Network Meta-analysis for Ordinal Outcomes: Applications in Comparing Crohn's Disease Treatments

Crohn's Disease is a life-long condition associated with recurrent relapses characterized by abdominal pain, weight loss, anemia, and persistent diarrhea. In the U.S., there are approximately 780,000 Crohn's disease patients and 33,000 new cases are adding each year. We propose a new network meta-regression approach for modeling ordinal outcomes in order to assess the efficacy of treatments for Crohn's disease. Specifically, we develop regression models based on aggregate trial-level covariates for the underlying cut-off points of the ordinal outcomes as well as for the variances of the random effects to capture heterogeneity across trials. Our proposed models are particularly useful for indirect comparisons of multiple treatments that have not been compared head-to-head within the network meta-analysis framework. Moreover, we introduce Pearson residuals to detect outlying trials and construct an invariant test statistic to evaluate goodness-of-fit in the setting of ordinal outcome data. A detailed case study demonstrating the usefulness of the proposed methodology and model diagnostics are carried out using aggregate ordinal outcome data from 16 clinical trials for treating Crohn's disease.