Vitamin D status was not associated with ‘one-year’ progression of coronary artery disease, assessed by coronary angiography in statin-treated patients

Degerud, Eirik; Løland, Kjetil Halvorsen; Nygård, Ottar; Midttun, Øivind; Ueland, Per Magne; Seifert, Reinhard; Strand, Elin; Bleie, Øivind; Dierkes, Jutta

doi:10.1177/2047487314522137

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…There is a vast number of applications of mixed models to the analysis of clustered data in the social, life and physical sciences (Pinheiro and Bates, 2000;Demidenko, 2013). Linear quantile mixed models (Geraci andBottai, 2007, 2014) have too been used in a wide range of research areas, including marine biology (Muir et al, 2015;Duffy et al, 2015;Barneche et al, 2016), environmental science (Fornaroli et al, 2015), cardiovascular disease (Degerud et al, 2014;Blankenberg et al, 2016), physical activity (Ng et al, 2014;Beets et al, 2016), and ophthalmology (Patel et al, 2015;Patel, Geraci and Cortina-Borja, 2016). The present paper provides a novel and valuable contribution to the modeling of nonlinear quantile functions which broadens the applicability of quantile regression for clustered data.…”

Section: Discussionmentioning

confidence: 93%

Linear quantile mixed models

2013

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Dependent data arise in many studies. For example, children with the same parents or living in neighboring geographic areas tend to be more alike in many characteristics than individuals chosen at random from the population at large; observations taken repeatedly on the same individual are likely to be more similar than observations from different individuals. Frequently adopted sampling designs, such as cluster, multilevel, spatial, and repeated measures (or longitudinal or panel), may induce this dependence, which , the analysis of the data needs to take into due account. In a previous publication (Geraci and Bottai, Biostatistics 2007), we proposed a conditional quantile regression model for continuous responses where a random intercept was included along with fixed-coefficient predictors to account for betweensubjects dependence in the context of longitudinal data analysis. Conditional on the random intercept, the response was assumed to follow an asymmetric Laplace distribution. The approach hinged upon the link existing between the minimization of weighted least absolute deviations, typically used in quantile regression, and the maximization of a Laplace likelihood. As a follow up to that study, here we consider an extension of those models to more complex dependence structures in the data, which are modeled by including multiple random effects in the linear conditional quantile functions. Differently from the Gibbs * Draft version: June 1, 2011. Copyright to this paper remains with the authors or their assignees. Users may produce this paper for their own personal use, but distributing or reposting to other electronic bulletin boards or archives, may not be done without the written consent of the authors. To cite this paper: Geraci, M. and Bottai, M. (1 June 2011). Linear Quantile Mixed Models. Unpublished manuscript. 1 sampling expectation-maximization approach proposed previously, the estimation of the fixed regression coefficients and of the random effects covariance matrix is based on a combination of Gaussian quadrature approximations and optimization algorithms. The former include Gauss-Hermite and Gauss-Laguerre quadratures for, respectively, normal and doubleexponential (i.e., symmetric Laplace) random effects; the latter include a modified compass search algorithm and general purpose optimizers. As a result, some of the computational burden associated with large Gibbs sample sizes is avoided. We also discuss briefly an estimation approach based on generalized Clarkes derivatives. Finally, a simulation study is presented and some preliminary results are shown.

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Section: Discussionmentioning

confidence: 93%

Linear quantile mixed models

2013

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“…31 Model ( 11) is a median regression model with normal random effects, a special case of the linear quantile mixed models (LQMMs). 7,32 LQMMs have been used in a wide range of research areas, including marine biology, [33][34][35] environmental science, 36 cardiovascular disease, 37,38 physical activity, 39,40 and ophthalmology. 41,42…”

Section: Normal-laplace (Nl)mentioning

confidence: 99%

A family of linear mixed-effects models using the generalized Laplace distribution

Geraci

Farcomeni

2020

Stat Methods Med Res

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We propose a new family of linear mixed-effects models based on the generalized Laplace distribution. Special cases include the classical normal mixed-effects model, models with Laplace random effects and errors, and models where Laplace and normal variates interchange their roles as random effects and errors. By using a scale-mixture representation of the generalized Laplace, we develop a maximum likelihood estimation approach based on Gaussian quadrature. For model selection, we propose likelihood ratio testing and we account for the situation in which the null hypothesis is at the boundary of the parameter space. In a simulation study, we investigate the finite sample properties of our proposed estimator and compare its performance to other flexible linear mixed-effects specifications. In two real data examples, we demonstrate the flexibility of our proposed model to solve applied problems commonly encountered in clustered data analysis. The newly proposed methods discussed in this paper are implemented in the R package nlmm.

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“…Model ( 9) is a median regression model with normal random effects, a special case of the linear quantile mixed models (LQMMs) discussed by Geraci andBottai (2007, 2014). LQMMs have been used in a wide range of research areas, including marine biology (Muir et al, 2015;Duffy et al, 2015;Barneche et al, 2016), environmental science (Fornaroli et al, 2015), cardiovascular disease (Degerud et al, 2014;Blankenberg et al, 2016), physical activity (Ng et al, 2014;Beets et al, 2016), and ophthalmology (Patel et al, 2015;Patel, Geraci and Cortina-Borja, 2016).…”

Section: Convolutionsmentioning

confidence: 99%

Mixed-effects models using the normal and the Laplace distributions: A $\mathbf{2 \times 2}$ convolution scheme for applied research

Geraci

2017

Preprint

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In statistical applications, the normal and the Laplace distributions are often contrasted: the former as a standard tool of analysis, the latter as its robust counterpart. I discuss the convolutions of these two popular distributions and their applications in research. I consider four models within a simple 2 × 2 scheme which is of practical interest in the analysis of clustered (e.g., longitudinal) data. In my view, these models, some of which are less known than others by the majority of applied researchers, constitute a 'family' of sensible alternatives when modelling issues arise. In three examples, I revisit data published recently in the epidemiological and clinical literature as well as a classic biological dataset.

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Vitamin D status was not associated with ‘one-year’ progression of coronary artery disease, assessed by coronary angiography in statin-treated patients

Abstract: Plasma 25OHD3 levels were not associated with 'one-year' progression of CAD, assessed by CA in statin-treated patients.

Cited by 11 publications

References 38 publications

Linear quantile mixed models

Linear quantile mixed models

A family of linear mixed-effects models using the generalized Laplace distribution

Mixed-effects models using the normal and the Laplace distributions: A $\mathbf{2 \times 2}$ convolution scheme for applied research

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