Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R.
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.
CHICAS, Lancaster Medical School, Faculty of Health and Medicine, Lancaster LA1 4YG, UK. Electronic address: o.asar@lancaster.ac.uk.
Ozgür
O
Asar
Department of Statistics, Faculty of Arts and Sciences, Middle East Technical University, Ankara 06800, Turkey.
Ozlem
O
Ilk
D000465:Algorithms; D001066:Appetite; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004032:Diet; D005260:Female; D006261:Headache / Q000453:epidemiology; D006801:Humans; D007630:Kenya; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D012984:Software
Clustered data; Multiple outcomes; Parsimonious model building; Quasi-likelihood inference; Statistical software