Each week, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
The article featured today is from Applied Stochastic Models in Business and Industry, with the full article now available to read in Early View here.
Comparison of control charts for Poisson count data in health‐care monitoring. Appl Stochastic Models Bus Ind. 2020; 1– 16. https://doi.org/10.1002/asmb.2560, , .
Statistical surveillance is a noteworthy endeavor in many healthcare areas such as epidemiology, hospital quality, infection control and patient safety. For monitoring hospital adverse events the Shewhart u-control chart is the most used methodology. One possible issue of the u-chart is that in healthcare applications the lower control limit (LCL) is often conventionally set to zero as the adverse events are rare and the sample sizes are not sufficiently large to obtain LCL greater than zero. Consequently, the control chart loses any ability to signal improvements. Furthermore, as the area of opportunity (sample size) is not constant over time, the in-control and out-of-control run length performances of the monitoring scheme are unknown. In this article, on the basis of a real case and through an intensive simulation study, the in-control statistical properties of the u-chart are investigated. Then, several alternative monitoring schemes, with the same in-control performances, are set up and their out-of-control properties are studied and compared. The aim is to identify the most suitable control chart considering jointly: the ability to detect unexpected changes (usually worsening), the ability to test the impact of interventions (usually improvements), the ease of use and clarity of interpretation. The results indicate that the EWMA control chart derived under the framework of weighted likelihood ratio test has the best overall performance.