在事件驱动的临床试验中预测事件发生的高级模型，考虑患者脱落、治愈和正在进行的招募
Advanced models for predicting event occurrence in event-driven clinical trials accounting for patient dropout, cure and ongoing recruitment
摘要：
我们考虑事件驱动的临床试验，一旦达到预定数量的临床事件，就进行分析。例如，这些事件可能是肿瘤学进展或心血管试验中的卒中。在中期阶段，主要任务之一是预测一段时间内的事件数量和达到特定里程碑的时间，我们需要考虑不仅可能发生在已招募和随访患者中的事件，还可能发生在尚未招募的患者中的事件。因此，在此类试验中，我们需要同时对患者招募和事件计数进行建模。在本文中，我们开发了一种新的分析方法，该方法考虑了患者被治愈的机会，以及他们辍学和失访的机会。招聘建模使用泊松-伽马模型在以前的出版物。当考虑事件的发生时，我们假设到主要事件的时间和到退出的时间是独立的随机变量，并且我们已经开发了一些使用指数分布、威布尔分布和对数正态分布的具有治愈的高级模型。这种技术得到了良好开发、测试和记录的软件的支持。使用模拟和参考开发的软件的真实数据集的结果进行了说明。
英文摘要：
We consider event-driven clinical trials, where the analysis is performed once a pre-determined number of clinical events has been reached. For example, these events could be progression in oncology or a stroke in cardiovascular trials. At the interim stage, one of the main tasks is predicting the number of events over time and the time to reach specific milestones, where we need to account for events that may occur not only in patients already recruited and are followed-up but also in patients yet to be recruited. Therefore, in such trials we need to model patient recruitment and event counts together. In the paper we develop a new analytic approach which accounts for the opportunity of patients to be cured, as well as for them to dropout and be lost to follow-up. Recruitment is modelled using a Poisson-gamma model developed in previous publications. When considering the occurrence of events, we assume that the time to the main event and the time to dropout are independent random variables, and we have developed a few advanced models with cure using exponential, Weibull and log-normal distributions. This technique is supported by well developed, tested and documented software. The results are illustrated using simulation and a real dataset with reference to the developed software.
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