Department of Statistics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.
Department of Statistics, University of Wah, Taxila, Pakistan.
Medicine (Baltimore). 2024 Sep 13;103(37):e39328. doi: 10.1097/MD.0000000000039328.
Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring.
最近的研究结果表明，医疗保健领域内数据分析的使用统计过程控制呈现出增长趋势。然而，所涉及的变量多样性需要扩展新的过程控制方法。本研究通过使用广义加性模型（GAMs）构建轮廓，同时涉及多个医疗保健变量（08），考察了控制图在心脏病学中的应用。两种不同的统计量：偏差（D）和霍特林（T2）用于构建控制图：一种常用的单变量统计量用于非参数轮廓，一种创新的多元统计量用于评估每个元素对过程变化的贡献。这些统计量用于监测 1 年内急性中风（369）患者的缺血性和出血性中风。模型中包括人口统计学参数（年龄、性别）、血管危险因素（糖尿病、家族史、睡眠）、社会经济变量（吸烟、位置）和血压。研究包括计算零状态平均运行长度（ARL），以评估控制图的性能。分析了拟议轮廓的特征，例如 T2 控制图，对于中到大的偏移（δ≥0.50），其性能优于 D 图。另一方面，对于小的 δ=0.25，D 控制图产生较小的 ARL 值，但更大的标准偏差。虽然这两种统计量都有助于轮廓监测，但 T2 更有效地识别和跟踪中大和大的偏移。总之，这些便捷的工具可能有助于医疗保健绩效监测，特别是对于复杂的预测-响应关系。监测轮廓表明 GAMs 可用于医疗保健分析和过程监测。
