，并利用关键意见领袖通过这些网络提供干预措施。我们使用“受环境驱动”一词来泛指此类试验（传统上称为聚类，其中分组是在随机分组之前或之后形成的，即整群随机试验），因为这些分组或网络提供了固定或随时间变化的环境，这些环境在干预措施的实施中具有理论和实际意义。虽然这种受环境驱动的试验可以提供高效和有效的方法来提供和评估预防计划，但它们都需要分析程序，以适当考虑非独立性，而这一点并不总是被理解。许多预防试验的已发表分析都没有考虑到这一点。我们讨论了不同类型的受环境驱动的设计，然后表明，即使少量的非独立性也会使实际的Ⅰ型错误率膨胀。这种膨胀导致过于频繁地拒绝零假设，并错误地导致我们得出干预措施之间存在显著差异的结论，而实际上并不存在这种差异。我们描述了一种在两臂试验的重要情况下考虑非独立性的程序，该试验在两个臂中随机分配个体或组织单位，然后在分配后通过形成的小组在一个臂中提供积极的治疗。我们提供了多种编程语言的示例代码，以指导分析师、区分不同的受环境驱动的设计，并总结对多个受众的影响。
