Estimating insulin sensitivity from glucose levels only: Use of a non-linear mixed effects approach and maximum a posteriori (MAP) estimation

2013 ◽  
Vol 109 (2) ◽  
pp. 134-143 ◽  
Author(s):  
James W.T. Yates ◽  
Edmund M. Watson
Keyword(s):  
Insulin Sensitivity ◽  
Mixed Effects ◽  
Maximum A Posteriori ◽  
Map Estimation ◽  
A Posteriori ◽  
Linear Mixed Effects ◽  
Glucose Levels ◽  
Non Linear

scholarly journals The power of modelling pulsatile profiles

2021 ◽  
Author(s):  
Michiel J. van Esdonk ◽  
Jasper Stevens
Keyword(s):  
Growth Hormone ◽  
Statistical Power ◽  
Quantitative Description ◽  
Mixed Effects ◽  
Post Dose ◽  
Deconvolution Analysis ◽  
Linear Mixed Effects ◽  
Non Linear ◽  
Over Time ◽  
Modelling Approach

AbstractThe quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

A meta-analysis using a logit non-linear mixed effects model for ‘Hass’ avocado postharvest performance data

2013 ◽  
Vol 86 ◽  
pp. 134-140 ◽  
Author(s):  
Jeremy Burdon ◽  
Patrick Connolly ◽  
Nihal de Silva ◽  
Nagin Lallu ◽  
Jonathan Dixon ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Mixed Effects ◽  
Performance Data ◽  
Mixed Effects Model ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Non Linear ◽  
Hass Avocado

scholarly journals Continuous Finger Gesture Recognition Based on Flex Sensors

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3986 ◽  
Author(s):  
Wei-Chieh Chuang ◽  
Wen-Jyi Hwang ◽  
Tsung-Ming Tai ◽  
De-Rong Huang ◽  
Yun-Jie Jhang
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Maximum A Posteriori ◽  
Map Estimation ◽  
A Posteriori ◽  
Sensory Data ◽  
Robust Recognition ◽  
Flex Sensors ◽  
Gated Recurrent Unit ◽  
Gesture Spotting

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.

Pharmacokinetic/Pharmacodynamic Modellingof GnRH Antagonist Degarelix: A Comparisonof the Non-linear Mixed-Effects Programs NONMEM and NLME

2004 ◽  
Vol 31 (6) ◽  
pp. 441-461 ◽  
Author(s):  
Christoffer W. Tornøe ◽  
Henrik Agersø ◽  
Henrik A. Nielsen ◽  
Henrik Madsen ◽  
E. Niclas Jonsson
Keyword(s):  
Gnrh Antagonist ◽  
Mixed Effects ◽  
Linear Mixed Effects ◽  
Non Linear
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