Likert Pain Score Modeling: A Markov Integer Model and an Autoregressive Continuous Model

El, Plan; Jp, Elshoff; Stockis, Armel; Ml, Sargentini-Maier; Mo, Karlsson

doi:10.1038/clpt.2011.301

Cited by 24 publications

(23 citation statements)

References 41 publications

(39 reference statements)

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“…The baseline MET (MET 0 ) was estimated to be 10.8 h; a linear increase in MET with a typical slope (Slope MET ) estimated to be 0 . 7 4 0 w e e k − 1 s i g n i fi c a n t l y i m p r o v e d m o d e l fi t (dOFV = −539) and was consistent with the time-dependent increase in score stability (9). The probability of lower pain scores was found to increase with time (θ time = − 0.141 week −1 ) and decrease with acetaminophen intake (θ ace =1.84).…”

Section: Example 3: Likert Pain Scorementioning

confidence: 63%

“…Patients were allowed to take rescue medicine (acetaminophen) during the study, and the intake was recorded daily. Available data, demographic description, and covariate representation are presented elsewhere (9).…”

Section: Example 3: Likert Pain Scorementioning

confidence: 99%

“…For computational reasons, application of DTMM for variables with large number of categories (typically more than six) may not always be feasible. In this case, although alternative models accounting for serial correlations have been proposed (9), modelers often treat the outcome as a continuous variable, thereby ignoring the true discrete nature of the data.…”

Section: Introductionmentioning

confidence: 99%

“…Through this assumption and a reparameterization, the Markov property can be expressed through the MET and the remaining parameters can be expressed as typically done for PO models. The application of mCTMM and its comparison to PO, DTMM, and count models are then illustrated by three real data examples: fatigue data and hand-foot syndrome (HFS) data from sunitinib-treated gastrointestinal stromal tumor (GIST) patients (7) and Likert pain score data in patients with neuropathic pain (9).…”

Section: Introductionmentioning

confidence: 99%

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A Minimal Continuous-Time Markov Pharmacometric Model

Schindler

Karlsson

2017

AAPS J

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Abstract.In this work, an alternative model to discrete-time Markov model (DTMM) or standard continuous-time Markov model (CTMM) for analyzing ordered categorical data with Markov properties is presented: the minimal CTMM (mCTMM). Through a CTMM reparameterization and under the assumption that the transition rate between two consecutive states is independent on the state, the Markov property is expressed through a single parameter, the mean equilibration time, and the steady-state probabilities are described by a proportional odds (PO) model. The mCTMM performance was evaluated and compared to the PO model (ignoring Markov features) and to published Markov models using three real data examples: the four-state fatigue and hand-foot syndrome data in cancer patients initially described by DTMM and the 11-state Likert pain score data in diabetic patients previously analyzed with a count model including Markovian transition probability inflation. The mCTMM better described the data than the PO model, and adequately predicted the average number of transitions per patient and the maximum achieved scores in all examples. As expected, mCTMM could not describe the data as well as more flexible DTMM but required fewer estimated parameters. The mCTMM better fitted Likert data than the count model. The mCTMM enables to explore the effect of potential predictive factors such as drug exposure and covariates, on ordered categorical data, while accounting for Markov features, in cases where DTMM and/or standard CTMM is not applicable or conveniently implemented, e.g., non-uniform time intervals between observations or large number of categories.

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Section: Example 3: Likert Pain Scorementioning

confidence: 63%

Section: Example 3: Likert Pain Scorementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Minimal Continuous-Time Markov Pharmacometric Model

Schindler

Karlsson

2017

AAPS J

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“…These computerized gait analysis systems need high standard technical equipment and specialized technicians. Pain can be assessed by the Visual Analog Scale (VAS) or by categorized scales like the Likert Scale (Plan et al 2012 ). For pain assessment in children, categorized scales are preferably used.…”

Section: Outcome Measurementmentioning

confidence: 99%

Principles of Nerve and Muscle Rehabilitation

Paternostro-Sluga

Quittan

2014

Atlas of Neuromuscular Diseases

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A Breathable and Stretchable Metastructure for a Versatile Hybrid Electronic Skin Patch with Long‐Term Skin Comfort

Hwang

Kim

Park

et al. 2022

Adv Materials Technologies

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neuroengineering, [10] human-machine interfaces, [11,12] and smart prosthetics. [13] Monitoring of bio-signals by electrical means enables an integration of the electronic skin (E-skin) sensor with big data, [14] artificial intelligence, [15] and internet of things (IoT) technologies. [16] As the applications of on-skin devices expand, novel approaches to realize wearable electronics on non-conventional substrates such as 3D freeform surfaces, skin, and topographic substrates have been reported. [8,17,18] Moreover, additional attractive functionalities of E-skin have been also demonstrated. For example, implementing optical functionality to visualize information related to health conditions is an attractive direction in terms of intuitive interaction with humans. [19] Wearable sensors with selfpowering functionality can also extend their applicability. [20,21] A wireless E-skin system that transmits the measured data to mobile devices and enables daily activities while monitoring health conditions is also attractive in terms of user convenience. [22] Moreover, the possible functionality to transmit only key necessary information out of the bio-signals is a particularly attractive direction for the E-skin sensor in that it can reduce the power consumption for wireless data transmission and the number of data for post-processing.With increasingly diverse functionalities of electronic skins (E-skins), components and structures for the E-skin have also become more diverse and complex. It is extremely challenging to make all the components and devices required for additional functionalities stretchable and breathable to ensure skin comfort. Herein, we report a facile strategy to realize a versatile hybrid E-skin patch with great skin comfort by developing a breathable and stretchable metastructure to serve as the platform material of the hybrid E-skin patch. A Kagome-based mechanical metastructure made of breathable, stretchable medical adhesive integrates and tethers non-stretchable or stiff components and devices to the skin, allowing for both the breathability and mechanical comfort of skin. A wireless skin sensor system to sense electrocardiogram (ECG) signals and wirelessly transmit ECG signals in an event-driven manner such as sending R peaks only is developed on a polyimide-based flexible printed circuit board. The Kagome metastructure-tethered wireless ECG sensor patch does not cause significant skin discomfort when worn for five days, and successfully enables the event-driven wireless monitoring of ECG signals. We envision that this facile and versatile approach expands the type of materials and functionalities of E-skin for digital healthcare, personalized medicine, and smart prosthetics with emerging functionalities.

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Likert Pain Score Modeling: A Markov Integer Model and an Autoregressive Continuous Model

Cited by 24 publications

References 41 publications

A Minimal Continuous-Time Markov Pharmacometric Model

A Minimal Continuous-Time Markov Pharmacometric Model

Principles of Nerve and Muscle Rehabilitation

A Breathable and Stretchable Metastructure for a Versatile Hybrid Electronic Skin Patch with Long‐Term Skin Comfort

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