Risk-adjusted monitoring of time to event
事件发生时间的风险调整监测AI 解读 ↓
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事件发生时间的风险调整监测
- 研究问题
- 如何利用事件发生时间模型开展风险调整后的持续监测，并评估比例与非比例变化以及初始预置对报警表现的影响？
- 方法与思路
- 作者以受控与失控状态的部分似然比构造 CUSUM，并在比例备择下解析计算停止前事件数期望及给定事件数前的停止概率。
- 材料陈述的贡献
- 摘要称初始预置对阈值的影响取决于所用准则，生存分析监测在模拟中优于较简单方案，并展示零售金融和医疗应用。
- 阅读关注点
- 应核对风险调整模型、受控和失控状态的定义、事件数与日历时间的联系，以及不同报警准则下阈值的校准方法。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要。解析结果的具体条件和模拟设置未说明，风险调整也不等同于金融尾部风险分析。
原文摘要
Recently there has been interest in risk-adjusted cumulative sum charts, CUSUMs, to monitor the performance of e.g. hospitals, taking into account the heterogeneity of patients. Even though many outcomes involve time, only conventional regression models are commonly used. In this article we investigate how time to event models may be used for monitoring purposes. We consider monitoring using CUSUMs based on the partial likelihood ratio between an out-of-control state and an in-control state. We consider both proportional and non-proportional alternatives, as well as a head start. Against proportional alternatives, we present an analytic method of computing the expected number of observed events before stopping or the probability of stopping before a given observed number of events. In a stationary set-up, the former is roughly proportional to the average run length in calendar time. Adding a head start changes the threshold only slightly if the expected number of events until hitting is used as a criterion. However, it changes the threshold substantially if a false alarm probability is used. In simulation studies, charts based on survival analysis perform better than simpler monitoring schemes. We present one example from retail finance and one medical application.