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

TL;DR

This paper introduces a flexible modeling framework for multivariate longitudinal data that simplifies analysis across different response types and is implemented in an R package, enhancing efficiency and applicability.

Contribution

It proposes a novel multivariate marginal modeling approach that accommodates various response types and offers flexible model building strategies, with an R package implementation.

Findings

Model handles binomial, count, and continuous responses.

Simulation study confirms accurate parameter estimation.

Applied to maternal stress and child morbidity data.

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 modeling 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 Mother's Stress and Children's Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.