Independent increments and group sequential tests
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独立增量与组序贯检验
- 研究问题
- 当临床试验的序贯检验统计量不具有独立增量时，如何构造可用于现有提前停止方法的统计量，并针对特定备择提高检验功效？
- 方法与思路
- 作者对逐次计算的统计量构造序贯线性组合，使其具有独立增量，并讨论针对比例风险、非比例风险及对数优势备择的组合选择。
- 材料陈述的贡献
- 摘要称，该构造适用于不同协方差结构，可对接现有组序贯方法与软件，并针对目标备择获得高于原统计量程序的功效。
- 阅读关注点
- 应核对独立增量结论所需的分布条件、协方差是否已知或需估计，以及组合选择、停止边界和目标备择功效比较的实现。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实；摘要未说明完整概率条件或误差控制细节，不能将其概述直接视为任意有限样本保证。
原文摘要
Widely used methods and software for group sequential tests of a null hypothesis of no treatment difference that allow for early stopping of a clinical trial depend primarily on the fact that sequentially-computed test statistics have the independent increments property. However, there are many practical situations where the sequentially-computed test statistics do not possess this property. Key examples are in trials where the primary outcome is a time to an event but where the assumption of proportional hazards is likely violated, motivating consideration of treatment effects such as the difference in restricted mean survival time or the use of approaches that are alternatives to the familiar logrank test, in which case the associated test statistics may not possess independent increments. We show that, regardless of the covariance structure of sequentially-computed test statistics, one can always derive linear combinations of these test statistics sequentially that do have the independent increments property. We also describe how to best choose these linear combinations to target specific alternative hypotheses, such as proportional or non-proportional hazards or log odds alternatives. We thus derive new, sequentially-computed test statistics that not only have the independent increments property, supporting straightforward use of existing methods and software, but that also have greater power against target alternative hypotheses than do procedures based on the original test statistics, regardless of whether or not the original statistics have the independent increments property. We illustrate with two examples.