Repeated significance testing in longitudinal clinical trials

• 成果类型: Article
• 署名作者: Lee, SJ; Kim, K; Tsiatis, AA
• 署名单位: University of Michigan System; University of Michigan; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/83.4.779
• 发表日期: 1996
• 页码: 779789
• 关键词: GENERALIZED LINEAR-MODELS; multivariate observations; sequential-methods; BOUNDARIES; parameters; discrete

摘要

When longitudinal clinical trials are monitored, multiplicity from repeated significance testing as well as from repeated measures has to be accounted for properly to control the overall type I error. This often involves a multidimensional integration procedure to compute group sequential boundaries. We establish an independent increments structure of sequentially computed test statistics based on the generalised estimating equations of Liang & Zeger (1986) for longitudinal data. This simplifies the computational procedure for group:sequential boundaries to one involving recursive one-dimensional integrations and allows the use of standard methodology for group sequential tests. We also apply the error spending function approach of Lan & DeMets (1983) by defining information fraction in this setting.