Ou Fang-Shu, Heller Martin, Shi Qian
Department of Health Sciences Research, Mayo Clinic Cancer Center, Rochester, MN, USA.
Private Practitioner, Rochester, MN, USA.
Pharm Stat. 2019 Jul;18(4):433-446. doi: 10.1002/pst.1934. Epub 2019 Feb 26.
Predicting the times of milestone events, ie, interim and final analyses in clinical trials, helps resource planning. This manuscript presents and compares several easily implemented methods for predicting when a milestone event is achieved. We show that it is beneficial to combine the predictions from different models to craft a better predictor through prediction synthesis. Furthermore, a Bayesian approach provides a better measure of the uncertainty involved in prediction of milestone events. We compare the methods through two simulations where the model has been correctly specified and where the models are a mixture of three incorrectly specified model classes. We then apply the methods on two real clinical trial data, North Central Cancer Treatment Group (NCCTG) N0147 and N9841. In summary, the Bayesian prediction synthesis methods automatically perform well even when the data collection is far from homogeneous. An R shiny app is under development to carry out the prediction in a user-friendly fashion.
预测里程碑事件的时间，即临床试验中的中期和最终分析，有助于资源规划。本文介绍并比较了几种易于实施的预测里程碑事件实现时间的方法。我们表明，通过预测合成将不同模型的预测结果结合起来以构建更好的预测器是有益的。此外，贝叶斯方法能更好地衡量里程碑事件预测中涉及的不确定性。我们通过两个模拟对这些方法进行比较，一个模拟中模型已正确设定，另一个模拟中模型是由三个设定错误的模型类别混合而成。然后我们将这些方法应用于两个真实的临床试验数据，即北中部癌症治疗组（NCCTG）的N0147和N9841。总之，即使数据收集远非同质，贝叶斯预测合成方法也能自动表现良好。一个R闪亮应用程序正在开发中，以便以用户友好的方式进行预测。
