In many trials, the duration between patient enrolment and an event occurring is used as the efficacy endpoint. Common endpoints of this type include the time until relapse, progression to the next stage of a disease, or time until remission. The criteria of an event may be defined by multiple components, one or more of which may be a continuous measurement being above or below a threshold. Typical analyses consider all components as binary variables and record the first time at which the patient has an event. This is analysed through constructing and testing survival functions using Kaplan–Meier, parametric models or Cox models. This approach ignores information contained in the continuous components. We propose a method that makes use of this information to improve the precision of analyses using these types of endpoints. We use joint modelling of the continuous and binary components to construct survival curves. We show how to compute confidence intervals for quantities of interest, such as the median or mean event time. We assess the properties of the proposed method using simulations and data from a phase II cancer trial and an observational study in renal disease.

中文翻译：

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当事件涉及跨越阈值的连续变量时，有效分析事件发生时间端点


在许多试验中，患者入组和事件发生之间的持续时间被用作疗效终点。这种类型的常见终点包括复发前的时间、疾病进展到下一阶段的时间或缓解前的时间。事件的标准可以由多个组件来定义，其中一个或多个组件可以是高于或低于阈值的连续测量。典型的分析将所有成分视为二元变量，并记录患者第一次发生事件的时间。这是通过使用 Kaplan-Meier、参数模型或 Cox 模型构建和测试生存函数来进行分析的。这种方法忽略了连续分量中包含的信息。我们提出了一种利用这些信息来提高使用这些类型的端点进行分析的精度的方法。我们使用连续和二元分量的联合建模来构建生存曲线。我们展示了如何计算感兴趣的数量的置信区间，例如中值或平均事件时间。我们使用 II 期癌症试验和肾脏疾病观察研究的模拟和数据来评估所提出方法的特性。