cosinoRmixedeffects: an R package for mixed-effects 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 paramete...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ruixue Hou, Lewis E. Tomalin, Mayte Suárez-Fariñas
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Circadian data Cosinor Mixed-effects Wearable data R package Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b6571d57102d43d1904ac1e86ff6929c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b6571d57102d43d1904ac1e86ff6929c
record_format	dspace
spelling	oai:doaj.org-article:b6571d57102d43d1904ac1e86ff6929c2021-11-14T12:13:12ZcosinoRmixedeffects: an R package for mixed-effects cosinor models10.1186/s12859-021-04463-31471-2105https://doaj.org/article/b6571d57102d43d1904ac1e86ff6929c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04463-3https://doaj.org/toc/1471-2105Abstract 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.Ruixue HouLewis E. TomalinMayte Suárez-FariñasBMCarticleCircadian dataCosinorMixed-effectsWearable dataR packageComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Circadian data Cosinor Mixed-effects Wearable data R package Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Circadian data Cosinor Mixed-effects Wearable data R package Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Ruixue Hou Lewis E. Tomalin Mayte Suárez-Fariñas cosinoRmixedeffects: an R package for mixed-effects cosinor models
description	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.
format	article
author	Ruixue Hou Lewis E. Tomalin Mayte Suárez-Fariñas
author_facet	Ruixue Hou Lewis E. Tomalin Mayte Suárez-Fariñas
author_sort	Ruixue Hou
title	cosinoRmixedeffects: an R package for mixed-effects cosinor models
title_short	cosinoRmixedeffects: an R package for mixed-effects cosinor models
title_full	cosinoRmixedeffects: an R package for mixed-effects cosinor models
title_fullStr	cosinoRmixedeffects: an R package for mixed-effects cosinor models
title_full_unstemmed	cosinoRmixedeffects: an R package for mixed-effects cosinor models
title_sort	cosinormixedeffects: an r package for mixed-effects cosinor models
publisher	BMC
publishDate	2021
url	https://doaj.org/article/b6571d57102d43d1904ac1e86ff6929c
work_keys_str_mv	AT ruixuehou cosinormixedeffectsanrpackageformixedeffectscosinormodels AT lewisetomalin cosinormixedeffectsanrpackageformixedeffectscosinormodels AT maytesuarezfarinas cosinormixedeffectsanrpackageformixedeffectscosinormodels
_version_	1718429368931319808

cosinoRmixedeffects: an R package for mixed-effects cosinor models

Ejemplares similares