Abstract
Control charts are a popular statistical process control (SPC) technique for monitoring to detect the unusual variations in different processes. Contrary to the classical charts, control charts have also been modified to include covariates using regression approaches. This study assesses the performance of risk-adjusted control charts under the complexity of estimation error by considering logistic and negative binomial regression models. To be more precise, risk-adjusted Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) charts are used to evaluate the impact of the estimation error. To compute the average run length (ARL), Markov Chain Monte Carlo simulations are conducted. Furthermore, a bootstrap method is also used to compute the ARL assuming different Phase-I data sets to minimize the effect of estimation error on risk-adjusted control charts. The results for cardiac surgery and respiratory disease data sets show that the modified control charts improve the performance in detecting small shifts.
Highlights
Control charts are used to detect any undesirable changes in the process. ere are two main types of control charts: memoryless and memory-type control charts. e memoryless charts are known as the Shewhart charts, and their major drawback is the usage of current information while ignoring the history of the process. is disadvantage makes the Shewhart control chart quite insensitive to small shifts in the process
The Cumulative Sum (CUSUM) [6] and Exponentially Weighted Moving Average (EWMA) [7] control charts are suggested to detect small shifts in the processes [8]. e CUSUM was developed on the basis of the total deviations of successive samples from the target value
The main aim of this study is to assess the impact of the estimation error on the risk-adjusted control charts
Summary
Is disadvantage makes the Shewhart control chart quite insensitive to small shifts in the process To overcome this problem, alternatively, the CUSUM [6] and EWMA [7] control charts are suggested to detect small shifts in the processes [8]. E standard EWMA approach considers equal risk factors for all patients, which makes the method ineffective when monitoring healthcare outcomes. For. Complexity instance, an unexpected increase in the number of failures may be due to the treatment of numerous high risk patients and not because of alternate in the healthcare service. Complexity instance, an unexpected increase in the number of failures may be due to the treatment of numerous high risk patients and not because of alternate in the healthcare service This would result in increased false alarms in the monitoring. Grigg and Spiegelhalter [9] have addressed these issues and developed various promising techniques to include risk factors in process monitoring and control of healthcare processes
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