The Linear Mixed Model. A Critical Investigation in the Context of Longitudinal Data

1997 ◽  
pp. 89-99 ◽  
Author(s):  
Geert Verbeke ◽  
Emmanuel Lesaffre
Keyword(s):  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Critical Investigation

scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

Joint Modeling for Longitudinal Data with Missing Values: A Bayesian Perspective on Human Intelligence

2021 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
T. Gokul ◽  
M. R. Srinivasan
Keyword(s):  
Longitudinal Data ◽  
Missing Values ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Joint Modeling ◽  
Model Framework ◽  
Response Data ◽  
Count Response ◽  
Longitudinal Count Data

Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.

scholarly journals A latent factor linear mixed model for high-dimensional longitudinal data analysis

2013 ◽  
Vol 32 (24) ◽  
pp. 4229-4239 ◽  
Author(s):  
Xinming An ◽  
Qing Yang ◽  
Peter M. Bentler
Keyword(s):  
Data Analysis ◽  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Longitudinal Data Analysis ◽  
High Dimensional ◽  
Latent Factor
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