Early detection of changes in the frequency of events is an important task, in, for example, disease surveillance, monitoring of high-quality processes, reliability monitoring and public health. In this article, we focus on detecting changes in multivariate event data, by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited in the sense that, they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time to signal), especially if it is of interest to detect a change in one or a few of the processes. We propose a bivariate TBE (BTBE) chart which is able to signal in real time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. The findings showed that our method is a realistic approach to monitor bivariate time-between-event data, and has better detection ability than existing methods. A large benefit of our method is that it signals in real-time and that due to the analytical expressions no simulation is needed. The proposed method is implemented on a real-life dataset related to AIDS.
翻译:早期发现事件频率的变化是一项重要任务,例如在疾病监测、高质量过程的监测、可靠性监测和公共卫生方面。在本条中,我们的重点是通过监测活动之间的时间间隔(TBE)来检测多变事件数据的变化。现有的多变TBE图表是有限的,因为它们仅在每个过程发生事件之后才能发出信号。这导致延迟(即长时间的信号),特别是如果它有兴趣发现一个或几个过程的变化。我们提出了一个双变TBE(BTBE)图表,它能够实时发出信号。我们为控制限度和平均时间对信号的性能提供分析表达,进行绩效评估,并将我们的图表与现有方法进行比较。研究结果表明,我们的方法是一种现实的方法,可以监测两变事件之间数据,比现有方法更能探测。我们方法的一大好处是,它能够实时发出信号,而且由于分析表达方式而无需进行模拟。我们提出的艾滋病方法是在现实生命数据上实施的。