Marginally specified models for analyzing multivariate longitudinal binary data

Abstract

Marginally specified models have recently become a popular tool for discrete longitudinal data analysis. Nonetheless, they introduce complex constraint equations and model fitting algorithms. Moreover, there is a lack of available software to fit these models. In this paper, we propose a three-level marginally specified model for analysis of multivariate longitudinal binary response data. The implicit function theorem is introduced to approximately solve the marginal constraint equations explicitly. Furthermore, the use of \textit{probit} link enables direct solutions to the convolution equations. We propose an R package \textbf{pnmtrem} to fit the model. A simulation study is conducted to examine the properties of the estimator. We illustrate the model on the Iowa Youth and Families Project data set.