Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

Asar, Özgür; Ilk, Ozlem

doi:10.1016/j.cmpb.2014.04.005

Cited by 8 publications

(9 citation statements)

References 21 publications

(33 reference statements)

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“…This set-up would correspond to our model in maximum dimensionality (R), where each response pertains to a single dimension. Asar and İlk (2014) propose a method in which selected predictor variables have the same effect on selected response variables. This is similar to our constrained model, where predictors have a similar effect on response variables pertaining to a given dimension.…”

Section: Related and Competing Approachesmentioning

confidence: 99%

The MELODIC family for simultaneous binary logistic regression in a reduced space

Rooij¹,

Groenen²

2021

Preprint

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Logistic regression is a commonly used method for binary classification. Researchers often have more than a single binary response variable and simultaneous analysis is beneficial because it provides insight into the dependencies among response variables as well as between the predictor variables and the responses. Moreover, in such a simultaneous analysis the equations can lend each other strength, which might increase predictive accuracy. In this paper, we propose the MELODIC family for simultaneous binary logistic regression modeling. In this family, the regression models are defined in a Euclidean space of reduced dimension, based on a distance rule. The model may be interpreted in terms of logistic regression coefficients or in terms of a biplot. We discuss a fast iterative majorization (or MM) algorithm for parameter estimation. Two applications are shown in detail: one relating personality characteristics to drug consumption profiles and one relating personality characteristics to depressive and anxiety disorders. We present a thorough comparison of our MELODIC family with alternative approaches for multivariate binary data.

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Section: Related and Competing Approachesmentioning

confidence: 99%

The MELODIC family for simultaneous binary logistic regression in a reduced space

Rooij¹,

Groenen²

2021

Preprint

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show abstract

“…For example, in clinical trials, we often find experimental research conducted at several randomly chosen hospitals or among randomly selected groups of subjects. Multilevel models, for example, can address clustered data, repeated measures or longitudinal data [15,16].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of the descriptive binary model and its application for identification of clumps of toxic cyanobacteria

Nosov

Zholtkevych

Georgiyants

et al. 2017

EEJET

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“…We might gain in efficiencies considerably, e.g. when the relationships between covariates and multiple responses are not significantly different (Asar and Ilk, 2014). Another default setting is the assumption of accommodating only the relationship of responses with current covariates, i.e.…”

Section: General Frameworkmentioning

confidence: 99%

“…Publicly available software for analysis of multivariate longitudinal binary data is still rare. Available options include the SAS macro of Shelton et al (2004), and the R (R Core Development Team, 2015) packages mmm (Asar and Ilk, 2013) and mmm2 (Asar and Ilk, 2014). In this study, we propose the R package pnmtrem for first-order marginalised transition random effects models with probit link.…”

Section: Introductionmentioning

confidence: 99%

First-order marginalised transition random effects models with probit link function

Asar

Ilk

2015

Journal of Applied Statistics

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Marginalised models, also known as marginally specified models, have recently become a popular tool for analysis of discrete longitudinal data. Despite being a novel statistical methodology, these models introduce complex constraint equations and model fitting algorithms. On the other hand, there is a lack of publicly available software to fit these models. In this paper, we propose a three-level marginalised model for analysis of multivariate longitudinal binary outcome. The implicit function theorem is introduced to approximately solve the marginal constraint equations explicitly. probit link enables direct solutions to the convolution equations. Parameters are estimated by maximum likelihood via a Fisher-Scoring algorithm. A simulation study is conducted to examine the finite-sample properties of the estimator. We illustrate the model with an application to the data set from the Iowa Youth and Families Project. The R package pnmtrem is prepared to fit the model.

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Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

Cited by 8 publications

References 21 publications

The MELODIC family for simultaneous binary logistic regression in a reduced space

The MELODIC family for simultaneous binary logistic regression in a reduced space

Development of the descriptive binary model and its application for identification of clumps of toxic cyanobacteria

First-order marginalised transition random effects models with probit link function

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