Statistical surveillance is a noteworthy endeavor in many health‐care 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 health‐care 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, we first investigate the in‐control statistical properties of the u‐chart. Then we set up several alternative monitoring schemes with the same in‐control performances 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), and the ease of use and clarity of interpretation. The results indicate that the exponentially weighted moving average control chart derived under the framework of weighted likelihood ratio test has the best overall performance.

中文翻译：

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卫生监督中泊松计数数据的控制图比较

在许多医疗保健领域，例如流行病学，医院质量，感染控制和患者安全，统计监视是一项值得注意的工作。为了监测医院不良事件，Shewhart u控制图是最常用的方法。在一个可能的问题ü图表显示，在医疗保健应用中，由于不良事件很少发生且样本量不足以使LCL大于零，通常将下限（LCL）通常设置为零。因此，控制图失去了发出改善信号的任何能力。此外，由于机会区域（样本大小）在一段时间内不是恒定的，因此监控方案的控制内和控制外运行长度性能是未知的。在这篇文章中，一个真实的案例，并通过强化模拟研究的基础上，我们第一次调查的在控制统计特性ü-图表。然后，我们建立了几种具有相同控制内性能的替代监控方案，并对它们的失控性能进行了研究和比较。目的是共同考虑确定最合适的控制图：发现意外变化的能力（通常会恶化），测试干预措施的影响的能力（通常是改进）以及易于使用和解释清晰。结果表明，在加权似然比检验框架下得出的指数加权移动平均控制图具有最佳的整体性能。