Predicting study duration in clinical trials with a time-to-event endpoint
Predicting study duration in clinical trials with a time-to-event endpoint
复制标题
通过事件终点时间预测临床试验的研究持续时间
DOI：
10.1002/sim.8911
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发表时间：
2021
影响因子：
2
通讯作者：
Machida R. Fujii Y. Sozu T
中科院分区：
文献类型：
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作者：
Toko Yukako;Sato-Ilic Mika;Machida R. Fujii Y. Sozu T
在比较两组生存函数的事件驱动临床试验中，通常使用Freedman公式或Schoenfeld公式计算达到预期把握度所需的事件数量。然后，考虑从所需事件数量得出的样本量和研究持续时间;然而，它们的组合不是唯一确定的。在实践中，考虑到入学速度，研究持续时间和入学成本，对各种组合进行了检查。然而，有效的方法来直观地表示它们之间的关系，并评估研究持续时间的不确定性是不够的。我们开发了一种图形方法来检查样本量和研究持续时间之间的关系。为了评估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，以及根据观测事件数（即信息时间）更新概率密度函数的方法。预计所提出的方法将改善具有至事件时间终点的临床试验的操作和管理。
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.