Estimating insulin sensitivity from glucose levels only: use of a non-linear mixed effects approach and Bayesian estimation

2010 ◽  
Author(s):  
J.W.T. Yates ◽  
E.M. Watson
Keyword(s):  
Insulin Sensitivity ◽  
Bayesian Estimation ◽  
Mixed Effects ◽  
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

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
Sign in / Sign up

Export Citation Format

Share Document