BIVARIATE LATENT VARIABLE MODELS FOR CLUSTERED DISCRETE AND CONTINUOUS OUTCOMES

We use the concept of a latent variable to derive the joint distribution of a continuous and a discrete outcome, and then extend the model to allow for clustered data. The model Can be parameterized in a way that allows one to write the joint distribution as a product of a standard random effects model for the continuous variable and a correlated probit model for the discrete variable. This factorization suggests a convenient approach to parameter estimation using quasi-likelihood techniques. Our approach is motivated by the analysis of developmental toxicity experiments for which a number of discrete and continuous outcomes are measured on offspring clustered within litters. Fetal weight and malformation data illustrate the results.