Accounting for Context in Randomized Trials after Assignment.
Accounting for Context in Randomized Trials after Assignment.
复制标题
DOI：
10.1007/s11121-022-01426-9
复制
发表时间：
2022-11
影响因子：
3.5
通讯作者：
Wyman, Peter A
中科院分区：
文献类型：
--
作者：
Brown, C Hendricks;Hedeker, Donald;Gibbons, Robert D;Duan, Naihua;Almirall, Daniel;Gallo, Carlos;Burnett-Zeigler, Inger;Prado, Guillermo;Young, Sean D;Valido, Alberto;Wyman, Peter A
许多预防性试验将个体随机分配到干预条件下，然后在一组环境中进行。其他试验随机选择更高级别的组织，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。其他试验随机化或扩展现有的社交网络，并使用关键意见领袖通过这些网络进行干预。我们使用上下文驱动一词来泛指此类试验（传统上称为聚类，其中分组在随机化前或随机化后形成-即，分组随机试验），因为这些分组或网络提供了固定或随时间变化的背景，在理论上和实际上都对提供干预措施很重要。虽然这种情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方案，但它们都需要适当考虑非独立性的分析程序，这并不总是受到赞赏。许多已发表的预防试验分析都没有考虑到这一点。我们讨论了不同类型的上下文驱动的设计，然后表明，即使是少量的非独立性可以膨胀实际的I类错误率。这种膨胀导致我们过于频繁地拒绝零假设，并错误地导致我们得出结论，认为干预措施之间存在显着差异，而实际上它们并不存在。我们描述了一个程序，以占非独立性的重要情况下，一个两组试验，随机单位的个人或组织在两个武器，然后提供积极的治疗，在一个手臂通过分组后形成的。我们提供了多种编程语言的示例代码，以指导分析师，区分不同的上下文驱动的设计，并总结对多个受众的影响。 在线版本包含补充材料，可通过10.1007/s11121-022-01426-9获得。
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. The online version contains supplementary material available at 10.1007/s11121-022-01426-9.
影响因子：
2
作者：
Andridge, Rebecca R.;Shoben, Abigail B.;Muller, Keith E.;Murray, David M.
通讯作者：
Murray, David M.