A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses

Abstract

A difficulty in joint modeling of continuous and discrete response variables is the lack of a natural multivariate distribution. For joint modeling of clustered observations on binary and continuous responses, we study a correlated probit model that has an underlying normal latent variable for the binary responses. Catalano and Ryan have factored the model into a marginal and a conditional component and used generalized estimating equations methodology to estimate the effects. We propose a Monte Carlo expectation–conditional maximization algorithm for finding maximum likelihood estimates of the mixed model itself, extending and accelerating an algorithm for models with binary responses. We demonstrate the methodology with a developmental toxicity study measuring fetal weight and a binary malformation status for several litters of mice. A simulation study suggests that efficiency gains of joint fittings over separate fittings of the response variables occur mainly for small datasets with strong correlations between the responses within cluster.