论文标题
在混合对照试验中从外部数据借来的信息的注释，并带有事件的结果
A note on the amount of information borrowed from external data in hybrid controlled trials with time-to-event outcomes
论文作者
论文摘要
在很难让患者参加随机对照试验的情况下，外部数据可以提高效率和可行性。在这种情况下，自适应试验设计可用于通过更新临时分析中的随机比率来减少试验控制组的入学率。更新随机比率需要估计从外部数据中有效借用的信息量，这通常是通过线性近似来完成的。但是，这种线性近似并不总是可靠的估计值，这可能会导致次优的随机比率更新。在本说明中，我们通过模拟指数的事件结果来强调此问题，因为在这种简单的环境中，有一个精确的解决方案可供比较。我们还提出了一个潜在的概括，可以补充更复杂的环境中的线性近似，讨论此概括的挑战，并建议对借入的有效事件数量计算和解释估计的最佳实践。
In situations where it is difficult to enroll patients in randomized controlled trials, external data can improve efficiency and feasibility. In such cases, adaptive trial designs could be used to decrease enrollment in the control arm of the trial by updating the randomization ratio at the interim analysis. Updating the randomization ratio requires an estimate of the amount of information effectively borrowed from external data, which is typically done with a linear approximation. However, this linear approximation is not always a reliable estimate, which could potentially lead to sub-optimal randomization ratio updates. In this note, we highlight this issue through simulations for exponential time-to-event outcomes, because in this simple setting there is an exact solution available for comparison. We also propose a potential generalization that could complement the linear approximation in more complex settings, discuss challenges for this generalization, and recommend best practices for computing and interpreting estimates of the effective number of events borrowed.
