ABSTRACT Recent evidence suggests ever-increasing applications of statistical process control (SPC) in health data analysis. However, the diversity in numbers and types of included variables warrant new statistical control charts. This inclusion can be improved by profiles that monitor a describing functional relationship of the process. In this article, we proposed multiple generalized additive models (GAMs) for profile construction. GAMs permit complex fitting models with simultaneous inclusions of parametric and nonparametric terms. Therefore, GAMs can be applied in health data monitoring with a wide range of explanatory variables. We used two statistics to build control charts: (1) a commonly used univariate statistic in nonparametric profiles; (2) a new proposed multivariate statistic which enables the chart to track the role of each included element in the process changes. The statistics are compared according to their performance in monitoring monthly stroke types, including ischaemic and haemorrhagic strokes of patients with acute stroke in the Mashhad Stroke Incidence Study. Features of the proposed profile are discussed and suggestions are made about the utilized statistics in process monitoring. The results show the successful performance of GAMs in profile monitoring.

中文翻译：

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一种使用广义添加剂配置文件监测医疗保健绩效的新方法

摘要 最近的证据表明，统计过程控制 (SPC) 在健康数据分析中的应用不断增加。然而，包含变量的数量和类型的多样性需要新的统计控制图。可以通过监视描述过程的功能关系的配置文件来改进这种包含。在本文中，我们为配置文件构建提出了多个广义可加模型 (GAM)。GAM 允许同时包含参数和非参数项的复杂拟合模型。因此，GAM 可以应用于具有广泛解释变量的健康数据监测。我们使用两个统计量来构建控制图：(1) 非参数剖面中常用的单变量统计量；(2) 新提议的多变量统计量，使图表能够跟踪每个包含的元素在流程变化中的作用。根据马什哈德卒中发病率研究中他们在监测每月卒中类型（包括急性卒中患者的缺血性和出血性卒中）方面的表现，对统计数据进行比较。讨论了提议的配置文件的特征，并就过程监控中使用的统计数据提出了建议。结果表明 GAM 在剖面监测中的成功表现。讨论了提议的配置文件的特征，并就过程监控中使用的统计数据提出了建议。结果表明 GAM 在剖面监测中的成功表现。讨论了提议的配置文件的特征，并就过程监控中使用的统计数据提出了建议。结果表明 GAM 在剖面监测中的成功表现。