Time-to-event outcomes are common in clinical studies. For example, the time to a first major adverse cardiovascular event (MACE, defined as CVD death, nonfatal myocardial infarction, or stroke) is a commonly used outcome in cardiovascular outcome trials. Owing to the lengthy time frame and other factors, the high costs of conducting such studies has been identified as one of the major obstacles in conducting clinical trials in the United States. However, typical approaches for designing clinical trials with time-to-event outcomes do not consider study costs. For a given effect size (eg, hazard ratio), the power to detect differences between two groups is typically a function of the total number of events observed in the study. Therefore, the same level of power will be achieved based on various combinations of the total number of participants, length of enrollment and total follow-up times, and group allocation probability. Herein, we provide a general framework for designing cost-efficient studies comparing treatments with respect to continuous time-to-event outcomes. Among the various designs that achieve the desired level of power to detect a given effect size for a fixed type-I error level, the optimal cost-efficient design is the design that minimizes the expected total study cost. The method is general and can be used for Cox proportional hazards models or Aalen additive models, and under various recruitment and censoring assumptions. The proposed approach for designing cost-efficient studies is illustrated for a Weibull time-to-event outcome with uniform recruitment and exponentially distributed censoring time. The case of an additive hazards model is also described. A Shiny web application implementation of the proposed methods is presented.

中文翻译：

---

具有持续时间生存结果的具有成本效益的临床研究

事件发生时间结果在临床研究中很常见。例如，发生第一次主要不良心血管事件（MACE，定义为 CVD 死亡、非致死性心肌梗死或中风）的时间是心血管结局试验中常用的结局。由于时间长和其他因素，进行此类研究的高成本已被确定为在美国进行临床试验的主要障碍之一。然而，设计具有事件发生时间结果的临床试验的典型方法不考虑研究成本。对于给定的效应大小（例如，风险比），检测两组之间差异的功效通常是研究中观察到的事件总数的函数。因此，将根据参与者总数的各种组合实现相同级别的权力，入组时间和总随访时间，以及组分配概率。在此，我们提供了一个通用框架，用于设计比较治疗与连续事件发生时间相关的具有成本效益的研究。在各种设计中，为了检测固定 I 类错误水平的给定效应大小而达到所需的功效水平，最佳的成本效益设计是最小化预期总研究成本的设计。该方法是通用的，可用于 Cox 比例风险模型或 Aalen 可加模型，以及各种招募和审查假设。为具有统一招募和指数分布审查时间的 Weibull 时间到事件结果说明了用于设计具有成本效益的研究的建议方法。还描述了附加危险模型的情况。