Control charts are tools from the field of statistical process control for visualizing the longitudinal development of quality indicators, and detecting whether the underlying process is changing. They have been used in critical care and disease management settings to monitor and improve patient outcomes. This paper investigates the application of control charts to monitor adherence to clinical...

With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Extensions of traditional univariate Shewhart, CUSUM and EWMA control charts to multivariate quality control situations are based on Hotelling's T 2 statist...

November 1, 2015 Version 0.5.2 Date 2015-10-31 Title Statistical Process Control -Collection of Some Useful Functions Author Sven Knoth Maintainer Sven Knoth Depends R (>= 1.8.0) Description Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts un...

The purpose of this study was to retrospectively evaluate the results from a Helical TomoTherapy Hi-Art treatment system relating to quality controls based on daily static and dynamic output checks using statistical process control methods. Individual value X-charts, exponentially weighted moving average charts, and process capability and acceptability indices were used to monitor the treatment...

Control chart is the most important Statistical Process Control tool used to monitor reliability and performance of industrial processes. For monitoring changes in process dispersion, the R and S charts are widely used. These control charts perform better under the ideal assumption of normality but are well known to be very inefficient in presence of outliers or departures from normality. In th...

Attribute control charts (e.g., charts for counts and proportions) have discrete jumps in the quantities plotted on the charts, which means that the chart designer has only a discrete set of unique control limits to choose from when designing a chart. These charts have a small set of in-control Average Run Lengths (ARLs) from which to determine the appropriate control scheme. In a chart with ra...

In some statistical process monitoring applications, quality of a process or product is described by more than one ordinal factors called ordinal multivariate process. To show the relationship between these factors, an ordinal contingency table is used and modeled with ordinal log-linear model. In this paper, a new control charts based on ordinal-normal statistic is developed to monitor the ord...

OBJECTIVE To investigate and illustrate the use of statistical process control (SPC) for prospective monitoring and detection of regional changes in prescription opioid abuse rates. METHODS A demonstration project was conducted by applying several types of SPC charts to Louisiana TANF (Temporary Assistance for Needy Families) applicant self-reported opioid illicit use data collected during 21...