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

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Asar O., İLK DAĞ Ö.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.115, no.3, pp.135-146, 2014 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 115 Issue: 3
  • Publication Date: 2014
  • Doi Number: 10.1016/j.cmpb.2014.04.005
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.135-146
  • Keywords: Clustered data, Multiple outcomes, Parsimonious model building, Statistical software, Quasi-likelihood inference, BINARY DATA, OUTCOMES
  • Middle East Technical University Affiliated: Yes

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. (C) 2014 Elsevier Ireland Ltd. All rights reserved.