Event-driven type design for clinical trials with recurrent events
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复发事件临床试验的事件驱动型设计
- 研究问题
- 复发事件存在受试者内相关性且设计阶段相关信息有限时，如何调整试验监测与停止安排以达到目标检验功效？
- 方法与思路
- 作者在边际发生率或均值模型下，以盲态监测稳健方差构造事件驱动型设计，并通过模拟及真实临床试验数据检查运行特征。
- 材料陈述的贡献
- 摘要称该设计可在其他量设定变化时达到目标功效，模拟中功效受到良好控制且未见严重第一类错误膨胀，并给出数据示例。
- 阅读关注点
- 应核对盲态下稳健方差的计算、监测与停止规则、受试者内相关性的处理，以及反复监测对第一类错误和功效的影响。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明模型条件、监测频率或模拟数值，不能据此认定任何相关结构下均有效。
原文摘要
It is a common practice in randomized clinical trials with the standard survival outcome to follow patients until a prespecified number of events have been observed, a type of trial known as the event-driven trial. The event-driven design ensures that the target power for a specified type 1 error rate is achieved to detect the target hazard ratio, regardless of the specification of other quantities. To understand the treatment effect for chronic conditions, the analysis of recurrent events has gained popularity in randomized controlled trials, particularly large-scale confirmatory trials. In the absence of within-subject correlation among multiple events, a similar event-driven design can be employed for recurrent event outcomes. On the other hand, in the presence of the within-subject correlation, one needs to model the correlation among recurrent events in evaluating power and setting the sample size. However, information useful in modeling the within-subject correlation is limited at the design stage. Failing to consider the correlation properly may lead to underpowered studies. We propose an event-driven type design for recurrent event outcomes. Our method ensures the target power for the target treatment effect, regardless of the specification of other quantities, by monitoring the robust variance under the marginal rates/means model in a blinded manner. We investigate the operating characteristics of the proposed monitoring procedure in simulation studies. The results of simulation studies showed that the proposed blinded monitoring procedure controlled the power well so that the test possessed the target power and did not lead to serious inflation of the type 1 error rate. Furthermore, we illustrate the proposed method using a real clinical trial dataset.