Informative Simultaneous Confidence Intervals for Graphical Group Sequential Test Procedures
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图形化群序贯检验的信息性同时置信区间
- 研究问题
- 在多假设群序贯临床试验中，如何利用历次检验信息控制族错误率，并构造与检验兼容、又能提供治疗效应信息的同时置信区间？
- 方法与思路
- 作者以当前阶段重复 p 值进行检验、用既往证据调整显著性水平，并将信息性区间扩展到群序贯图形检验，设计迭代边界算法。
- 材料陈述的贡献
- 摘要称新策略较类似程序更有力，信息性边界仅带来较小功效损失；还提出数值精度判据，并据此形成中位数保守的效应估计。
- 阅读关注点
- 应核对显著性水平传递规则、跨阶段错误率控制、区间与拒绝决策的兼容性，以及边界迭代误差和中位数保守性的定义。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给具体检验条件与功效数值，不能认定所提策略优于所有序贯程序。
原文摘要
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.