In event‐driven clinical trials comparing the survival functions of two groups, the number of events required to achieve the desired power is usually calculated using the Freedman formula or the Schoenfeld formula. Then, the sample size and the study duration derived from the required number of events are considered; however, their combination is not uniquely determined. In practice, various combinations are examined considering the enrollment speed, study duration, and the cost of enrollment. However, effective methods for visually representing their relationships and evaluating the uncertainty in study duration are insufficient. We developed a graphical approach for examining the relationship between sample size and study duration. To evaluate the uncertainty in study duration under a given sample size, we also derived the probability density function of the study duration and a method for updating the probability density function according to the observed number of events (ie, information time). The proposed methods are expected to improve the operation and management of clinical trials with a time‐to‐event endpoint.

中文翻译：

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预测具有事件发生时间终点的临床试验中的研究持续时间

在事件驱动的临床试验中，比较两组患者的生存功能，通常使用Freedman公式或Schoenfeld公式计算达到所需功效所需的事件数。然后，考虑从所需事件数得出的样本量和研究持续时间；但是，它们的组合不是唯一确定的。在实践中，考虑了入学速度，学习时间和入学成本，对各种组合进行了检查。但是，以视觉方式表示它们之间的关系并评估研究持续时间的不确定性的有效方法是不够的。我们开发了一种图形方法来检查样本量与研究持续时间之间的关系。为了评估在给定样本量下研究持续时间的不确定性，我们还推导了研究持续时间的概率密度函数，并根据观察到的事件数量（即信息时间）更新了概率密度函数。预期所提出的方法将改善具有事件发生时间终点的临床试验的操作和管理。