Multivariate Multinomial Logit Model with ANOVA Decomposition for Correlated Categorical Outcomes

Abstract

In medical research, patients often have multiple interdependent outcomes, such as posttraumatic stress disorder (PTSD), depression, and pain among trauma survivors. Most existing research uses multinomial regression to analyze these interdependent outcomes separately, which ignores correlations between concurrent conditions. This omission may lead to loss of information and reduced predictive accuracy. Accounting for correlations between multiple categorical outcomes requires a high-dimensional parameter space, making model estimation challenging. In this paper, we propose a multivariate multinomial logit model that captures outcome correlations and uses the ANOVA decomposition of the parameter space to reduce the number of parameters. The ANOVA decomposition enables explicit conditional model formulations, which allow for a computationally much simpler composite likelihood for model estimation. We develop an efficient Minorization-Maximization (MM) algorithm to maximize the composite likelihood, which also incorporates variable selection via a bridge penalty. Simulation studies are conducted to evaluate our method, demonstrating its accuracy in parameter estimation and variable selection. We further illustrate our method using data from the AURORA study.