Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to genetic study

Background The linear mixed-effects model (LME) is a conventional parametric method mainly used for analyzing longitudinal and clustered data in genetic studies. Previous studies have shown that this model can be sensitive to parametric assumptions and provides less predictive performance than non-p...

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Bibliographic Details
Published in:	BioData mining Vol. 18; no. 1; pp. 22 - 18
Main Authors:	Jahangiri, Mina, Kazemnejad, Anoshirvan, Goldfeld, Keith S., Daneshpour, Maryam S., Momen, Mehdi, Mostafaei, Shayan, Khalili, Davood, Akbarzadeh, Mahdi
Format:	Journal Article
Language:	English
Published:	London BioMed Central 19.03.2025 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Autocorrelation Autoregressive processes Bioinformatics Biomedical and Life Sciences Body mass index Body size Chromosomes Clinical trials Computational Biology/Bioinformatics Computer Appl. in Life Sciences Correlation analysis Data Mining and Knowledge Discovery Datasets Dimensional analysis Genetic study Genetics Genomes Genomics High-dimensional data Life Sciences Longitudinal data Medical research Medicine, Experimental Methodology Mixed-effects model Modelling Normal distribution Nucleotides Phenotypes Regression analysis Regression tree Risk groups Single nucleotide polymorphisms Single-nucleotide polymorphism Software Subgroups Variables High-dimensional data Mixed-effects model Regression tree Genetic study Longitudinal data
ISSN:	1756-0381, 1756-0381
Online Access:	Get full text
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