Performance of the hotelling T2 control chart for compositional data in the presence of measurement errors

Monitoring multistage healthcare processes using state space models and a machine learning based framework

Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.

Investigating zero-state and steady-state performance of MEWMA-CoDa control chart using variable sampling interval

Traditional process monitoring control charts (CCs) focused on sampling methods using fixed sampling intervals (FSI s). The variable sampling intervals (VSI s) scheme is receiving increasing attention, in which the sampling interval (SI ) length varies according to the process monitoring statistics. A shorter SI is considered when the process quality indicates the possibility of an out-of-control (OOC) situation; otherwise, a longer SI is preferred. The VSI multivariate exponentially moving average for compositional data (VSI -MEWMA CoDa ) CC based on a coordinate representation using isometric log-ratio (ilr ) transformation is proposed in this study. A methodology is proposed to obtain the optimal parameters by considering the zero-state (ZS ) average time to signal (ZATS ) and the steady-state (SS) average time to signal (SATS ). The statistical performance of the proposed CC is evaluated based on a continuous-time Markov chain (CTMC ) method for both cases, the ZS and the SS using a fixed value of in-control (IC) ATS 0 . Simulation results demonstrate that the VSI -MEWMA CoDa CC has significantly decreased the OOC average time to signal (ATS ) than the FSI MEWMA CoDa CC. Moreover, it is found that the number of variables (d) has a negative impact on the ATS of the VSI -MEWMA CoDa CC, and the subgroup size (n) has a mildly positive impact on the ATS of the VSI -MEWMA CoDa CC. At the same time, the SATS of the VSI -MEWMA CoDa CC is less than the ZATS of the VSI -MEWMA CoDa CC for all the values of n and d. The proposed VSI -MEWMA CoDa CC under steady-State performs effectively compared to its competitors, such as the FSI -MEWMA CoDa CC, the VSI -T 2 CoDa CC and the FSI -T 2 CoDa CC. An example of an industrial problem from a plant in Europe is also given to study the statistical significance of the VSI -MEWMA CoDa CC.

Monitoring coefficient of variation using one-sided run rules control charts in the presence of measurement errors

We investigate, in this paper, the effect of the measurement error (ME) on the performance of Run Rules control charts monitoring the coefficient of variation (CV) squared. The previous Run Rules CV chart in the literature is improved slightly by monitoring the CV squared using two one-sided Run Rules charts instead of monitoring the CV itself using a two-sided chart. The numerical results show that this improvement gives better performance in detecting process shifts. Moreover, we will show through simulation that the precision and accuracy errors do have a negative effect on the performance of the proposed Run Rules charts. We also find out that taking multiple measurements per item is not an effective way to reduce these negative effects. The proposed Run Rules control charts can be applied in the anomaly detection area.

A combined mixed-s-skip sampling strategy to reduce the effect of autocorrelation on the X̄ scheme with and without measurement errors

In order to reduce the effect of autocorrelation on theX ¯ monitoring scheme, a new sampling strategy is proposed to form rational subgroup samples of size n. It requires sampling to be done such that: (i) observations from two consecutive samples are merged, and (ii) some consecutive observations are skipped before sampling. This technique which is a generalized version of the mixed samples strategy is shown to yield a better reduction of the negative effect of autocorrelation when monitoring the mean of processes with and without measurement errors. For processes subjected to a combined effect of autocorrelation and measurement errors, the proposed sampling technique, together with multiple measurement strategy, yields an uniformly better zero-state run-length performance than its two main existing competitors for any autocorrelation level. However, in steady-state mode, it yields the best performance only when the monitoring process is subject to a high level of autocorrelation, for any given level of measurement errors. A real life example is used to illustrate the implementation of the proposed sampling strategy.