Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution
Article first published online: 25 MAY 2004
Volume 56, Issue 1, pages 73–80, March 2000
How to Cite
Coull, B. A. and Agresti, A. (2000), Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution. Biometrics, 56: 73–80. doi: 10.1111/j.0006-341X.2000.00073.x
- Issue published online: 25 MAY 2004
- Article first published online: 25 MAY 2004
- Received July 1998. Revised March 1999. Accepted May 1999.
- Continuation-ratio logit;
- Generalized estimating equations (GEE);
- Generalized linear mixed model;
- Marginal model;
- Mixture model;
- Ordinal data;
Summary. The multivariate binomial logit-normal distribution is a mixture distribution for which, (i) conditional on a set of success probabilities and sample size indices, a vector of counts is independent binomial variates, and (ii) the vector of logits of the parameters has a multivariate normal distribution. We use this distribution to model multivariate binomial-type responses using a vector of random effects. The vector of logits of parameters has a mean that is a linear function of explanatory variables and has an unspecified or partly specified covariance matrix. The model generalizes and provides greater flexibility than the univariate model that uses a normal random effect to account for positive correlations in clustered data. The multivariate model is useful when different elements of the response vector refer to different characteristics, each of which may naturally have its own random effect. It is also useful for repeated binary measurement of a single response when there is a nonexchangeable association structure, such as one often expects with longitudinal data or when negative association exists for at least one pair of responses. We apply the model to an influenza study with repeated responses in which some pairs are negatively associated and to a developmental toxicity study with continuation-ratio logits applied to an ordinal response with clustered observations.