Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Abstract Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the rec...

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Autores principales:	Ashenafi A. Yirga, Sileshi F. Melesse, Henry G. Mwambi, Dawit G. Ayele
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/56219bb02d8a4539848d9b3009bc4dd4
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spelling	oai:doaj.org-article:56219bb02d8a4539848d9b3009bc4dd42021-12-02T19:12:35ZAdditive quantile mixed effects modelling with application to longitudinal CD4 count data10.1038/s41598-021-97114-92045-2322https://doaj.org/article/56219bb02d8a4539848d9b3009bc4dd42021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97114-9https://doaj.org/toc/2045-2322Abstract Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the recent computational advances and the ready availability of efficient linear programming algorithms. Recently, quantile regression has also been extended to additive mixed-effects models, providing an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency. This study applies the additive quantile mixed model to analyze the longitudinal CD4 count of HIV-infected patients enrolled in a follow-up study at the Centre of the AIDS Programme of Research in South Africa. The objective of the study is to justify how the procedure developed can obtain robust nonlinear and linear effects at different conditional distribution locations. With respect to time and baseline BMI effect, the study shows a significant nonlinear effect on CD4 count across all fitted quantiles. Furthermore, across all fitted quantiles, the effect of the parametric covariates of baseline viral load, place of residence, and the number of sexual partners was found to be major significant factors on the progression of patients’ CD4 count who had been initiated on the Highly Active Antiretroviral Therapy study.Ashenafi A. YirgaSileshi F. MelesseHenry G. MwambiDawit G. AyeleNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ashenafi A. Yirga Sileshi F. Melesse Henry G. Mwambi Dawit G. Ayele Additive quantile mixed effects modelling with application to longitudinal CD4 count data
description	Abstract Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the recent computational advances and the ready availability of efficient linear programming algorithms. Recently, quantile regression has also been extended to additive mixed-effects models, providing an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency. This study applies the additive quantile mixed model to analyze the longitudinal CD4 count of HIV-infected patients enrolled in a follow-up study at the Centre of the AIDS Programme of Research in South Africa. The objective of the study is to justify how the procedure developed can obtain robust nonlinear and linear effects at different conditional distribution locations. With respect to time and baseline BMI effect, the study shows a significant nonlinear effect on CD4 count across all fitted quantiles. Furthermore, across all fitted quantiles, the effect of the parametric covariates of baseline viral load, place of residence, and the number of sexual partners was found to be major significant factors on the progression of patients’ CD4 count who had been initiated on the Highly Active Antiretroviral Therapy study.
format	article
author	Ashenafi A. Yirga Sileshi F. Melesse Henry G. Mwambi Dawit G. Ayele
author_facet	Ashenafi A. Yirga Sileshi F. Melesse Henry G. Mwambi Dawit G. Ayele
author_sort	Ashenafi A. Yirga
title	Additive quantile mixed effects modelling with application to longitudinal CD4 count data
title_short	Additive quantile mixed effects modelling with application to longitudinal CD4 count data
title_full	Additive quantile mixed effects modelling with application to longitudinal CD4 count data
title_fullStr	Additive quantile mixed effects modelling with application to longitudinal CD4 count data
title_full_unstemmed	Additive quantile mixed effects modelling with application to longitudinal CD4 count data
title_sort	additive quantile mixed effects modelling with application to longitudinal cd4 count data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/56219bb02d8a4539848d9b3009bc4dd4
work_keys_str_mv	AT ashenafiayirga additivequantilemixedeffectsmodellingwithapplicationtolongitudinalcd4countdata AT sileshifmelesse additivequantilemixedeffectsmodellingwithapplicationtolongitudinalcd4countdata AT henrygmwambi additivequantilemixedeffectsmodellingwithapplicationtolongitudinalcd4countdata AT dawitgayele additivequantilemixedeffectsmodellingwithapplicationtolongitudinalcd4countdata
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Additive quantile mixed effects modelling with application to longitudinal CD4 count data

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