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

• Published in: Computer methods and programs in biomedicine 2014-07, Vol.115 (3), p.135-146
• Main Authors: Asar, Ozgür, Ilk, Ozlem
• Format: Article
• Language: English
• Subjects: Algorithms; Appetite; Applied sciences; Biological and medical sciences; Child; Child, Preschool; Cluster Analysis; Clustered data; Computer science; control theory; systems; Computer Simulation; Computerized, statistical medical data processing and models in biomedicine; Data Interpretation, Statistical; Data processing. List processing. Character string processing; Diet; Exact sciences and technology; Female; Headache - epidemiology; Humans; Internal Medicine; Kenya; Likelihood Functions; Linear inference, regression; Longitudinal Studies; Male; Mathematics; Medical sciences; Memory organisation. Data processing; Models and simulation; Models, Statistical; Multiple outcomes; Multivariate Analysis; Other; Parsimonious model building; Probability and statistics; Quasi-likelihood inference; Sciences and techniques of general use; Software; Statistical software; Statistics
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2014.04.005

Abstract Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.