Evaluation of the Long-Term Stability of Metrology Instruments

This chapter aims to emphasize the issue of the long-term stability of instruments used in metrology. This issue is a concern mentioned in the IEC/ISO17025:2017 standard and the JCGM100:2008 guide. Control charts are mentioned in these key documents as tools to assess whether a measurement process is under statistical control or not. Control charts (Shewhart charts, CUSUM chart, EWMA chart) are introduced and tested with simulated and real datasets from metrology instruments that operate at the ionizing department of the BIPM. The interest and the limits of such statistical analysis are discussed. They take their basis in a measurement model composed of Gaussian white noise. Although a measurement monitored over a relatively short period may be consistent with this model, it has been observed that the autocorrelation of the measurement data acquired over a long period limits the relevance of control charts. In this case, time series analysis seems more appropriate than conventional control charts. As an illustration, an optimal Bayesian smoother is introduced to demonstrate how to deconvolve the low-frequency random noise and refine the evaluation of uncertainty according to the measurement model for long-term measurement.

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.

Learning Curve for Endovascular Treatment of Anterior Circulation Large Vessel Occlusion at a Single Center

Background and purpose: Data concerning the learning curve for endovascular treatment (EVT) of anterior circulation large vessel occlusion are scarce. This study aimed to investigate the relationship between operator experience and the outcome of EVT and to further identify the number of cases needed to acquire the ability to perform successful reperfusion.Materials and methods: Four hundred and thirty-four patients who underwent EVT by seven operators at a single center from January 2016 to September 2019 were enrolled. Procedural experience was defined by the number of cases performed by each operator. Multivariable backward regression analyses were used to investigate the association between procedural experience and functional independence (defined as a modified Rankin Scale score of 0–2), 90-days mortality, successful reperfusion (defined as a modified Thrombolysis in Cerebral Infarction score of 2b-3), and puncture-to-reperfusion time after adjusting for covariates. A risk-adjusted cumulative sum (RA-CUSUM) chart was utilized to identify the number of caseloads needed to overcome the learning curve effect.Results: Procedural experience was independently associated with functional independence, 90-days mortality, successful reperfusion, and puncture-to-reperfusion time reduction (per 10-case increment: OR 1.219, 95% CI: 1.079–1.383, P < 0.001; OR 0.847, 95% CI: 0.738–0.968, P = 0.016; OR 1.553, 95% CI: 1.332–1.830, P < 0.001 and β 8.087 min, 95% CI: 6.184–9.991, P < 0.001, respectively). The RA-CUSUM chart indicated that at least 29 cases were required to overcome the learning curve effect.Conclusions: There was a dose-response relationship between operator case volume and clinical outcome, procedure time, and successful reperfusion. The experience needed for successful EVT was at least 29 cases.

Multichart Schemes for Detecting Changes in Disease Incidence

Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.

Cumulative Sum Chart Modeled under the Presence of Outliers

Cumulative sum control charts that are based on the estimated control limits are extensively used in practice. Such control limits are often characterized by a Phase I estimation error. The presence of these errors can cause a change in the location and/or width of control limits resulting in a deprived performance of the control chart. In this study, we introduce a non-parametric Tukey’s outlier detection model in the design structure of a two-sided cumulative sum (CUSUM) chart with estimated parameters for process monitoring. Using Monte Carlo simulations, we studied the estimation effect on the performance of the CUSUM chart in terms of the average run length and the standard deviation of the run length. We found the new design structure is more stable in the presence of outliers and requires fewer amounts of Phase I observations to stabilize the run-length performance. Finally, a numerical example and practical application of the proposed scheme are demonstrated using a dataset from healthcare surveillance where received signal strength of individuals’ movement is the variable of interest. The implementation of classical CUSUM shows that a shift detection in Phase II that received signal strength data is indeed masked/delayed if there are outliers in Phase I data. On the contrary, the proposed chart omits the Phase I outliers and gives a timely signal in Phase II.