scholarly journals cosinoRmixedeffects: an R package for mixed-effects cosinor models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruixue Hou ◽  
Lewis E. Tomalin ◽  
Mayte Suárez-Fariñas
Keyword(s):  
Hypothesis Testing ◽  
R Package ◽  
Mixed Effects ◽  
Ease Of Use ◽  
Model Framework ◽  
Mixed Effect ◽  
Non Linear ◽  
Time Varying Covariates ◽  
Circadian Patterns ◽  
Cosinor Models

Abstract Background Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm characteristics. However, the currently available R package cosinor could only analyze daily cross-sectional data and compare the parameters between groups with two levels. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates. Results We developed the cosinoRmixedeffects R package for modelling longitudinal periodic data using mixed-effects cosinor models. The model allows for covariates and interactions with the non-linear parameters MESOR, amplitude, and acrophase. To facilitate ease of use, the package utilizes the syntax and functions of the widely used emmeans package to obtain estimated marginal means and contrasts. Estimation and hypothesis testing involving the non-linear circadian parameters are carried out using bootstrapping. We illustrate the package functionality by modelling daily measurements of heart rate variability (HRV) collected among health care workers over several months. Differences in circadian patterns of HRV between genders, BMI, and during infection with SARS-CoV2 are evaluated to illustrate how to perform hypothesis testing. Conclusion cosinoRmixedeffects package provides the model fitting, estimation and hypothesis testing for the mixed-effects COSINOR model, for the linear and non-linear circadian parameters MESOR, amplitude and acrophase. The model accommodates factors with any number of categories, as well as complex interactions with circadian parameters and categorical factors.

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.

Mixed-effect non-linear modelling for diameter estimation along the stem of Tectona grandis in mid-western Brazil

2018 ◽  
Vol 81 (2) ◽  
pp. 167-173 ◽  
Author(s):  
Luciano Rodrigo Lanssanova ◽  
Sebastião do Amaral Machado ◽  
Alexandre Techy de Almeida Garrett ◽  
Izabel Passos Bonete ◽  
Allan Libanio Pelissari ◽  
...  
Keyword(s):  
Tectona Grandis ◽  
Mixed Effect ◽  
Linear Modelling ◽  
Non Linear

scholarly journals multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

2019 ◽  
Vol 35 (17) ◽  
pp. 2916-2923 ◽  
Author(s):  
John C Stansfield ◽  
Kellen G Cresswell ◽  
Mikhail G Dozmorov
Keyword(s):  
Comparative Analysis ◽  
A Priori ◽  
Three Dimensional ◽  
R Package ◽  
Supplementary Information ◽  
Chromatin Interaction ◽  
Model Framework ◽  
Chromatin Interactions ◽  
Loess Regression ◽  
Sequencing Studies

Abstract Motivation With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. Results Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. Availability and implementation multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. Supplementary information Supplementary data are available at Bioinformatics online.

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 ARTIFICIAL NEURAL NETWORKS AND MIXED-EFFECTS MODELING TO DESCRIBE THE STEM PROFILE OF Pinus taeda L.

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1123
Author(s):  
Izabel Passos Bonete ◽  
Julio Eduardo Arce ◽  
Afonso Figueiredo Filho ◽  
Fabiane Aparecida de Souza Retslaff ◽  
Luciano Rodrigo Lanssanova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pinus Taeda ◽  
Graphical Analysis ◽  
Mixed Effects ◽  
Mixed Effect ◽  
Mixed Effects Modeling ◽  
Artificial Neural ◽  
Pinus Taeda L ◽  
Function Models

The aim of this study was to compare the effectiveness of artificial neural networks (ANNs) and mixed-effects models (MEMs) in describing the stem profile of Pinus taeda L., using sample data from 246 trees. First, three taper functions of different classes were adjusted: non-segmented, segmented, and variable-form. To adjust the models, the nonlinear regression technique (nls) was used. In the best performance equation for nls-adjusted diameter estimates, the nonlinear MEM (nlme) was applied at two levels, using the age class (ci) and DBH class (cd). For this, three different study scenarios were considered, with the number of coefficients with random effects ranging from one to three in each scenario. The adjustments were made using the nls and nlme functions in R software. The selected mixed-effect equations were compared with ANNs generated in Neuro 4.0 software. The taper function models and ANNs were classified according to statistical criteria and graphical analysis of residues. The tapering equation of Bi (2000) presented better performance for diameter estimates than the non-segmented and segmented equations. Application of the nlme technique in the Bi (2000) equation increased the accuracy of the diameter estimates for Pinus taeda, in relation to the adjustment using the nls technique. In the comparison of ANNs with the variations of the Bi equation of mixed-effects, the networks performed better, indicated in the description of the P. taeda profile.

scholarly journals Detecting and Visualizing Outliers in Provider Profiling Using Funnel Plots and Mixed Effects Models—An Example from Prescription Claims Data

2018 ◽  
Vol 15 (9) ◽  
pp. 2015
Author(s):  
Oliver Hirsch ◽  
Norbert Donner-Banzhoff ◽  
Maike Schulz ◽  
Michael Erhart
Keyword(s):  
Health Insurance ◽  
Claims Data ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Federal State ◽  
Mixed Effect ◽  
Provider Profiling ◽  
Mixed Effect Models ◽  
First Case ◽  
Funnel Plots

When prescribing a drug for a patient, a physician also has to consider economic aspects. We were interested in the feasibility and validity of profiling based on funnel plots and mixed effect models for the surveillance of German ambulatory care physicians’ prescribing. We analyzed prescriptions issued to patients with a health insurance card attending neurologists’ and psychiatrists’ ambulatory practices in the German federal state of Saarland. The German National Association of Statutory Health Insurance Physicians developed a prescribing assessment scheme (PAS) which contains a systematic appraisal of the benefit of drugs for so far 12 different indications. The drugs have been classified on the basis of their clinical evidence as “standard”, “reserve” or “third level” medication. We had 152.583 prescriptions in 56 practices available for analysis. A total of 38.796 patients received these prescriptions. The funnel plot approach with additive correction for overdispersion was almost equivalent to a mixed effects model which directly took the multilevel structure of the data into account. In the first case three practices were labeled as outliers, the mixed effects model resulted in two outliers. We suggest that both techniques should be routinely applied within a surveillance system of prescription claims data.

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