🤖 AI Summary
This study addresses a key challenge in multi-hypothesis group sequential clinical trials: how to provide informative simultaneous confidence intervals for effective treatment effect estimation while rigorously controlling the family-wise error rate (FWER). The authors propose a novel group sequential testing strategy that bases decisions solely on repeated p-values from the current stage and dynamically enhances significance thresholds by integrating evidence accumulated in prior stages. For the first time, they extend informative simultaneous confidence interval methodology to a graphical group sequential framework, combining the Bonferroni closure principle with repeated p-value methods. An iterative algorithm is developed to compute testing boundaries, complemented by precision assessment criteria and a conservative median estimation technique. The resulting approach maintains strict FWER control while substantially improving statistical power, with only minimal power loss attributable to the confidence intervals, and supports dynamic updating at each interim analysis stage.
📝 Abstract
Test procedures for multiple hypotheses in a group sequential clinical trial that control the family-wise error rate are considered. Several graphical group sequential tests suggested in the literature, which are special cases of Bonferroni-closure tests, are discussed. The focus is on the question of whether to consider at the current stage only the evidence of the current repeated p-value or the evidence over all repeated p-values from the previous stages. A new test strategy controlling the family-wise error rate is introduced that consistently works across all hypotheses, with the evidence (i.e., repeated p-value) from the current stage. The strategy is more powerful than similar previously suggested test procedures. This is achieved by using the evidence from previous stages to increase the significance levels. For the test procedures, corresponding compatible simultaneous confidence intervals are presented, having the disadvantage of often not providing additional information on the treatment effects. For this reason, we extend previous work about informative simultaneous confidence intervals for one-stage graphical tests to graphical group sequential trials. Iterative algorithms are introduced that calculate these informative bounds that have a small power loss compared to the original graphical group sequential test. The boundaries can be calculated after each stage. In addition, previous work is extended by a criterion to estimate the accuracy of the numerically calculated boundaries. The suggested informative bounds can be used to provide median-conservative, i.e., reliable estimators, for estimating the treatment effects in a group sequential test with multiple hypotheses.