Optimal permutation tests for the analysis of group randomized trials

• 成果类型: Article; Proceedings Paper
• 署名作者: Braun, TM; Feng, ZD
• 署名单位: University of Michigan System; University of Michigan; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214501753382336
• 发表日期: 2001
• 页码: 1424-1432
• 关键词: GENERALIZED LINEAR-MODELS; intraclass correlation; inference; DESIGN; parameters; BEHAVIOR

摘要

Two facts complicate the comparison of interventions in group randomized trials (GRT's), a family of clinical trials in which each member of a particular group receives the same treatment assignment. First, individual outcomes within each group are often correlated. Second, the number of groups in a GRT is often not sufficient to make asymptotic approximations possible. Therefore, the tests used with methods, such as generalized estimating equations (GEE) and penalized quasi likelihood (PQL), originally developed for longitudinal studies, may not be valid. As an alternative, a class of permutation tests is derived to maximize the power of testing for an intervention effect in a GRT while maintaining a nominal test size. The test uses a statistic that is a weighted sum of residuals, with the weights based on the group sizes and the variability of each individual outcome. Through simulation, we demonstrate the importance of weights to a permutation test's power and compare the power of permutation tests, GEE, and PQL. Last, we apply our methods to an actual GRT to study smoking cessation, discuss the findings based on permutation tests, and compare those findings with traditional methods.