Monitoring multistage multivariate therapeutic processes using risk‐adjusted model‐based group multivariate EWMA control chart

Abstract

There exist multitude of therapeutic processes and the results are commonly observed during various dependent steps. For studying such processes that are referred to as multistage therapeutic processes, two concepts are of particular importance; risk adjustment and cascade property. To monitor such processes, a variety of control charts including model‐based control charts are used. In order to design model‐based control charts, analysts must first recognize a suitable model to identify the multistage processes by considering process risks and cascade property. Based on the identified model, the control charts can be proposed. In this study, a risk‐adjusted time‐variant linear state space model is introduced. Afterward, the model order and its parameters are estimated based on Hankel singular value decomposition (HSVD) and prediction error minimization (PEM) methods. Then, the group multivariate exponentially weighted moving average (GMEWMA) control chart is used to monitor a multistage multivariate therapeutic process. To evaluate the performance of the model‐based control chart, a simulation study as well as a two‐stage thyroid cancer surgery was used. Results show that the proposed control chart performs well for predicting and monitoring of multistage multivariate therapeutic processes in real world.

Monitoring profiles in multistage processes using the multivariate multiple regression model

“…The multistage manufacturing process has been surveyed by Lowry and Montgomery, 14 Woodall and Montgomery, 15,16 Stoumbos et al., 17 and Tsung et al 18 . Furthermore, there have been many applications of the multistage manufacturing process such as Kim and May, 19 Djurdjanovic and Ni, 20 Zhou et al., 21 Zeng and Zhou, 22 Park and Tsui, 23 Wang and Tsung, 24 Wang and Shi, 25 and Rasouli et al 26 . The multistage service process has been studied by Apte and Reynolds, 27 Mehring, 28 Sulek et al., 29 and Ündey and Çinar 30 …”

“…We do not make any distinction between the prior input and the in-process input, but collectively call them the input variables. The multistage manufacturing process has been surveyed by Lowry and Montgomery, 14 Woodall and Montgomery, 15,16 Stoumbos et al, 17 and Tsung et al 18 Furthermore, there have been many applications of the multistage manufacturing process such as Kim and May, 19 Djurdjanovic and Ni, 20 Zhou et al, 21 Zeng and Zhou, 22 Park and Tsui, 23 Wang and Tsung, 24 Wang and Shi, 25 and Rasouli et al 26 The multistage service process has been studied by Apte and Reynolds, 27 Mehring, 28 Sulek et al, 29 and Ündey and Çinar. 30 In each stage of the multistage process, there may be multiple input variables and multiple output quality variables, which are correlated with each other.…”