A mixed-effect state space model to environmental data

Costa, Marco; Monteiro, Magda

doi:10.1063/1.4912409

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…85 To consider the inherent differences of patients in multistage therapeutic processes, Funatogawa et al, 86,87 Funatogawa et al, 88 and Funatogawa and Funatogawa 89 tried to model such processes using autoregressive (AR) models taking into consideration the mixed effects. Also, Funatogawa and Funatogawa, 90 Liu et al, 91 Zhou et al, 92 and Costa and Monteiro 93 proposed models based on mixed-effects with a linear state space approach for the therapeutic processes considering the latent variable. However, since the mixed effects approach considers the inherent differences of patients randomly and does not spot the effects of each patient's risk on the outcomes, the use of such an approach can be misleading.…”

Section: Introductionmentioning

confidence: 99%

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

Rasouli

Noorossana

Samimi

2022

Quality & Reliability Eng

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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.

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