title: Adaptive graph-based multiple testing procedures.
abstract: Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations.
authors.affiliation: Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
authors.forename: Florian
authors.initials: F
authors.lastname: Klinglmueller
authors.forename: Martin
authors.initials: M
authors.lastname: Posch
authors.forename: Franz
authors.initials: F
authors.lastname: Koenig
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D012107:Research Design / Q000706:statistics & numerical data*
keywords: adaptive design; graphical approach; multiple comparisons; multiple endpoints; partial conditional error rate; treatment selection