title: Information-based sample size re-estimation in group sequential design for longitudinal trials.
abstract: Group sequential design has become more popular in clinical trials because it allows for trials to stop early for futility or efficacy to save time and resources. However, this approach is less well-known for longitudinal analysis. We have observed repeated cases of studies with longitudinal data where there is an interest in early stopping for a lack of treatment effect or in adapting sample size to correct for inappropriate variance assumptions. We propose an information-based group sequential design as a method to deal with both of these issues. Updating the sample size at each interim analysis makes it possible to maintain the target power while controlling the type I error rate. We will illustrate our strategy with examples and simulations and compare the results with those obtained using fixed design and group sequential design without sample size re-estimation.
authors.affiliation: Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, U.S.A.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhou
authors.forename: Adeniyi
authors.initials: A
authors.lastname: Adewale
authors.forename: Yue
authors.initials: Y
authors.lastname: Shentu
authors.forename: Jiajun
authors.initials: J
authors.lastname: Liu
authors.forename: Keaven
authors.initials: K
authors.lastname: Anderson
mesh_terms: D002986:Clinical Trials as Topic; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size
keywords: adaptive design; group sequential design; information; longitudinal data analysis; sample size re-estimation