A Real Time Monitoring Approach for Bivariate Event Data

TL;DR

This paper introduces a real-time bivariate time-between-events chart for detecting changes in multivariate event data, improving detection speed and accuracy over existing methods, with practical application to AIDS data.

Contribution

The paper presents a novel bivariate TBE chart that signals in real-time and provides analytical control limits, enhancing detection performance without simulation.

Findings

Better detection ability than existing methods

Signals in real-time, reducing delay

Analytical expressions eliminate the need for simulation

Abstract

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.