Conditional Inference for Secondary Outcomes Based on the Testing Result for the Primary Outcome in Clinical Trials
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基于临床试验主要结局检验结果的次要结局条件推断
- 研究问题
- 当次要结局分析取决于主要结局显著性或试验提前终止时，如何构造在这些选择事件条件下覆盖率正确的置信区间？
- 方法与思路
- 作者利用选择过程诱导的截断正态分布构造枢轴量并反演区间，结合理论、模拟和 SPRINT 数据分析评估方法。
- 材料陈述的贡献
- 摘要强调多重检验校正未必保证条件覆盖率，并称所提区间在 SPRINT 平均处理效应分析中更保守且具有条件有效性。
- 阅读关注点
- 应核对选择事件如何表达、正态分布依据和相关参数处理，并区分条件覆盖率、无条件覆盖率与族错误率控制。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明正态近似和选择建模条件，不能确认任意试验中的条件有效性。
原文摘要
In clinical trials, inferences on clinical outcomes are often made conditional on specific selective processes. For instance, only when a treatment demonstrates a significant effect on the primary outcome, further analysis is conducted to investigate its efficacy on selected secondary outcomes. Similarly, inferences may also depend on whether a trial is terminated early at interim stage. While conventional approaches primarily aim to control the family-wise error rate through multiplicity adjustments, they do not necessarily ensure the desired statistical property of the inference result, when the analysis is conducted according to a selective process. For example, the conditional coverage level of a regular confidence interval under a selective processes can be very different from its nominal level even after adjustment for multiple testing. In this paper, we argue that the validity of the inference procedure conditional on selective process is important in many applications. In addition, we propose to construct confidence intervals with correct conditional coverage probability by accounting for related selective process. Specifically, our approach involves a pivotal quantity constructed by inversing the cumulative distribution function of a truncated normal distribution induced by the selective process. Theoretical justification and comprehensive simulations illustrate the effectiveness of this method in realistic settings. We also apply our method to analyze data from the SPRINT, resulting in more conservative but conditionally valid confidence intervals for the average treatment effect than those originally published.