The ordinal response variable will inevitably contain unknown response categories because they cannot be directly derived from published data in the literature. In this talk, we propose a statistical methodology to overcome such a common but unresolved issue in the context of network meta-regression for aggregate ordinal outcomes. Specifically, we introduce unobserved latent counts and model these counts within a Bayesian framework. The proposed approach includes several existing models as special cases and also allows us to conduct a proper statistical analysis in the presence of trials with certain missing categories. We then develop an efficient Markov chain Monte Carlo sampling algorithm to carry out Bayesian computation. A variation of the deviance information criterion is used for the assessment of goodness-of-fit under different distributions of the latent counts. A case study demonstrating the usefulness of the proposed methodology is carried out using aggregate ordinal outcome data from 17 clinical trials in treating Crohn’s Disease.