Group sequential methods for interim monitoring of randomized clinical trials with time-lagged outcome
结局延迟随机临床试验期中监测的组序贯方法AI 解读 ↓
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结局延迟随机临床试验期中监测的组序贯方法
- 研究问题
- 随机临床试验结局存在观测延迟时，如何在期中分析利用尚未完整观测者的信息和协变量，提高及时停止试验的效率？
- 方法与思路
- 作者提出一般组序贯框架，以半参数理论构造考虑删失及基线、时变协变量的处理效应估计量和检验统计量。
- 材料陈述的贡献
- 摘要称所提统计量具有独立增量结构，可使用标准软件获得停止边界，并在展示中比常规方法提供更强的早停证据。
- 阅读关注点
- 应核对延迟结局的删失机制、协变量信息如何进入估计、独立增量结论的条件，以及停止边界对整体错误率的控制。
- 阅读边界
- 这是预印本，同行评审未核验；摘要未列出删失和模型条件，也未报告效率增益或实际早停概率的具体数值。
原文摘要
The primary analysis in two-arm clinical trials usually involves inference on a scalar treatment effect parameter; e.g., depending on the outcome, the difference of treatment-specific means, risk difference, risk ratio, or odds ratio. Most clinical trials are monitored for the possibility of early stopping. Because ordinarily the outcome on any given subject can be ascertained only after some time lag, at the time of an interim analysis, among the subjects already enrolled, the outcome is known for only a subset and is effectively censored for those who have not been enrolled sufficiently long for it to be observed. Typically, the interim analysis is based only on the data from subjects for whom the outcome has been ascertained. A goal of an interim analysis is to stop the trial as soon as the evidence is strong enough to do so, suggesting that the analysis ideally should make the most efficient use of all available data, thus including information on censoring as well as other baseline and time-dependent covariates in a principled way. A general group sequential framework is proposed for clinical trials with a time-lagged outcome. Treatment effect estimators that take account of censoring and incorporate covariate information at an interim analysis are derived using semiparametric theory and are demonstrated to lead to stronger evidence for early stopping than standard approaches. The associated test statistics are shown to have the independent increments structure, so that standard software can be used to obtain stopping boundaries.