Simultaneous Variable Selection and Estimation in Generalized Semiparametric Mixed Effects Modeling of Longitudinal Data

2020 ◽  
pp. 447-474
Author(s):  
Mozghan Taavoni ◽  
Mohammad Arashi
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Mixed Effects ◽  
Mixed Effects Modeling ◽  
Variable Selection And Estimation

scholarly journals Latent Curve Analyses of Longitudinal Twin Data Using a Mixed-Effects Biometric Approach

2006 ◽  
Vol 9 (3) ◽  
pp. 343-359 ◽  
Author(s):  
John J. McArdle
Keyword(s):  
Longitudinal Data ◽  
Goodness Of Fit ◽  
Mixed Effects ◽  
Simultaneous Estimation ◽  
Twin Data ◽  
Mixed Effect ◽  
Mixed Effects Modeling ◽  
Curve Model ◽  
Latent Curve ◽  
Longitudinal Twin Data

AbstractIn a recent article McArdle and Prescott (2005) showed how simultaneous estimation of the bio-metric parameters can be easily programmed using current mixed-effects modeling programs (e.g., SAS PROC MIXED). This article extends these concepts to deal with mixed-effect modeling of longitudinal twin data. The biometric basis of a polynomial growth curve model was used by Vandenberg and Falkner (1965) and this general class of longitudinal models was represented in structural equation form as a latent curve model by McArdle (1986). The new mixed-effects modeling approach presented here makes it easy to analyze longitudinal growth-decline models with biometric components based on standard maximum likelihood estimation and standard indices of goodness-of-fit (i.e., χ2, df, εa). The validity of this approach is first checked by the creation of simulated longitudinal twin data followed by numerical analysis using different computer programs (i.e., Mplus, Mx, MIXED, NLMIXED). The practical utility of this approach is examined through the application of these techniques to real longitudinal data from the Swedish Adoption/Twin Study of Aging (Pedersen et al., 2002). This approach generally allows researchers to explore the genetic and nongenetic basis of the latent status and latent changes in longitudinal scores in the absence of measurement error. These results show the mixed-effects approach easily accounts for complex patterns of incomplete longitudinal or twin pair data. The results also show this approach easily allows a variety of complex latent basis curves, such as the use of age-at-testing instead of wave-of-testing. Natural extensions of this mixed-effects longitudinal approach include more intensive studies of the available data, the analysis of categorical longitudinal data, and mixtures of latent growth-survival/ frailty models.

scholarly journals From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

Biometrics ◽  
2019 ◽  
Vol 76 (3) ◽  
pp. 913-923
Author(s):  
Chase N. Joyner ◽  
Christopher S. McMahan ◽  
Joshua M. Tebbs ◽  
Christopher R. Bilder
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Group Testing ◽  
Mixed Effects ◽  
Bayesian Regression ◽  
Mixed Effects Modeling ◽  
Testing Data

scholarly journals Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection

2021 ◽  
Author(s):  
D Bottino ◽  
G Hather ◽  
L Yuan ◽  
M Stoddard ◽  
L White ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Statistical Model ◽  
Half Life ◽  
Model Fitting ◽  
Mixed Effects ◽  
Natural Immunity ◽  
Decay Kinetics ◽  
Mixed Effects Modeling ◽  
Kinetics Of ◽  
Over Time

Abstract The duration of natural immunity in response to SARS-CoV-2 is a matter of some debate in the literature at present. For example, in a recent publication characterizing SARS-CoV-2 immunity over time, the authors fit pooled longitudinal data, using fitted slopes to infer the duration of SARS-CoV-2 immunity. In fact, such approaches can lead to misleading conclusions as a result of statistical model-fitting artifacts. To exemplify this phenomenon, we reanalyzed one of the markers (pseudovirus neutralizing titer) in the publication, using mixed-effects modeling, a methodology better suited to longitudinal datasets like these. Our findings showed that the half-life was both longer and more variable than reported by the authors. The example selected by us here illustrates the utility of mixed-effects modeling in provide more accurate estimates of the duration and heterogeneity of half-lives of molecular and cellular biomarkers of SARS-CoV-2 immunity.

A novel quantification of information for longitudinal data analyzed by mixed‐effects modeling

2020 ◽  
Vol 19 (4) ◽  
pp. 388-398
Author(s):  
Min Yuan ◽  
Yi Li ◽  
Yaning Yang ◽  
Jinfeng Xu ◽  
Fangbiao Tao ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Mixed Effects ◽  
Mixed Effects Modeling

scholarly journals Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection

2021 ◽  
Author(s):  
D. Bottino ◽  
G. Hather ◽  
L. Yuan ◽  
M. Stoddard ◽  
L. White ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Statistical Model ◽  
Half Life ◽  
Model Fitting ◽  
Mixed Effects ◽  
Natural Immunity ◽  
Decay Kinetics ◽  
Mixed Effects Modeling ◽  
Kinetics Of ◽  
Over Time

AbstractThe duration of natural immunity in response to SARS-CoV-2 is a matter of some debate in the literature at present. For example, in a recent publication characterizing SARS-CoV-2 immunity over time, the authors fit pooled longitudinal data, using fitted slopes to infer the duration of SARS-CoV-2 immunity. In fact, such approaches can lead to misleading conclusions as a result of statistical model-fitting artifacts. To exemplify this phenomenon, we reanalyzed one of the markers (pseudovirus neutralizing titer) in the publication, using mixed-effects modeling, a methodology better suited to longitudinal datasets like these. Our findings showed that the half-life was both longer and more variable than reported by the authors. The example selected by us here illustrates the utility of mixed-effects modeling in provide more accurate estimates of the duration and heterogeneity of half-lives of molecular and cellular biomarkers of SARS-CoV-2 immunity.

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