scholarly journals Use of non-linear mixed-effects modelling and regression analysis to predict the number of somatic coliphages by plaque enumeration after 3 hours of incubation

2017 ◽  
Vol 15 (5) ◽  
pp. 706-717 ◽  
Author(s):  
Javier Mendez ◽  
Antonio Monleon-Getino ◽  
Juan Jofre ◽  
Francisco Lucena
Keyword(s):  
Linear Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Suitable Model ◽  
Laboratory Equipment ◽  
Linear Mixed Effects ◽  
Non Linear ◽  
Agar Layer ◽  
Linear Fit ◽  
Working Day

The present study aimed to establish the kinetics of the appearance of coliphage plaques using the double agar layer titration technique to evaluate the feasibility of using traditional coliphage plaque forming unit (PFU) enumeration as a rapid quantification method. Repeated measurements of the appearance of plaques of coliphages titrated according to ISO 10705-2 at different times were analysed using non-linear mixed-effects regression to determine the most suitable model of their appearance kinetics. Although this model is adequate, to simplify its applicability two linear models were developed to predict the numbers of coliphages reliably, using the PFU counts as determined by the ISO after only 3 hours of incubation. One linear model, when the number of plaques detected was between 4 and 26 PFU after 3 hours, had a linear fit of: (1.48 × Counts3 h + 1.97); and the other, values >26 PFU, had a fit of (1.18 × Counts3 h + 2.95). If the number of plaques detected was <4 PFU after 3 hours, we recommend incubation for (18 ± 3) hours. The study indicates that the traditional coliphage plating technique has a reasonable potential to provide results in a single working day without the need to invest in additional laboratory equipment.

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.

scholarly journals Autoregressive Models of the Random fields—A Survey

2020 ◽  
pp. 1-9
Keyword(s):  
Random Fields ◽  
Linear Models ◽  
Estimation Methods ◽  
Suitable Model ◽  
Main Mode ◽  
One Dimensional ◽  
Non Linear ◽  
The Status ◽  
Ar Models ◽  
Shed Light

Autoregressive (AR) random fields are widely use to describe changes in the status of real-physical objects and implemented for analyzing linear & non-linear models. AR models are Markov processes with a higher order dependence for one-dimensional time series. Actually, various estimation methods were used in order to evaluate the autoregression parameters. Although in many applications background knowledge can often shed light on the search for a suitable model, but other applications lack this knowledge and often require the type of trial errors to choose a model. This article presents a brief survey of the literatures related to the linear and non-linear autoregression models, including several extensions of the main mode models and the models developed. The use of autoregression to describe such system requires that they be of sufficiently high orders which leads to increase the computational costs.

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
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